Abstract: This study presents a novel resource scheduling framework for cloud computing environments that incorporates the Age of Information (AOI) metric into the decision-making process, enabling precise quantification and optimization of information freshness. The proposed framework leverages an enhanced deep reinforcement learning algorithm to adaptively learn optimal scheduling policies in dynamic cloud settings. We introduce a multidimensional reward function that not only considers traditional metrics such as resource utilization and task completion time but also integrates AOI as a core indicator, thereby achieving holistic optimization of information freshness at the system level. The method incorporates prioritized experience replay and n-step learning mechanisms, which enhance learning efficiency and policy stability. Extensive simulation experiments demonstrate that the framework maintains low average AOI under varying workloads while adhering to resource capacity and energy consumption constraints. This approach provides novel theoretical foundations and practical guidelines for improving real-time cloud service quality and facilitating timely decision-making in edge computing scenarios.

Abstract: This article constructs a collaborative innovation decision-making model for the new energy vehicle industry chain under decentralized and carbon quota sharing contracts, and obtains the optimal parameter values and profit values of the new energy vehicle industry entities under two different scenarios. Taking BYD's new energy vehicle industry as a case study, the beneficial effect of carbon sharing contracts on the collaborative decision-making of the new energy vehicle industry system is empirically analyzed. Research has found that although carbon sharing contracts may weaken the willingness of new energy vehicle battery suppliers to innovate in carbon reduction, they will effectively improve their innovation in the range of new energy vehicles. The market price of new energy vehicle manufacturers under carbon sharing contracts decreases with the increase of the carbon sharing coefficient. Carbon sharing contracts can significantly increase the profits of the main players in the new energy vehicle industry system, and are directly proportional to the carbon sharing coefficient of the contract.

Abstract: This study focuses on optimizing supply chain decisions under two scenarios: government subsidies during disaster years and farmers with varying risk preferences. An order-agriculture supply chain model is constructed, involving three parties: farmers, distributors, and insurance companies. Farmers cultivate agricultural products with varying levels of greenness. A three-stage game model is employed to derive the optimal planting scale for farmers, the optimal wholesale price for distributors, and the optimal premium rate for insurance companies. The results indicate that government disaster year subsidies directly increase the Conditional Value-at-Risk (CVaR) of farmers, although a maximum subsidy rate exists to prevent inequity. Enhancing the greenness of agricultural products has a positive impact on agricultural production. As the probability of disaster years increases, loan guarantee insurance becomes more effective in expanding farmers' planting scales, while yield guarantee insurance demonstrates superior performance in improving farmers' CVaR. The practical value of this study lies in providing farmers with optimal decision-making frameworks and profit calculations for loan guarantee insurance and yield guarantee insurance under varying disaster-year probability scenarios. Additionally, it explores the impact of government subsidies during disaster years, the greenness level of agricultural products, and the risk of crop failure on changes in farmers' value. These findings contribute to the optimization of farmers' decision-making processes, enhancement of their economic welfare, and the promotion of sustainable agricultural development, ultimately improving the livelihoods of farmers.

Abstract: Building on the use of digital technology in supply chain management, this paper integrates data governance service providers into the supply chain. Given the distinct nature of data governance services, the paper illustrated the learning effect curve and simulated their output function. Building on this, four different supply chain data governance models were proposed, namely, manufacturer single governance model, retailer single governance model, manufacturer and retailer independent governance model, and manufacturer and retailer collaborative governance model. Constructed the profit model for the supply chain within the relevant framework. By vertically comparing the optimal decisions and system performance across various models, the study concluded that the collaborative governance model maximizes supply chain profit and is more responsive to factors that enhance overall profitability.

Abstract: In this paper, a distributed no-wait permutation flowshop scheduling problem with a preventive maintenance operation (PM/DNWPFSP) is investigated. A mixed-integer linear programming model for the PM/DNWPFSP is established. The problem characteristics and preventive maintenance characteristics of the PM/DNWPFSP are analyzed, and an accelerated calculation method of the completion time is proposed. A hybrid artificial bee colony (HABC) algorithm with an iterated local search mechanism for neighborhood search is proposed. To improve the quality of the solution, the shift, the swap and the hybrid operators are conducted in the critical factory. A local search operator based on the shift, the swap and the hybrid operators is proposed to jump out of local optima. A large number of experiments are conducted to evaluate the performance of the proposed HABC. The experimental results show that the proposed HABC algorithm has many promising advantages in solving the PM/DNWPFSP.

Abstract: Blockchain technology is introduced into the dual-channel supply chain system of online direct marketing and offline traditional retailing to solve the problem of opaque product sources and information asymmetry while also considering consumers' privacy concerns to increase their willingness to buy and improve enterprises' profitability. Based on the introduction of blockchain technology, the paper considers consumers' privacy concerns, uses the manufacturer-dominated Stackelberg game model to solve the equilibrium, and compares and analyzes the optimal pricing decisions and profits of supply chain members in different models before and after the introduction of blockchain technology. It is shown that when blockchain is not adopted, the rise in consumer sensitivity to false appraisal results leads to lower prices, and demand and pricing increase with the probability of the product being genuine; when blockchain is adopted, the increase in privacy concern costs will lead to lower demand and prices. Under a given condition, introducing blockchain technology can enhance the profits of all parties in the supply chain.

Abstract: We approach an open dimension problem, in specific, a two-dimensional strip packing problem variation found in sheet metal laser cutting, where rectangular items must be cut from a metal sheet, aiming to increase the packing layout added value. Therefore, this research objective is to analyze the packing layout added value with raw material reuse and practical constraints found in real-life laser cutting operations. The Best Fit Decreasing Height heuristic was modified to reuse raw material and calculate the packing layout added value, being compared with three construction heuristics using a set of literature and generated instances. We show the modified best fit decreasing height heuristic obtained better results when compared to the selected heuristics, with a high sheet metal utilization by the original instance rectangles and efficient raw material reuse. Thus, for sheet metal laser cutting practical operations, the modified best fit decreasing height heuristic is suitable for generating good packing layouts, resulting in industrial benefits including cost savings, increased productivity, greater competitiveness, and sustainability. Approaching raw material reuse increased the packing layout added value in most solutions found, and should be considered in real-life laser cutting operations. However, prioritizing only raw material reuse is not ideal, since a high number of additional rectangles can cause manufacturing wastes including overproduction, stock, and extra processing.

Abstract: Studies in the literature on flexible job-shop scheduling problems (FJSP) generally assume that one worker is assigned to each machine and that processing times are constant. However, in some industries, multiple workers with cooperation can process complex operations faster than one worker. If the possibility of completing jobs in a shorter time with worker cooperation is not taken into account, the opportunity to create more effective schedules may not be taken advantage of. Therefore, it is essential to consider the flexibility of collaboration between employees. However, to increase labor efficiency in businesses, jobs are also expected to be done with the minimum number of workers possible. This study considers the FJSP with both machine and number of workers dependent processing times. The objectives are minimizing the total tardiness and the total number of workers. A bi-objective mathematical model and an NSGA-II algorithm for large-sized problems have been proposed. The performance of the proposed solution approaches is demonstrated by using randomly generated test problems. For each problem, the most successful Pareto solution among the obtained solutions by the mathematical model and the NSGA-II algorithm was determined using the TOPSIS method. Furthermore, the effect of the total number of workers on the total tardiness is examined. The performance of proposed solution approaches, and when the worker number increases, the total tardiness of jobs can be reduced by an average of 75.88%, have been shown through comprehensive experimental studies.

Abstract: This paper investigates a closed loop supply chain (CLSC) encompassing a manufacturer, a retailer, and consumers operating within the carbon trading scheme. Employing the focus theory of choice, we analyze the decision-making processes of the retailer, considering various personality traits. A Stackelberg game is formulated, wherein the manufacturer assumes responsibility for recycling activities. The research explores the impact of the retailer’s optimism and confidence levels on optimal decision-making within a positive evaluation system. Numerical examples are employed to elucidate equilibrium solutions, illustrating the correlation between the retailer’s personality traits and the manufacturer’s optimal decisions. Furthermore, a sensitivity analysis is conducted on the carbon trading price and the manufacturer’s carbon emission quota allocation within a single cycle under the carbon trading scheme. The investigation concludes with an examination of the influence of recycling prices on the manufacturer’s optimal revenue. The findings indicate that retailers with distinct personality traits adopt varied pricing strategies. Decreases in optimism and self-confidence levels prompt the retailer to opt for relatively lower retail profit pricing. Simultaneously, the manufacturer demonstrates a preference for collaborating with a retailer characterized by optimism and lower confidence levels, thereby enhancing overall manufacturing revenue. Notably, under the carbon trading scheme, fluctuations in carbon trading and recycling prices distinctly influence the manufacturer’s decisions.

Abstract: Aiming at the inefficiency caused by an unbalanced workstation load in the mixed-model assembly line (MMAL), we study the assembly line (AL) design and load balancing problem under parallel tasks. Considering the task configuration cost, workstation opening cost and penalty cost of unbalanced load on the assembly line, a mixed integer programming model with the workstation’s space capacity constraint is established to formulate the mixed-model assembly line load balancing problem (MMALLBP), which is aiming at minimizing the total cost. In addition, the simulated annealing algorithm with an improvement strategy is proposed. Numerical experiments using the improved simulated annealing algorithm are superior to the solver in terms of solving time and stability, and the solving accuracy is higher than that of the traditional simulated annealing algorithm. Allowing parallel tasks can flexibly allocate tasks to the workstations, effectively use the idle time of the workstations, reduce the number of opened workstations, improve the production efficiency, reduce construction costs and the risk caused by the unbalanced load of AL.

Abstract: The advancement of Internet of Things technology enables the collection and transmission of real-time environmental and vehicle information, aiding the scheduling of postearthquake emergency supplies. Earthquakes often cause victims psychological pain due to insufficient supplies, and secondary disasters during transportation complicate supply scheduling. This study used a questionnaire to determine the psychological pain perception cost function of victims and identify the parameter value ranges under various environmental conditions. A fuzzy inference system was applied to ascertain the function parameters based on actual earthquake losses. Subsequently, a mixed-integer programming model for the multiperiod scheduling of emergency supplies was developed. An improved particle swarm optimization (IPSO) algorithm with a nondominated solution adjustment strategy was devised to solve the model and compared with the traditional particle swarm optimization (PSO) algorithm. The efficacy of the IPSO algorithm was validated through multiple examples. Additionally, a sensitivity analysis of factors such as supply satisfaction proportion was conducted. Results indicated that when remaining supplies fail to meet the minimum needs of undistributed disaster points, setting a minimum satisfaction percentage effectively reduces the total psychological pain cost. This study offers significant theoretical value in alleviating victims' psychological pain and enhancing rescue efficiency.

Abstract: As Industry 4.0 continues to transform the manufacturing domain, the focus is shifting towards mass personalization of products, enabling companies to efficiently produce customized goods that meet individual customers’ unique needs and preferences. This requires manufacturing enterprises to be flexible and adaptable with their scheduling processes and manufacturing setup. Flexibility and subsequent realization of personalization of products can be realized by utilizing the notion of a Line-less Assembly System (LAS), which replaces a fixed conveyor system with a system in which the products move between machines, with products being fitted on Autonomous Mobile Robots (AMRs) to transport the products from one machine to another as per their production routing. This necessitates scheduling products as per their production routing on available AMRs to reap the benefits of LAS, which is viewed as a Job Shop Scheduling Problem (JSSP) to maximize resource utilization while adhering to constraints. The novelty of this approach is that, in addition to scheduling products, it also considers the scheduling of AMRs. A mathematical formulation to solve the deterministic JSSP is presented in the current work. The formulation is solved for various inputs using a mathematical solver. In general, JSSPs are NP-hard problems. Subsequently, a meta-heuristic-based Genetic Algorithm (GA) has been constructed to solve the JSSP. The solutions obtained through both GA and mathematical solver are compared, and it was found that GA performs well in computation and optimization efficiencies.

Abstract: Recyclers can derive benefits from horizontal demand information sharing with competitors under specific conditions. However, these advantages may be compromised by the actions of remanufacturers. Information leakage occurs when a remanufacturer selectively discloses information obtained from one recycler to another. This study aims to support recyclers within the reverse supply chain in effectively engaging in horizontal information sharing while mitigating the risk of remanufacturers disclosing proprietary information to competitors, thereby preventing the dissemination of information contrary to the recyclers' intentions for sharing. The research focuses on analyzing the impact of horizontal information sharing and information leakage on the profitability of both remanufacturers and recyclers. An analytical model has been developed based on partial and asymmetric signals of customer valuation. Three scenarios are explored: no information sharing and no leakage, information sharing only, and scenarios involving both sharing and leakage. The novelty of this study lies in its examination of a demand process characterized by distributional uncertainty, which mirrors the informational challenges faced by recyclers entering new markets or expanding into new recycling categories. Recyclers operate with incomplete information and cannot determine whether they possess superior information compared to their competitors. The findings suggest that information sharing among recyclers can enhance the profits of those experiencing high demand but may adversely affect those with lower demand levels. In the absence of horizontal information sharing between recyclers, remanufacturers tend to leak information about higher-demand recyclers to others. Ultimately, managers of competing firms who face uncertainty regarding their information standing should consider sharing information to gain improved demand forecasts or, at minimum, to prevent remanufacturers from exploiting information leakage for personal gain. This refined analysis provides critical insights for stakeholders in the reverse supply chain, highlighting the complex interplay between information sharing and competitive advantage, as well as the strategic importance of managing information flow to safeguard business interests.

Abstract: This study explores technology licensing in a low-carbon supply chain under cap-and-trade regulations, with an upstream firm holding partial shareholding in a downstream firm. We established a Stackelberg game to analyze four licensing strategies: free, fixed fee, royalty, and revenue-sharing. We investigate the effects of vertical shareholding and cap-and-trade regulation, as well as whether technology licensing yields a more favorable outcome compared to non-licensing and which licensing strategy proves superior. The findings reveal that when the upstream firm holds a higher share in the downstream firm, it results in increased profits for the upstream firm, the supply chain system, and consumer surplus, but decreased profit for the downstream firm. Furthermore, when carbon emission quotas are sufficiently high (low), a higher carbon trading price leads to increased (decreased) supply chain profitability, while inevitably decreasing consumer surplus. Increased carbon emission quotas consistently contribute to increased supply chain profitability, but have no impact on consumer surplus. All licensing contracts enhance the profitability of the upstream firm, the supply chain system, as well as consumer surplus, with revenue-sharing emerging as the most effective strategy. However, whether technology licensing promotes social welfare depends on factors such as the carbon emissions per unit of product and the environmental impact of each unit of carbon emission.

Abstract: This paper presents a comprehensive review of the last two decades of research on Kidney Exchange Programs (KEPs), systematically categorizing and classifying key contributions to provide readers with a structured understanding of advancements in the field. The review highlights the evolution of KEP methodologies and lays the foundation for our contribution. We propose three mathematical models aimed at improving both the quantity and quality of kidney transplants. Model 1 maximizes the number of transplants by focusing on compatibility based on blood type and PRA, without additional constraints. Model 2 introduces a minimum Human Leukocyte Antigen (HLA) compatibility threshold to enhance transplant quality, though this leads to fewer matches. Model 3 extends the problem to a Multi-Agent Kidney Exchange Program (MKEP), pooling incompatible donor-recipient pairs across multiple agents, resulting in a higher number of successful transplants while ensuring fairness across agents. Sensitivity analyses demonstrate trade-offs between transplant quantity and quality, with Model 3 striking the optimal balance by leveraging multi-agent collaboration to improve both the number and quality of transplants. These findings underscore the potential benefits of more integrated kidney exchange systems.