Theoretical and Applied Mathematics in Supply Chain Management

Against the backdrop of the rapid growth in the scale of the prepared food market, safety issues have gradually become prominent. Establishing a traceability system has become crucial to safeguarding consumer rights and promoting the sustainable development of the industry, with traceability information sharing serving as the core link. However, affected by differences in interest demands and information asymmetry between manufacturers and retailers in the prepared food supply chain, there are obstacles to traceability information sharing. To explore the coordination mechanism of traceability information-sharing behavior in the prepared food supply chain under different decision-making models and its impact on profit distribution, this paper constructs a two-level supply chain model including manufacturers and retailers, comprehensively considers the online–offline dual-channel sales model, and distinguishes four scenarios: centralized decision-making, decentralized decision-making, retailer-led cost-sharing contract decision-making, and manufacturer-led cost-sharing contract decision-making. Using a differential game model, the equilibrium results under different decision-making models are discussed. The validity of the model is verified through fitting with empirical analysis and numerical example analysis. The research results show the following: (1) The centralized decision-making model has the best effect on increasing the market share of the prepared food supply chain, and although the cost-sharing contract model can improve it, there is still a gap. (2) The centralized decision-making model is not the one with the maximum profit, and manufacturer-led cost-sharing decision-making basically achieves Pareto optimality. The main reasons are the insufficient incentive mechanism, high coordination costs, and uneven profit distribution in centralized decision-making. (3) The impact of manufacturers’ offline channel traceability information-sharing behavior on profits is more significant than that of online channels. (4) In a market environment with information asymmetry, the impact of goodwill on the profits of prepared foods is more prominent. This research provides a theoretical basis for the management of the prepared food supply chain, helps optimize the traceability information-sharing mechanism and profit distribution plan, and promotes the healthy development of the industry. (5) When the coefficient measuring the intensity of traceability information sharing’s impact on product quality across manufacturers’ online and offline channels increases, only under the retailer-led model does product quality and goodwill exhibit a fluctuating trend of “rising from the bottom to the second place and then falling back to the bottom,” while the profits of all subjects increase simultaneously. (6) As the system attenuation coefficient increases, the evolution of product quality and goodwill under different cooperation models shows significant differences; in terms of profits, the profits of manufacturers’ online channels increase over time, while those of other subjects decrease. (7) When the discount rate rises, the manufacturer-led model presents distinct characteristics: both the ranking and absolute value of product quality decline synchronously, the ranking of goodwill falls, but its absolute value rises against the trend, the evolution of product quality and goodwill shows obvious model heterogeneity, and the profits of all subjects generally decrease. Full article

In China’s on-demand service platforms, daigou agents utilize locational differences through proxy purchasing. Daigou creates an informal supply chain that directly competes with official channels. This study incorporates daigou arbitrage into the channel competition framework via a multi-stage Stackelberg game-theoretic model. An analysis of the subgame perfect Nash equilibrium shows that daigou activity disrupts the manufacturer’s profits. We have thus developed a strategy based on mathematical optimization and compared its effectiveness and side effects with those of existing methods. We came to identify purchase restrictions as one of the most powerful strategies. Equilibrium analysis and numerical experiments confirm that proper purchase restriction choices reduce daigou arbitrage and minimize negative impacts on legitimate demand. This work provides the first game-theoretic model that integrates informal proxy-purchase supply chains into dual-channel competitions. Full article

Optimizing the siting and servicing of urban facilities is a core operations research problem that must reconcile heterogeneous demand, spatial constraints, and network-realistic travel. We present GD-ARISE, a GIS-integrated and data analytics pipeline that maintains a pedestrian–road network metric from demand inference through siting to routing. The workflow has three modules: (i) GIS integration that unifies spatial layers on one network and distance metric; (ii) data analytics that builds multi-criteria suitability via the Analytic Hierarchy Process (AHP) and maps scores to adaptive service radii; (iii) optimal location-and-routing that selects nonoverlapping sites with a transparent greedy rule (SCASS) and computes depot-to-depot routes via simulated annealing on the same metric. A case study in Seoul’s Gangnam District yields a high-coverage portfolio and feasible collection routes. We add a theoretical framework that casts SCASS as a conflict-graph problem, document the AHP elicitation with consistency checks, and report robustness analyses including sensitivity to AHP weights and to radius bounds. Results indicate that core hotspots remain stable to weighting, whereas mid-range corridors shift as criteria priorities or spatial parameters change. Full article

In today’s volatile supply chain environment, organizations require flexible and collaborative procurement strategies. Swap contracts, originally developed as financial instruments, have recently been adopted to address inventory imbalances—such as the 2021 COVID-19 vaccine swap between South Korea and Israel. Despite its increasing adoption in the real world, theoretical studies on swap-based procurement remain limited. This study proposes an integrated model that combines buyer-to-buyer swap agreements with long-term wholesale contracts under demand uncertainty. The model quantifies the expected swap quantity between parties and embeds it into the profit function to derive optimal order quantities. Numerical experiments are conducted to compare the performance of the proposed strategy with that of a baseline wholesale contract. Sensitivity analyses are performed on key parameters, including demand asymmetry and swap prices. The numerical analysis indicates that the swap-integrated procurement strategy consistently outperforms procurement based on long-term wholesale contracts. Moreover, the results reveal that under the swap-integrated strategy, the optimal order quantity must be adjusted—either increased or decreased—depending on the demand scale of the counterpart and the specified swap price, deviating from the optimal quantity under traditional long-term contracts. These findings highlight the potential of swap-integrated procurement strategies as practical coordination mechanisms across both private and public sectors, offering strategic value in contexts such as vaccine distribution, fresh produce, and other critical products. Full article

The purpose of this paper is to present a novel optimization framework that enhances Wasserstein Distributionally Robust Optimization (WDRO) for chance-constrained facility location problems under demand uncertainty. Traditional methods often rely on predefined probability distributions, limiting their flexibility in adapting to real-world demand fluctuations. To overcome this limitation, the proposed approach integrates two methodologies, specifically a Genetic Algorithm to search for the optimal decision about facility opening, inventory, and allocation, and a constrained Jordan–Kinderlehrer–Otto (cJKO) scheme for dealing with robustness in the objective function and chance-constraint with respect to possible unknown fluctuations in demand. Precisely, cJKO is used to construct Wasserstein ambiguity sets around empirical demand distributions (historical data) to achieve robustness. As a result, computational experiments demonstrate that the proposed hybrid approach achieves over 90% demand satisfaction with limited violations of probabilistic constraints across various demand scenarios. The method effectively balances operational cost efficiency with robustness, showing superior performance in handling demand uncertainty compared to traditional approaches. Full article

This paper introduces and analyzes the concept of $\lambda$-statistical convergence in fuzzy paranormed spaces, demonstrating its relevance to supply chain inventory management under demand shocks. We establish key relationships between generalized convergence methods and fuzzy convex analysis, showing how these results extend classical summability theory to uncertain demand environments. By exploring $\lambda$-statistical Cauchy sequences and $(V,\lambda )$-summability in fuzzy paranormed spaces, we provide new insights applicable to adaptive inventory optimization and decision-making in supply chains. Our findings bridge theoretical aspects of fuzzy convexity with practical convergence tools, advancing the robust modeling of demand uncertainty. Full article

Reducing carbon emissions is of immense interest to most modern organizations striving for sustainability. Effective inventory management is crucial for achieving resource optimization and minimizing environmental impact. Very little work has been conducted up to this point on slowly declining, low-quality products with multi-level trade credit rules under the influence of carbon emissions. In this study, an inventory model is tailored specifically for imperfect and gradually deteriorating products with a multi-level trade credit policy. Further, the impact of carbon emissions on the retailer’s ordering strategies is also considered. To determine the optimal policy for supply chain partners, three trade credit instances with seven subcases are taken into consideration. To choose the best scenario out of ten cases, an algorithm is also developed. The model’s validity is illustrated through a numerical experiment and sensitivity analysis. This study is an innovative approach to balancing economic trade credit policy in sustainable supply chain management. Full article

This study investigates the evolutionary game dynamics among electric vehicle distribution entities in the context of shared charging facilities, addressing the critical issue of inadequate charging resources. To understand the behavior of different stakeholders under government incentive policies, we develop an evolutionary game model involving a government department and two logistics enterprises (A and B). Through stability analysis, we explore equilibrium conditions of evolutionarily stable strategies (ESSs) for the tripartite evolutionary game. To ensure the robustness of our findings, we conduct a MATLAB simulation analysis to validate the analytical results. Our findings highlight that government subsidies, the costs incurred by logistics enterprises to share charging facilities, and the additional distribution income derived from this sharing are critical in determining whether the evolutionary game can achieve a stable equilibrium state. This research enables logistics companies to optimize the use of charging resources, lower operating costs, and enhance delivery efficiency. Additionally, government subsidy policies play a crucial role in encouraging logistics enterprises to engage in charging facility sharing, thereby fostering the sustainable development of the entire logistics industry. Based on these insights, the paper offers practical recommendations to further promote the sharing of charging facilities in electric vehicle distribution. Full article

We study a continuous-review stock management of a retailer for a single item in a limited storage (buffer) in a random environment. The stock level fluctuates according to two independent compound Poisson processes with discrete amounts of items (batches) that enter and leave the storage facility. The storage facility is controlled by a three-parameter base-stock replenishment policy. All items exceeding the storage capacity are transferred to an unlimited foreign facility. In addition, a restricted backlogging possibility is permitted; additional demands for items are lost sales. We further assume a random shelf life, the possibility of total inventory collapse, and a random lead time. Applying Markov theory, we derive the optimal control parameters minimizing the long-run expected total cost. A sensitivity analysis is conducted focusing on the comparison between the pure lost-sales policy and a partial backordering policy. Accordingly, we identify cases where one policy is cost effective compared to the other, particularly with respect to the batch patterns (sign, rate, average, and variability), and the associated costs. Full article

The technology options for sustainable development are explored with customer low-carbon preference in a port supply chain consisting of one ship and one port. Port supply chains can opt for either shower power or low-sulfur fuel oil to cut down emissions. We set game models considering three power structures: the port dominant (port-led Stackelberg game), the ship dominant (ship-led Stackelberg game), and the port and ship on the same footing (Nash game). We compare the performances of different technologies. It is shown that, when customer low-carbon preference and carbon tax are both low, LSFO is the appropriate choice from the supply chain’s profit perspective, SP is preferred from the emission control perspective, and LSFO is preferred from the social welfare perspective. However, when customers’ low-carbon preferences, carbon tax, and environmental concerns are all low or all high, LSFO should be adopted from the view of social welfare. The profits and carbon emissions of the supply chain in the Nash game are higher than those in the Stackelberg game. While the environmental concern is low, the social welfare of the supply chain in the Nash game is greater than that in the Stackelberg game. Otherwise, it is less than that in the Stackelberg game. The obtained results can help governments formulate policies and ships make emission reduction technology decisions according to their own interests. Full article

This paper presents an optimisation model for cost optimisation of maintenance at an offshore wind farm (OWF). The model is created for OWF project developers to optimise strategic resources to meet their maintenance demand. The model takes into account various maintenance categories on a full range of wind turbine components; the failure rate associated with each component is dependent on wind speed in order to consider weather uncertainty. Weibull distribution is used to predict the probability of wind speed occurring during a given period based on available historical data. The performance of the proposed optimisation model has been validated using reference cases and a UK OWF in operation. Various optimal solutions are investigated for the problems with increased and decreased mean turbine failure rates as a sensitivity test of the model. Full article