Proceedings of Industrial Engineering and Management

The call center industry urgently needs efficient staff scheduling due to unpredictable customer service demands and challenges in human resource coordination. Appropriate scheduling is vital to enhance operational efficiency, reduce labor costs and improve customer satisfaction. This research is aim to solve the daily multi-task staff scheduling problem by integrating the innovative capabilities of QUBO (Quadratic Unconstrained Binary Optimization) model and Quantum Computing. We formulate the intricate parameters related to the number of employees, task type, and skill proficiency using a traditional mixed-integer programming model. Specifically, the study formulates scheduling constraints and objectives and then we convert this model into a QUBO model this transformation makes the problem to be suitable for Quantum Annealing (QA) and enabling subsequent integration with QA technology. This technology enables rapid and advanced exploration of extensive solution spaces and identify the optimal or near-optimal solutions. This research offers valuable insights and substantial groundwork for future explorations and developments in optimizing call center staff scheduling problem.

In this paper, Ant Colony Optimization (ACO) is used to determine whether or not to perform simultaneous processing and the combination of jobs that make up a batch in a scheduling problem to achieve the objective of minimizing the make-span. After constructing the scheduling model and decision-making model for simultaneous processing, computer experiments in conjunction with a reactive scheduling system based on genetic algorithms (GA) that considered simultaneous processing and preparatory operations of jobs are conducted to verify the effectiveness of the proposed method.

Despite the high costs that limit the widespread adoption of automation, manual assembly lines remain prevalent in small and medium-sized manufacturing. This study uses the multi-period non-reset model to address ergonomic risks and adopts a period-based perspective. By incorporating engineering task durations and designated inspection points, the approach allows for switchable processing rates to optimize production output. Through numerical experiments in medium to high-risk environments, our model has demonstrated its ability to ensure worker safety and improve production efficiency.

In recent years, problems of quality fraud, such as falsification of inspection data and inspections by unqualified inspectors, have come to light, shaking the confidence of companies. This is true even for global leading manufacturing companies. In addition, the increase in the number of foreign workers and differences in length of service have resulted in differences in workers’ abilities in Japan. Therefore, the objective is to find optimal worker assignment rules that take into account quality and worker levels in order to achieve efficient production. To solve this problem, we developed a mathematical model that introduces quality cost into the concept of the multi-period constraint cycle model. Numerical experiments were also conducted by varying the parameters of worker level and number of processes using programming and assuming various process situations. In this paper, we propose a mathematical model for optimal assignment of production line considering due time and quality in the case of two worker levels. Also, we provide the proof to generalize the rules of one special worker and two special worker cases. And numerical experiments confirm that the rule is satisfied.

In vehicle routing, estimating route lengths using continuous approximation models can be valuable for delivery planning, especially for tour cost estimation and territory design because it avoids the computational cost associated with solving TSP and VRP directly. In this study, we propose a route length estimation formula based on rectilinear distances by considering the shape of the area. We calibrated the parameters through numerical experiments. Thus, our proposed formula can estimate the average tour length in rectilinear distance with high accuracy; and as the number of points increases, the influence of the shape decreases.

The aging population has recently led to a labor shortage. Developing resources and transmitting skills to workers in the manufacturing industry is crucial to address these challenges. However, these skills must be conveyed through verbal instructions or on-the-job training from experienced workers to inexperienced ones. Thus, a quantitative analysis is required to understand their learning progress effectively. This study applies motion capture technology to visualize nut-tightening operations for gaining insights into the efficiency of workers and their proficiency levels, and assesses the skill development overtime by analyzing the time series of coordinate data.

In order to effectively deal with the problems such as high transportation cost and difficult timeliness in the current material transportation system of coal mining enterprises, a vehicle scheduling model is proposed. Different from the traditional method of finding the optimal path between a single starting point and a single destination and vehicle combination, the vehicle scheduling model between different starting points and different destination sets is constructed in this paper, and the optimization goal is to minimize the total distribution scheduling cost. At the same time, factors with the particularity of the coal mine system, such as market settlement transportation cost, vehicle fault maintenance, and centralized outage time for inspection, are taken into account in the model. A heterogeneous vehicle logistics scheduling model for the coal mine is established, and a whale swarm optimization algorithm is used to solve the model. It was found that the total transport cost of vehicles was reduced by 25.9%, the delivery time was reduced by 65.4%, and the number of vehicles delivered was reduced by 11.1%. The results show the practicability of the model and the effectiveness of the solution method, which can provide reference for the optimization and improvement of other coal mine material transportation systems.

In recent years, corporate quality irregularities have been reported in various media outlets, shaking the confidence of companies and affecting sales and stock prices. This is true even in Japan's leading manufacturing industries. In addition, the increase in the number of foreign workers and differences in length of service have resulted in differences in workers’ abilities. Therefore, the objective is to find the optimal worker allocation rules that take into account quality and worker level in order to achieve efficient production. In this paper, we propose an optimal allocation model for smart production line in limited-cycle problem with multiple periods considering quality and due time. We also discuss the optimal arrangement strategy and characteristics in the case of different worker levels by numerical experiments.

In this study, a method of integrated assembly job shop scheduling considering fuzzy operation time (IAJSS-FOT) is proposed to minimize the fuzzy total production completion time and the fuzzy total inventory time. In this method, part processing and assembly sequences are optimized simultaneously and a triangular fuzzy number is used to describe fuzzy operation time in processing process and assembly process. A hybrid chromosome encoding structure with processing and assembly operation is designed and NSGA-II is used to solve the problem. With a case study, the validity of the proposed method is verified.

Wafer test, also known as wafer probe or wafer sort, is a critical process that ensures the quality of dies after the wafer has been fabricated. Wafer testing process involves multiple components, including tester, prober, load board (LB), and probe card (PC), and solving the wafer test scheduling problem (WTSP) can be challenging due to its complexity. Traditional algorithms often struggle with large-scale problems in this domain. To address this issue, this paper aims to propose a hybrid multi-objective generic algorithm combined with a dispatching rule to solve WTSP. In this paper we compared our algorithm with dispatching rules and non-dominated sorting genetic algorithm (NSGAII) combined with variable neighborhood descent algorithm (VND) in terms of minimizing tardy jobs and reducing the changeover of PC and LB. The results demonstrate that our algorithm is capable of effectively solving large-scale scheduling problems.

The emergence of automated guided vehicles (AGV) has dramatically facilitated shop floor transportation, making production scheduling challenging. Meanwhile, the arrival of dynamic tasks increases the complexity of production scheduling. Therefore, this study focuses on the dynamic scheduling problem with AGVs under new order arrival. First, the mathematical model of dynamic flexible job-shop scheduling problems with AGVs (DFJSPA) is developed. Then, the DFJSPA is modeled as a Markov Decision Process (MDP), where processing and transportation tasks are considered. Next, we propose a dueling double deep Q network (D3QN) algorithm to optimize the problem. The evaluation results under nine scenarios demonstrate that the D3QN algorithm has less tardiness than the composite scheduling rules, which indicates that the D3QN algorithm can achieve high-efficiency decision-making in dynamic manufacturing systems.

Meeting customer time requirements poses a major challenge in the context of high-variety make-to-order companies. Companies need to reduce the lead time and process urgent jobs in time, while realizing high delivery reliability. The key decision stages within Workload Control (WLC) are order release and shop floor dispatching. To the best of our knowledge, recent research has mainly focused on order release stage and inadvertently ignored that different dispatching rules for different urgency state of the job should be adopted at shop floor dispatching stage. At the shop floor dispatching stage, adopting different rule for urgent jobs and non-urgent jobs can reduce the percentage tardy and tardiness time. In response, this study focuses on shop floor dispatching stage and considers urgent state of job dispatching rule based on WLC. That is, an integrated shop floor dispatching rule (time-oriented dispatching rule for urgent jobs and load-oriented dispatching rule for non-urgent jobs) is adopted. That is, an integrated shop floor dispatching rule (time-oriented dispatching rule for urgent jobs and load-oriented dispatching rule for non-urgent jobs) is adopted. Using simulation, the results show that adopting a time-oriented shop floor dispatching rule for urgent jobs according to current time and due date or planned start time of operations improves the delivery performance, especially percentage tardy. These have important implications on how dispatching rules for urgent jobs should be designed.

We consider a two-stage production assembly scheduling problem which includes processing stage and assembly stage. In first stage, the parts of these products are produced by these parallel machines, and each machine can process only one of these parts at a time. There is only one single assembly machine or a group of assembly workers in the second stage. In this problem, the objective is to minimize total completion time. The two-stage assembly scheduling problem is an NP-hard problem, so that it is very difficult and time consuming to obtain the optimal solution. This paper proposes a heuristic algorithm to solve the total completion time of two-stage assembly scheduling problem. Then, this algorithm is tested on the issue in different sizes and compare with the discrete particle swarm optimization (DPSO) algorithm. The results show that the proposed heuristic algorithm is a very efficient algorithm for assembly scheduling problem.

The research on intelligent manufacturing is divided into two parts, namely equipment intelligence and decision-making system intelligence. In the process of promoting intelligent manufacturing, it is not only necessary to automate the transformation of equipment and systems, but also to make their systems possess the characteristics of intelligent decision-making. One of scheduling problem inspired by a real case from the automobile factory with undergoing digital transformation is considered and a mathematical model is established to solve this scheduling problem. A simple heuristic is proposed to solve the problem. The solution is better than that by current method.

The primary challenge facing mixed-flow assembly lines is how to effectively balance the production process to accommodate diverse product combinations and constantly changing demands. This study actively addresses the problem of rebalancing mixed-flow assembly lines by considering variations in assembly task complexities, differences in worker skills, and the serial-parallel operation modes of workstations. A mathematical model is constructed with the objective of minimizing production cycle time, workstation smoothness index, and rebalancing costs. To solve this problem, a hybrid Gray Wolf-Genetic Algorithm is designed. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified through a case study involving the reconfiguration of a mixed-flow assembly line for mobile phone case production at Company H. This research aims to help manufacturing companies better adapt to changing market demands, potentially enhancing production efficiency and competitiveness.

With the development of global manufacturing, the distributed flexible job shop scheduling problem (DFJSP) has attracted much attention. However, DFJSPs that simultaneously consider constraints such as workers are rarely mentioned. Therefore, a distributed flexible job shop scheduling problem with dual resource constraints (DFJSP-DRC) is proposed in this paper, aiming to increase productivity while reducing energy consumption by scheduling machines and workers. A multi-objective optimization model with the goal of minimum energy consumption and makespan is developed. In addition, an improved multi-objective gray wolf optimization algorithm (IMOGWO) is developed in this paper to solve the proposed problem. In IMOGWO, three scheduling rules and an active decoding strategy are designed to generate high-quality solutions in the initialization phase. One wolf predation strategy is designed to enhance the local search capability of the algorithm and expand the scope of solution space. Finally, the effectiveness of the IMOGWO in solving the DFJSP-DRC is verified through comprehensive experiments.

Lead time reduction is a major challenge facing Engineer-to-Order manufacturing. Rapid skill improvement of the workforce will positively impact the processing time of work. For Engineer-to-Order production systems, the product design activity significantly affects product lead time. In this research, a scheduling system for design engineers in such a production environment is investigated as a combinatorial optimization problem. A tabu-search based algorithm is proposed, and neighbourhood structures are explored.

In make-to-order plants that produce a wide variety of products in small lots, the production lead time must be reduced to meet the due date specified by the customer. In this study, we propose an integrated scheduling method in which GA and local search methods are used to allocate the work of jobs to machines to obtain the best chromosomes. PSO is used to assign works of jobs to the workers. The production scheduling problem is challenging to find optimal solutions because of the large number of valuables. PSO is one of the effective methods in the meta-heuristics method for the reduction of computation time. Therefore, this study attempts to develop PSO and GA. PSO is used to seek the worker’s assignment, and GA is used to seek the job assignment. The performance of the proposed method is investigated using some scheduling models.

The Flexible Job Shop Scheduling Problem (FJSP) is crucial in adapting to varying market demands and production contexts, yet existing approaches tend to prioritize machine efficiency, often sidelining worker needs. This research advocates for a holistic approach to FJSP that concurrently considers machinery and worker allocation, focusing not just on operational efficiency, but also on the physiological and psychological wellness of workers. A mathematical model addressing these facets within a multi-objective FJSP framework is proposed and validated through numerical experiments, providing a balanced and human-centric perspective to scheduling solutions.

This study expresses the uncertainty of manufacturing processing time as a random variable and proposes a new production schedule evaluation method based on the probability distribution. We obtained results by using this proposed method. The results were compared with simulation results in order to clarify the effectiveness of the proposed method.

The scheduling problem for vinegar production and filling operations of a target factory is investigated. The processes include mixing, sterilization, and filling with several tanks. In the mixing process, it is essential to consider the mixing time and tank capacity constraint, and in the sterilization and filling processes, the filling speed is the critical factor in generating feasible schedules. When multiple batches are necessary for a semi-finished product, it is necessary to mix them promptly, and feed them to the sterilization plate without a break. Given the quantity of the products to be filled in a single day, the problem of determining the schedule of when to start and finish each process is formulated as a mixed-integer programming model. The solutions obtained by using actual production schedule data were recognized as acceptable, as the model includes the key features of the processes.

The order batching problem and the picker routing problem are methods for improving the efficiency of order picking operations in logistics warehouses. The former is a problem to optimize the combination of multiple orders for efficient picking, and the latter is a problem to optimize the route for picking batches, which are a sets of combined orders. Both problems have been formulated as mathematical optimization problems to minimize the total travel distance, and have been the subject of numerous previous studies. However, when the scale of the problem is large, it is often impossible to obtain an exact solution in a practical time, and even a feasible approximate solution cannot be obtained. In this study, we propose a new optimization algorithm based on the tabu-search method, a meta-heuristic, for the batching and routing problems in logistics warehouses, where the order batching and picker routing problems are considered simultaneously. The effectiveness of the proposed method is verified through a large number of numerical experiments using large-scale problem data prepared with reference to real data.

We are concerned with a worker assignment problem in multi-stage production lines, in which workers may have different skill levels. The model called the restricted-cycle model with multiple periods is under consideration. The objective is to minimize the expected cost of assignments, which is defined based on the expected idle cost and the expected delay cost at all stages. We propose a new branch and bound algorithm for finding an optimal worker assignment, and the proposed algorithm can also be applied when parallel production line environments are under consideration. Based on numerical experiments, we show that our proposed algorithm is more effective than previously proposed algorithms.

One of the purposes of a circular economy is to reuse repeatedly products to extend their useful life and ultimately to be returned to nature. Therefore, following the concept of the circular economy, we need to previously design the process of reuse and remanufacturing of products to repeatedly reuse the products. In this research, we develop a service and product design process considering product lifecycle management in the circular economy. First, we inspect the characteristics of product design to realize the circular economy with reference to papers discussing the circular economy. Then, we propose the table based on a journey map to create processes to reuse a product. In addition, we propose service design method using IoT/DX systems for enhancing reuse of products, and explain the effectiveness of the developed method.

Taking a fresh juice store in a certain school as the research object, data collection was conducted through on-site research. Excel was used to test the stability, homogeneity, and independence of the data. MATLAB was used for data fitting analysis. Flexsim simulation software was used to establish a model and run it. It was found that the shop had problems such as poor layout, unreasonable employee work routes, and waste of homework actions. By applying industrial engineering theory and methods, adjusting store layout, optimizing employee work routes, and improving actions, the number of employees has been reduced from 4 to 3. The labor intensity of employees has been reduced, and the average waiting time of customers has been reduced by 40.77 s. At the same time, through visual management, more convenient and fast services have been provided to customers, improving customer satisfaction.

Cylindrical turning, as one of the most common machining methods in mechanical processing, accurately evaluating the power during the material removal process in cylindrical turning is a prerequisite and foundation for achieving energy optimization in mechanical machining processes. Currently, there are three main modeling methods for the material removal power in cylindrical turning: First, calculating the material removal power through the specific energy of cutting per unit volume (Method I); Second, calculating the material removal power through cutting forces (Method II); Third, calculating the material removal power through power measurement combined with statistical analysis (Method III). However, there is currently no general agreement on the effectiveness and applicability of these three modeling methods. Therefore, the purpose of this study is to compare and evaluate these three modeling methods through cylindrical turning experiments. The experimental results indicate that Method II has the smallest prediction error, with an average error of 4.91%. Method III follows with prediction errors all below 8.57%. Method I has the largest prediction error, with the maximum error reaching 31.09%.

The existing methods of operating express/local mode require additional railway lines. However, renovation projects of existing lines are often limited by engineering technology and funding. Meanwhile, tidal passenger flow in rush hour is obvious. To sum up, this paper proposes a method of operating express/local mode under existing railway lines and unidirectional tidal passenger flow. This method sets that all trains in the same direction of unidirectional tidal passenger flow are express trains, and all trains in the opposite direction of unidirectional tidal passenger flow are local trains. After simulation, this method has been proven to effectively reduce total passenger travel time in specific scenarios, providing references for subsequent research and engineering applications.

This study conducted ergonomic experiments based on three MATB-II tasks with different difficulty levels, collected the subjects’ EEG indicators, task performance, and subjective mental workload indicators during task execution, and proposed a mental workload evaluation method based on the Error Back Propagation (BP) algorithm. The results showed that subjective mental workload increased with the increase in task difficulty and duration. The resource management task scored the lowest among the three tasks, which may reveal the significance of the difficulty of the resource management task. Through EEG data analysis, there were significant differences in the four types of EEG power with different difficulty and duration. The BP algorithm based on difficulty and duration can distinguish the mental workload under different task difficulties in the time dimension, and the accuracy reached 66.61% under fivefold cross-validation. The research results can guide complex human–computer interaction systems’ design, task planning, and mental workload assessment.

Based on the second generation of Multi-Attribute Task Battery-II (MATB-II) experimental paradigm of concurrent multi-task, this paper collects the multi-modal physiological signals and subjective mental workload data of NASA-TLX scale during the task completion process of experimental objects, proposes a mental workload classification model based on multi-modal physiological signal feature analysis and pattern recognition, and compares the classification and recognition effects of different modal physiological signals and their combinations in three typical machine learning algorithms (Random forests, decision trees, and k-nearest neighbor models). The results show that, among the classification models based on single-modal physiological signals, the classification models based on skin electrical, electrocardiographic, and EEG signals increase in accuracy in turn; the classification models based on multi-modal physiological signals are generally better than the single-modal classification models; the random forest classification model based on the three modalities of EEG, ECG, and skin electrical physiological signals has the highest classification accuracy. In occupational settings, the interaction between perceived mental workload and physical health effects should be considered, as workers often face both physical and mental demands at the same time. Controlling the mental workload of operators within reasonable limits can reduce human error.

The purpose of this paper is to study the prediction of assembly time quota of electronic toy products of A company, and use XGBoost and improved RFECV method to predict and optimize. After analyzing the factors affecting the assembly hours quota of electronic toy products, the assembly hours of different toy products can be accurately predicted by constructing the XGBoost model. In addition, the improved RFECV method is used to select the most influential features to help determine the main factors affecting the assembly hours. Finally, the grid search method is used to find the value of the optimal parameter in XGBoost. Through the analysis of the data of electronic toy manufacturing enterprises, the man-hour error rate is controlled within 10%. The result shows that this method can improve the quality and efficiency of the man-hour quota work of electronic toy products.

The significant income gap among suppliers on vehicle-cargo matching platforms poses a looming threat of supplier loss. Furthermore, the persistently low success rate in matching cargo with suitable suppliers reinforces this problem, leading to a decline in both supplier and customer numbers. These challenges have a profound impact on the operational efficiency of these platforms. To balance the suppliers’ income and enhance the matching success rate, this paper proposes a novel model that takes into account the income of suppliers while incorporating the concept of soft matching constraints. When a customer's request cannot be immediately fulfilled, a suboptimal matching solution is proposed to the customer. The customer is then given the choice to either accept this recommendation or opt to wait for the subsequent round of matching. To balance the suppliers’ income, suppliers with lower income have high priority in matching, thereby creating a fairer and more equitable matching environment.

Service composition and optimal selection problem (SCOSP) in cloud manufacturing are crucial tasks. However, due to insufficient historical data or accurate forecasting methods, making unbiased decisions for this problem often faces challenges in addressing uncertainties. In this paper, we address the problem of service composition and optimal selection within the framework of adaptive distributionally robust optimization. In particular, we design an event-dependent ambiguity set associated with manufacturing capability in different events, which combines the 1-Wasserstein metric with the box support set to effectively capture the distributional ambiguous information for each event. To solve SCOSP exactly, we reformulate adaptive distributionally robust SCOSP into the mixed integer programming model. In the end, we conduct a series of numerical experiments to assess the value of incorporating event-dependent distributional information and to evaluate the robustness of the model.

To solve the problem of low accuracy of material requirement forecasting in discrete manufacturing enterprises, a novel method is being presented by using BP neural network and genetic algorithm. With the proposed method, the collected data of customer orders are cleaned to remove the invalid data. And the processed data are trained using BP neural network to generate the objective function of genetic algorithm. Then, the operators of selection, crossover and mutation are optimized to eliminate inferior populations, increase superior populations and accelerate the iteration rate of superior population genes with adaptive genetic algorithm. In addition, the improved operators and objective functions can be involved in the MATLAB simulation model to achieve a better material requirement forecasting result for the discrete manufacturing system. Finally, empirical research shows that the accuracy of material requirement forecasting has been improved by using the improved genetic algorithm.

This paper aims to derive optimal combination of electric powers for electric power market network considering environmental evaluation. Currently, attention is being paid to increasing the ratio of renewable energy generation in the electric power market which called Green Energy Coefficient (GEC). Many studies have analyzed different purposes for power market, such as configuration problem of electric power price, the development problem of the power generating system and so on. In this research, we propose the optimization stochastic programming models for electricity supply chain under renewable integration. We explicitly consider intermittency of renewable energy by developing a scenario decision tree, and further formulate and solve a multistage stochastic supply balance model to meet the aggregate demand in each period. This research is responsible for adjusting supply and demand for virtual power plants, which have been attracting attention in recent years. The case study application is used to illustrate the model and how it supports the electricity marketing strategy.

Bucket brigade order picking systems (OPSs) exhibit high operational productivity due to their self-adjusting characteristic without requiring management intervention. However, the unproductive travel behaviors of pickers can impact productivity, particularly when the OPS handles a large number of small-sized orders that lead pickers to walk long distances to pick a few items. This study proposes an OPS to mitigate unnecessary walking behaviors of pickers in bucket brigade OPSs. The proposed OPS incorporates a conveyor system to assist pickers in transporting totes that have completed order picking to the unloading station and to introduce new empty totes. By doing so, the proposed OPS reduces the average total walking time cost by 36.64% and increases productivity by 9.65% in the designed simulation experiments.

The rapid adoption of electronic commerce (EC) services has led to the establishment of numerous online sales platforms. Forecasting the order quantity is crucial for effective inventory management at EC-affiliated stores and meeting demand in EC services. In this study, we explored time series modeling, focusing on ARIMA-derived models, namely SARIMA and SARIMAX, considering the limited features in the dataset for forecasting. The dataset was obtained from an EC platform specializing in floral products with peak demand during Japanese Mother’s Day. In the SARIMAX models, we proposed exogenous variables such as binary indicators for Mother’s Day and holidays and a variable denoting the week of May. The SARIMAX model with the “Mother’s Day” variable yielded the best performance. However, forecasting accuracy was inadequate due to date variability. To improve forecasting accuracy, we propose a data-formatting approach that expresses dates based on Mother’s Day. This approach aims to eliminate the influence of date variability. By adopting the proposed approach, we achieved more accurate forecasts compared to our previous results. In conclusion, our proposed exogenous variables and data-formatting approach allowed for order quantity forecasts with optimal accuracy. Despite the promising results, our study has limitations, such as the reliance on a specific dataset and need for further validation in diverse EC contexts. Future research could explore the integration of additional exogenous variables and investigate the scalability of our forecasting approach.

This study addresses the augmentation of processing line capacity, transitioning from a U-sharped configuration to an O-sharped layout. To facilitate this transformation, a method grounded in Tabu search principles is devised for the judicious allocation of transport tasks, distributing responsibilities among both the incumbent and newly integrated transfer robots.

Recently, more and more people have been using reinforcement learning (RL) to solve industrial issues. RL requires long-term trial and error. So, when dealing with large-scaled or complex systems where it’s hard to apply RL directly, discrete system simulation is used as an instead of the real system. This report explained RL using simulation. First, we used a simple logistics model and described the linking simulation and RL. Then, we looked at a more complex industrial case, where we explained the application of RL in the pit crane operation at a waste incineration facility. This study explained the linkage process of simulation and RL in more detail and identified the issues.

Various factors, such as shortening product life cycles, commoditization, and changing and diversifying needs due to ICT, are forcing companies to create even more innovations. The theory of ambidexterity is important in this context. And today, innovation creation is required in both exploration and exploitation activities. This paper discusses organizational structures and innovation models to promote innovation creation through ambidexterity.

Queueing theory is a mathematically sophisticated discipline, and there is currently an expectation to apply it to optimization problems using computational results. To achieve the overall optimization of facilities, it is essential to consider selects in the number of service nodes, the availability of nodes, and the relationships with neighboring nodes. In this research, we construct a model to perform simultaneous optimization of the number of service nodes and the number of service points at each node, given the number of people classes and the number of people within the system, in a closed network constructed using closed BCMP. The theoretical calculation of closed BCMP utilizes the mean value analysis method. We employ a genetic algorithm for optimization, where the objective function considers both the standard deviation of the mean number of people within the system and the cost of installing servers at nodes, aiming to distribute congestion effectively within the network. Constraints are implemented to ensure that the mean number of people at each node does not exceed its maximum allowable capacity. Additionally, if a specific node is not used, the number of servers at that node becomes zero. The total maximum allowable capacity of all nodes is subject to specified conditions. This approach allows for the effective dispersion of congestion within the network, prevents excessive increases in the number of people at each node, and minimizes the cost of servers installation.

This study aims to determine the effects of risk aversion and experiential learning on decision making regarding inventory management in supply chain management. The experiment replicated the replenishment decision model in a beer game. A total of 33 subjects participated in the experiment, including students and inventory control experts in the Department of Industrial Engineering at the Tokyo University of Science. In this study, it was found that the decision-making results tended to differ depending on the degree of risk aversion after a number of experiments. Future work is needed to analyze the impact of the factors identified in this study on supply chain performance.

The logistics crisis spread worldwide due to the imbalance between the supply shortage and the rise of fluctuating demands. Physical Internet (PI), the analogous of digital internet described as an open logistics network sharing assets and flow consolidation, is introduced to solve the inefficiency of transportation by providing high interconnectivity, standardization protocol, and decentralized management. This research comparatively investigates the performance of a supply chain under different network frameworks, namely, traditional supply network (TR) and PI system by using multi-agent simulation approach. Regarding to PI in particular, assuming the initial phase of PI implementation, decentralized control system has been adopted in which each PI participant pursues their own profitability like TR even though PIS participants share the resources such as warehouses and trucks in a supply chain. Simulation results showed that PI maintain significantly lower number of opportunity losses than TR, even in the system under decentralized control that are assumed to be in the initial phase of PI implementation.

This study examines a contract farmer system between a public institution and farmers from the perspective of mathematical optimization, with local production for local consumption of agricultural products in mind. Specifically, it is considered to stabilize the profits of local farmers by maximizing their agricultural output, while at the same time maximizing the profits (or minimizing the costs) of the public agency. The proposed problem is formalized as a multi-objective optimization problem, and the optimal solution cannot be obtained directly. Therefore, in this study, the proposed mathematical programming problem is solved in two steps to obtain the optimal solution, taking into account the importance of the objective function.

A linear consecutive-k-out-of-n: G system consists of n components which are arranged in a line and the system works if exists k or more working components continuously arranged. In this paper, we first summarize the several proposed methods of system reliability evaluation of the consecutive-k-out-of-n:G systems. However, these methods based on the assumption that all components are independent and identical. We then consider the system reliability of the linear consecutive-k-out-of-n: G system when components lifetime not need to be identical, and a method of domination is introduced.

Information technology has triggered a new round of industrial revolution, and intelligent warehouse management, as the foundation and guarantee for normal business operations, is a prerequisite and driving force for the continuous development of enterprises. This article firstly proposes a warehouse management model with deep integration of IE + IT technology; Next, develop a classification partition storage strategy, construct a cargo allocation model, and apply an improved algorithm based on the NSGA-II to solve the model, thereby achieving an increase in cargo turnover rate; Finally, using Company B as an example to conduct intelligent warehousing simulation based on any logic, the feasibility and efficiency of the warehousing management model proposed in this article are verified.

Warehouse management is an important part of the production process. As a result of the development of technology, more and more companies tend to use automated logistics technologies such as AVS/RS to handle the internal logistics of the production system. Especially in multi-floor workshops, the production process on each floor are connected with each other through AVS/RS. It is becoming more and more important to study the integration process of AVS/RS and multi-floor workshop production process. In this paper, an integrated queuing network model of multi-floor workshop and AVS/RS was established, and a calculation method of system performance index was proposed. The accuracy and efficiency of the proposed method are demonstrated by comparing the results with simulations from numerical experiments. The work of this article can provide a basis for the design of autonomous vehicle storage and retrieve systems in multi-floor workshop, and the calculation method can be used as the support of WMS technology base.

In the extensive adoption of industrial robots in practice, a multitude of challenges are supposed to be addressed, including the complexities associated with remote monitoring, operational process management, the technical expertise for maintenance, and the time-consuming feature of debugging and deployment processes. However, the conventional service pattern employed in management of industrial robots is hindered by several shortcomings, such as limited computational efficiency, high communication delays, inadequate data privacy, and high demand for network and configuration. Consequently, this study proposes an industrial robot platform on the basis of cloud-edge-end collaboration architecture, which utilizes computing resource virtualization, container orchestration technology, and CI/CD tools to facilitate the deployment of cloud-edge-end collaboration services. To validate the feasibility of the architecture proposed, an industrial robot monitoring scenario is taken as an example. The results demonstrate that the architecture partially mitigates the shortcomings of traditional services, thereby offering valuable insights and guidance for cloud-edge-end based management of industrial robots.

This research employs a multi-dimensional approach to the assessment of component quality in aerospace industry, utilizing data from Destructive Physical Analysis (DPA), Physics of Failure Analysis (PFA), retest screening, and Failure Analysis (FA). By formulating an innovative model for component quality situation assessment, we first construct a thorough and exhaustive evaluation metric for the quality of components integrated within the system. Then we develop a prediction method for it based on Long Short-Term Memory (LSTM) networks techniques. This investigation contributes to both the theoretical underpinnings and practical applications of component quality situation assessment, furnishing the industry with a dependable analytical instrument that enhances the quality control procedures of components in aerospace industry.

Objective: The development of coal mine production construction to modernization has led to a dramatic increase in the amount of information on the display interface of the system, and the contradiction of “huge amount of information, limited display” makes the operator need to pay attention to more information display areas at the same time when facing the complex operation tasks, which reduces the reliability of the work of the human factors. This study investigates the influence of data update rate among the three factors of the human–computer interaction interface (information display rate, information provision rate, and data update rate) in the intelligent coal mine production system. Methods: In this paper, we take the data update rate of the display interface of a coal mine system as the research object, use the E-prime software to simulate the state of the display interface of the coal mine system in the real working environment, design multi-group controlled experiments, and analyze the performance of the subjects to complete the visual searching task under different data update rates (1 s, 2 s, and 3 s). Results: The experimental results showed that in a display interface with 24 changing regions, the operators’ task performance (average reaction time, number of correct mouse clicks) was best when the data update rate was 2 s. Conclusion: Changes in the data update rate affect the operator's task performance. This study provides a relevant basis for subsequent research on the design of the display interface of coal mine intelligent production systems.

This study aims to explore the situation awareness construction of individuals in different collaborative conditions during dual-person collaborative work in the intelligent coal mine comprehensive mining control system. The study aims to provide relevant basis for the management of personnel collaborative operation modes in intelligent coal mines. The E-prime software is used to simulate the task of monitoring the operating status of the shearer in the intelligent coal mine comprehensive mining control system. Simulated experiments are conducted under different collaborative conditions, including individual work, operational collaboration, work content communication collaboration, and non-work content communication collaboration. The SAGAT is used to measure the individual’s situation awareness scores. The total situation awareness score, scores at each stage of the three-stage situation awareness, and work performance are analyzed to verify the differences in situation awareness impact on individuals in different collaborative conditions. The collaborative modes of dual-person operational collaboration and work content communication collaboration significantly enhance the level of situation awareness and achieve high work performance. In the perception stage of situation awareness, the non-work content communication collaboration group shows a significant decrease in situation awareness scores compared to the other three groups. In the comprehension stage, the work content communication group shows significant differences in situation awareness scores compared to the other three groups, with the highest scores. In the prediction stage, the individual work group shows significant differences in SA scores compared to the operational collaboration group and work content communication collaboration group, with significantly lower scores in the prediction stage than the collaborative groups.

The article studies the service path planning problem of overhaul workers in coal mining industry. Firstly, combined with the characteristics of coal mining industry, the problem studied in the article can be attributed to the traveler problem, and the multi-objective constraint model with time window is constructed by describing and making corresponding assumptions from the traveler problem, with the goal of optimal path and shortest time. Secondly, in order to solve the problem, the ant colony algorithm, which is more flexible, is selected, and the parameters are adjusted appropriately to make it closer to the actual situation. Finally, the ACO algorithm is designed for example validation using the actual mine data as the experimental data. The results show that the proposed ACO algorithm can greatly shorten the delivery distance, reduce the number of maintenance workers, lower the company's cost and provide a better customer experience while meeting the time window requirements.

Wireless charging system without wires and mechanical connectors and related infrastructure is environment and user friendly that widely applied in electric vehicles. It is crucial to establish a systematic performance improvement framework for the wireless charging systems of electric vehicles. This study proposes a Six Sigma wireless charging system evaluation framework with define-measure-analyze-improve-control management process, embedded with failure mode and effects analysis, Markov chain and quality control techniques to empower manufacturing operational excellence. The proposed framework can achieve the viability of intelligent decision-making to enhance the empirical implementation through continuous process improvement.

This paper introduces a question-answering system based on a knowledge graph of the aviation manufacturing process. By collecting and extracting knowledge in the fields of machining, assembly, forming, materials, and other relevant aspects of aviation manufacturing technology, we first construct a comprehensive knowledge graph within the domain. We then leverage natural language processing technologies to address natural language queries. This encompasses named entity recognition utilizing the Aho-Corasick algorithm and Levenshtein Distance, rule-based intention recognition, query template matching and instantiation, among other techniques. Subsequently, answers are retrieved from the established knowledge graph. The test results demonstrate a precision rate of knowledge retrieval at 94.86%, enabling fast and accurate responses to the majority of questions within this domain.

In this paper, we consider the vehicle routing problem with stochastic travel and service times. We determine the delivery routes that meet customer’s time window when the arrival time of the vehicle at customers is uncertain. We propose the algorithm that iteratively generates efficient routes and adds them as candidates. The algorithm adds multiple routes in one iteration to the possible routes, to improve computation time. The computational experiments show that the computation time can be significantly improved for the problem with many customers.

The domain of construction machinery maintenance involves high-risk tasks, such as replacing substantial components and working at elevated heights. As a result, the occurrence of work-related accidents in this field exceeds that in com-parable sectors like automobile maintenance. In our effort to gain an understanding of these accidents, our study focused workers eye tracking. To begin with, we leveraged eye-tracking technology to the tracking data of approximately 500 individuals engaged in the maintenance of construction Machinery. In this eye-tracking test, subjects viewed five videos demonstrating various maintenance tasks, and we recorded their visual engagement after each video. Simultaneously, we employed a questionnaire to gain insights into predictive safety measures and strategies for preventing accidents for each of the five videos. Subsequently, we analyzed the accumulated eye-tracking data using both hierarchical and non-hierarchical clustering techniques to classify and interpret trends in gaze conditions. Additionally, we assessed the occupational accident history of the subjects and predicted their accident risks through questionnaires, considering occupational accident risks for each category of gaze status. Through these analyses, we elucidated the relationship between gaze conditions, work-related accidents, and associated risks.

In recent years, metro logistics has gradually become one of the choices of urban logistics terminal distribution, scientific and reasonable layout of metro logistics distribution center is an important guarantee for the development of metro logistics. To reasonably design the metro logistics distribution center layout plan, this paper firstly introduces the general steps of Systematic Layout Planning (SLP) method. And then by using this method completes the functional area division of the distribution center according to the operation characteristics of metro logistics and gives the distribution center operation flow based on the functional area division. Finally, taking Xiong’an New Area as an example, the correlation between the functional areas of the distribution center is analyzed to get the functional area operation relationship diagram and complete the layout of the distribution center.

In recent years, in response to the diversification of customer needs and the shortening of product life cycles, there has been a shift towards establishing multiple production lines for each product, similar to cell production. Such production lines generally consist of multi-elemental work process, which multiple elemental works are allocated to a process. However, in production lines constructed with such multi-elemental work processes, it is often observed that operations cannot be carried out at the targeted tact time, resulting in a failure to meet production goals. In the production line under investigation in this study, the discrepancy between planned tact time and actual execution tact time also has been a problem. Therefore, in this study, we conducted an analysis of the factors causing an extension of process work time in multi-elemental work processes through the analysis of a manual assembly line for white goods appliances and assembly work experiments. The study clarified that, when estimating multi-elemental work process work time, it is necessary to consider not only the execution time of component elemental work but also the time for interruptions in operation and cognitive processes.

In the field of production control, solving production scheduling and line balancing problems is extremely important for improving productivity, reducing production costs, and meeting delivery dates. If a delay occurs in one stage on the production line, it will affect the next stage, causing a delay in delivery and an increase in costs. This study takes a parallel production line as an example and considers the problem of how to optimally assign workers to each stage when the total expected cost is minimized by having a different number of stages in each production line. We propose a method to verify the above sufficient conditions based on the actual measurement data by measuring the actual operating time of each worker in real-time using the ‘InQross Kaizen Maker’ (hereinafter referred to as ‘InQross System’).

This paper explores the impact of consumers’ anticipated regret on the remanufacturing supply chain under carbon cap-and-trade policy. First, we constructed three remanufacturing models considering only carbon cap-and-trade policy, considering only consumers’ anticipated regret, considering both carbon cap-and-trade policy and consumers’ anticipated regret. Second, the equilibrium solutions of the three models are solved. Finally, the effects of carbon cap-and-trade policy and consumers’ anticipated regret on the equilibrium solutions are analyzed. Analytical results show that: (1) The carbon quota trading price is positively related to the price of new and remanufactured products, but negatively related to the demand for new products. Carbon quota trading price is positively correlated with the demand for remanufactured products when the ratio of carbon emissions per unit of new and remanufactured products is less than a certain threshold. (2) As the consumers’ anticipated regret sensitivity coefficient increases, the price of new and remanufactured products decreases, the demand for new products and the profits of the OEMs decrease, while the demand of remanufactured products and the profits of the IRs increase.

Fresh product distribution, as a key part of fresh e-commerce market competition, needs to have high timeliness and reliability. Reasonable optimization of distribution routes can enable companies to save distribution cost and improve customer satisfaction. This paper constructs a hybrid nonlinear integer programming model based on carbon emissions and road risk coefficients by considering delivery time, cost, and distance, and designs an improved genetic algorithm to solve the model. Finally, Beijing J e-commerce fresh distribution was taken as an example to verify the model, optimize the fresh distribution path, and lay a decision foundation for decision makers.

The integration of digitalization and servitization has pushed the manufacturers to develop the smart product-service system (smart PSS), which highlight user-centric and value co-creation. Current research has not yet fully considered the influence of stakeholders, and the dynamic elicitation and forecasting for smart PSS innovation requirements. This study adopts a user-manufacturer dual perspective to propose an integrated framework for dynamic requirement elicitation and forecasting for smart product-service system. The dynamic topic model (DTM) and bidirectional encoder representations from transformers (BERT) model are combined with the dynamic importance-performance analysis (DIPA) to classify the innovation requirements of smart PSS. Then, the improvement indexes of these requirements are forecasted by a grey forecasting model to predict the future design direction of smart PSS. The feasibility of the proposed framework is verified by an empirical study on smartwatch.

Aiming at the high energy consumption and carbon emissions of cold chain distribution, a multi-center cold chain distribution route optimization model considering carbon trading policy was constructed, and improved genetic algorithm was designed to solve it. The algorithm speeds up convergence by initial node allocation and introduces elite retention strategy. Finally, a simulation example is given to verify the effectiveness of the model and the algorithm. The research results can provide reference for multi-warehouse enterprises to construct fresh distribution network under carbon trading policy, reduce delivery cost and realize environmental friendliness.

Demand for renewable energy is increasing. Along with this, the burden of electricity charges as levies is also increasing. There are many complaints about this levy. However, future levy prices are also expected to rise. Among these, what can be said to be important is how to get people to choose a renewable energy plan, even if there is a burden. In the research so far, there is no research that presents additional information on global environmental risk information, measures the impact of the information effect, or creates a new power plan that suits consumers. Therefore, in this study, in order to evaluate the influence of global environmental risk information on consumer perception, we extract stated preference data by selective conjoint survey and estimate consumer preference by latent class model. We decided to verify the extent to which the presentation of global environmental risk information is effective in improving consumer receptivity and increasing awareness of renewable energy plan selection. In addition to that, the purpose of this research is to derive optimal strategies of electricity plans using latent class analysis considering renewable energy for the optimization of power generation in virtual power plant environment.

JEPX Japan Wholesale Electric Power Exchange currently has a spot market (one-day market) that takes place one day before power supply and a pre-hour market (same-day market) that is subsequently adjusted. In addition, the operation of multiple markets such as the capacity market and the adjustment power market procured by local transmission and distribution companies is expanding. It is necessary to analyze market trades in order to respond to the market. To analyze the price formation process in the electricity trading market under the constraint of the future renewable energy ratio in electricity, in this paper, we will conduct an electricity trading simulation in which each bidder sets the bidding price between the two parties, the seller (power producer) and the buyer (retailer). Two markets, a one-day-ahead market and a day-ahead market, will be established, and the transaction method will be the same as that used in the actual market.

In recent years, the development of a green supply chain model that takes sustainability into consideration has become an urgent issue in corporate management and policy operation. For example, in the fashion industry, the proper circulation of not only used items purchased by consumers, but also unsold items generated in retail stores is one of the most important issues. In this study, we examine a supply chain model for the appropriate circulation of unsold new products from retail outlets. Specifically, we considered a supply chain model in which a retailer’s inventory is divided into two states under stochastic demand fluctuations: new and old items, and the unsold old items are collected and reproduced by a manufacturer. By formulating this model as a Markov decision process, the optimal decisions regarding the retailer’s ordering policy and the retail price and the manufacturer’s wholesale price are obtained. Optimal investment and appropriate institutional design to reduce CO2 emissions generated in a supply chain are also considered. Specifically, we examine the decision of green investment in manufacturing technology to reduce CO2 emissions when a manufacturer produce items. And we examine the design of a carbon tax system to control CO2 emissions. Sensitivity analysis on the carbon tax system shows that raising the carbon tax rate increases the optimal retail price, the optimal wholesale price, and the optimal green investment.

To reduce Greenhouse Gas (GHG) emissions in the global supply chain, various carbon policies such as carbon tax and carbon cap-and-trade have been introduced in many countries. By applying a mix policy, there is a possibility that GHG emissions can be reduced more effectively than applying a single policy. Furthermore, it is also necessary to consider tariffs and FTAs for constructing a global supply chain. This study models a global supply chain with mix and single carbon policies, and analyzes the GHG emissions and costs in the supply chain under different carbon prices in carbon tax and carbon cap-and-trade. It was found cases that the mix carbon policy could reduce much GHG emissions with less actual carbon price than carbon prices in single policies.

This paper overviews the software development process and points out virtualization, docker container, microservices, measurement etc. are the key technology to build a circular economy society. As software is now a SoS (System of systems), we pay much amounts of cost in software development process. In other words, software is now as if a kind of architecture which is as large and complicated as pyramid in ancient time. In this paper, paying attention to these situations, software development process is divided into 6 parts, and considered from various aspects in some details, i.e., what problems there are and how to solve the problems. The point is that in software development process, much amounts of paper is consumed, development processes are constantly repeated according to user’s requests or social needs changing on time, and recently Open AI software is frequently applied to software development process which consumes huge amounts of energy. This paper describes results of tackling to solve such problems.

This study develops a method for creating services and products for a circular economy. The method consists of creating services to provide additional value for customers and the design method of products considering environmental friendliness. In this paper, we show the characteristics of the proposed method to create valuable service using a function deployment process and the procedure of creating an IoT/DX (Internet of Things/Digital Transformation) system to enhance the value of services by using a sample problem.

As the core technology of the Internet of Thing (IoT), RFID has received attention from all walks of life. Based on the dual utility of RFID technology to improve the recycling rate and inventory accuracy rate, a Closed-loop Supply Chain (CLSC) composed of a manufacturer and a retailer was taken as the research object. From the perspective of evolutionary game theory, a dynamic game analysis on the adoption of RFID technology in CLSC was conducted in this paper. Considering the bounded rationality, adoption cost, adoption income and cost allocation of system members, an evolutionary game model of CLSC adopting RFID technology was established. Firstly, from the perspective of static game, the mixed strategy Nash equilibrium solution was solved. Then from the perspective of evolutionary game, the evolutionary stability strategy (ESS) of the system was solved, and its evolutionary stability and the sensitivity of each factor were analyzed. Finally, through numerical example simulation, the evolution path of the game model and the sensitivity of each parameter were analyzed, and the previous proposition was verified, which proved the value of the research.

There is multiple uncertain information in the closed-loop supply chain of remanufacturing, which continuously transmits and accumulates, affecting the operational efficiency of the system. Therefore, studying the impact of multiple uncertain information on system operation is of great significance for helping enterprises take measures to reduce or eliminate the impact of uncertain information. From the perspective of information transmission, fuzzy entropy was used to study the quantification method of uncertain information, and a system dynamics based uncertain information transmission model for the remanufacturing closed-loop supply chain was constructed to study the impact of uncertain information such as market demand, recycling time, recycling quantity, and recycling quality on inventory in each link of the remanufacturing closed-loop supply chain, as well as the transmission effect of multiple uncertain information in the system.

The supply chain is a network structure prone to disruptions due to its complexity. Specifically, tunnel boring machines (TBMs) are extensive and intricate equipment that undergo design, production, and construction simultaneously, further exacerbating the risks in the TBM cutterhead supply chain (TBMCSC). When a problem arises in an enterprise within the TBMCSC, the risk propagates along the supply chain, impacting other enterprises in the network. Although predicting risks in advance is deemed impossible, identifying the most vulnerable enterprises, which are referred to as key node sets, enables improved risk management. In light of this, this paper proposes a deep reinforcement learning (DRL)-based method for identifying key node sets in a TBMCSC. The approach involves the following steps: first, the entire TBMCSC is modeled using complex network theory (Step 1). Next, risk propagation processes on the network are revealed using the coupled map lattice (CML) method (Step 2). Finally, the DRL algorithm is used to identify key node sets in the TBMCSC, with the aim of maximizing the impact of risk propagation (Step 3). By comparing the extent of risk propagation of the critical node sets identified by the DRL method with the traditional methods when facing the same risks, the superiority of this approach is demonstrated.

This paper first analyzes the characteristics of customer distribution in rural areas, designs the neighborhood density grid clustering model and combines it with K-means clustering analysis, automatically divides the region according to the density of rural customer points and calculates the number and location of points to be selected in each region; then it designs the gradual location model of pickup point considering customer satisfaction, takes the satisfaction of customers’ pickup distance and the connectivity of rural roads as the factors influencing the customer satisfaction, and solves the case by using CPLEX, which validates the model’s validity, and finally it designs different distribution strategies for the express delivery company according to the customer satisfaction and the attributes of pickup points.

It is of great significance to explore the coordinated development level of logistics and manufacturing industries in order to enhance the core competitiveness of the manufacturing industry and promote the cost reduction and efficiency improvement of the logistics industry. Based on the coupled coordination model, this paper constructs an evaluation index system for the coordinated development level of logistics industry and manufacturing industry in the Pan-Pearl River Delta region from 2011 to 2021, and analyzes its spatial and temporal development characteristics. The results show that the coordinated development level of the two industries in the Pan-Pearl River Delta region is low, the differences between regions are large, the provinces have improved to varying degrees, the highest level in Guangdong has reached primary coordination, and the lowest level in Hainan is in a serious state of imbalance. Based on the research conclusions, the policy is proposed to promote the further coordinated development of the two industries in the Pan-Pearl River Delta region.

Medical equipment is a cornerstone of modern medical system,hence the importance of the maintenance for medical equipment comes out conspicuously, which can directly affect the operation of a hospital, and even determine a patient's satisfaction to the hospital. In the modern medical system, the maintenance of medical equipment is often provided by medical equipment suppliers. The time window of critical medical equipment maintenance is often narrow, while the medical equipment suppliers begin to repair after a maintenance request from hospital users is received. Hence, it is a great challenge to the maintenance response ability for these medical equipment suppliers. In order to improve the response capability of medical equipment suppliers and change the passive equipment management strategy to the proactive predictive maintenance management strategy, a predictive maintenance model based on ARMA (Auto regressive and moving average) for a medical equipment supplier is proposed. The model is able to predict the impending failure according to the daily maintenance and help the medical equipment supplier to provide maintenance services in time.

The contemporary manufacturing landscape grapples with the escalating dynamic and individualized demands. To swiftly meet personalized production orders, manufacturers are tapping into open production logistics (PL) resources from socialized resource platforms. However, in such a volatile and socialized production environment, manufacturers are in dire need of reliable open resource management and decision-making tools for production logistics to sustain their market edge. This becomes even more pronounced for SMEs. This paper, focusing on the intricacies of dynamic production logistics, explores the challenge of “ production logistics trusted synchronization (PLTS)” in an open-resource context. We propose an innovative service infrastructure that synergizes blockchain and digital twin for PLTS. This system effectively combines the advantages of blockchain-driven cloud manufacturing resource management with digital twin control systems, presenting a holistic solution encompassing flexible trustworthy resource organization and open production logistics synchronization support. Additionally, we shed light on associated enabling technologies, offering a roadmap for ensuing technical explorations.

With the continuous increase of per-capita income, people's demand for deteriorating items such as seafood and fruits is increasing with higher requirement for freshness, and their perishable and short sales cycle make retailers operating perishable products face new challenges in the market competition. This paper focuses on the perspective of retailers, supposing that the limited capacity of the retailer's own warehouse and allow shortage and partial backlogging, considering whether the supplier offers a price discount. On the purpose to minimize the cost of the retailer, this paper builds two-warehouse improved EOQ model based on traditional EOQ model, and obtained the optimal inventory strategies of the retailer under different models. Then, verify the effectiveness of the model by numerical analysis and sensitivity analysis, and compare the influence of considering price discounts or not on the two-warehouse inventory model of two types of deteriorating items to determine the optimal inventory decision minimizing unit cost for retailers.

The extant related reports and events have shown that product quality issue becomes the priority among priorities in food and pharmaceutical industries since food and drug safety are closely related to people’s life and health. This paper provides a game-theoretic model to investigate the interplay between a policy maker, a manufacturer and a group of consumers, in which the policy maker determines the subsidy strategy to compensate the manufacturer in order to improve product quality. We take the product quality, social welfare, manufacturer’s utility, consumer surplus as hierarchical optimization objectives respectively. Our results reveal that a combo subsidy is the optimal choice to improve product quality. Importantly, we find that the optimal strategies for different players are distinct. We show that the policy maker should always provide a comb of subsidy regardless of external factors, while per-unit cost subsidy and quality improvement subsidy both can outperform the other in certain conditions. Our results show that the manufacturer always prefers the per-unit cost subsidy, while the comparison of the other two strategies depends on the product market share and production cost. We also find that consumers are indifferent to a combo of subsidy and per-unit cost subsidy, but the quality improvement subsidy always performs the worst.

This research paper investigates the advantages of implementing a closed-loop supply chain (CLSC) and underscores the potential enhancements in efficiency, productivity, and sustainability it offers. Additionally, it explores the integration of a hybrid CLSC to bolster a business's resilience in the face of disruptions and fluctuations in demand, particularly in mitigating the notorious bullwhip effects. Existing studies on hybrid CLSC have primarily focused on control strategies, mainly the development of a push–pull model, emphasizing the necessity for proactive bullwhip effect mitigation. However, these studies have overlooked a critical aspect—comprehending the implications of these strategies on the total cost while considering each mode in detail. The primary objective of our research is to propose the revised switching policy and calculate the total cost within a hybrid CLSC framework utilizing a Markov chain approach. We achieve this by constructing a revised hybrid push–pull model, subjecting it to analysis within a Markov chain framework, and assessing its cost-effectiveness, particularly in the context of bullwhip effect mitigation.

The integration of corporate social responsibility (CSR) in the modern business environment, which is increasingly focused on sustainability, is further complicated by the emergence of dual-channel closed-loop supply chains (DCCLSCs). Our study examines the unexplored complexities of integrating CSR practices into this complex system, considering the growing focus on supply chains and societal expectations. Using equilibrium analysis and numerical simulations, this study intends to investigate how different recycling channel structures affect optimal CSR investments, the resulting impact on pricing and recycling rates, and the interaction with consumer channel preferences. Our findings indicate that increased consumer sensitivity to CSR correlates with increased profitability and recycling rates, providing a strategic advantage to the manufacturer. This study provides a novel contribution by revealing the operational and ethical complexities of CSR integration in DCCLSCs. It encourages enterprises to incorporate CSR efforts with consumer education and to synchronize pricing and recycling strategies for mutual benefit, thereby providing manufacturers and retailers with actionable strategies for achieving sustainable and profitable operations.