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Open Access Article | |
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Evolutionary game analysis of green packaging supply chain cooperative development considering consumer preferences for traceability
, Available Online, April, 25, 2025 Jing Peng, Yutong Shi and Jinfeng Zeng ![]() |
Abstract: Promoting green packaging production represents a crucial strategy for the packaging industry in its pursuit of sustainable development. This study constructs a three-party evolutionary game model involving suppliers, manufacturers, and brands to examine their strategic decision-making under various scenarios. Simulation and analysis yield three principal findings. First, the system initially begins at (0,0,0) and may transition to a manufacturer-dominated intermediate state—either (1,1,0) or (0,1,1)—before gradually stabilizing at the equilibrium point (1,1,1). Second, supply chain decision-making is influenced by both internal and external factors. Internal factors include penalty mechanisms, carbon trading allocation, and cooperative concessions, whereas external factors comprise consumer preferences for traceability and the environmental attributes of packaging. Specifically, suppliers are primarily driven by internal factors, manufacturers are predominantly influenced by external factors, and brands are impacted by a combination of both. Third, serving as the central node in the supply chain, manufacturers enable upstream and downstream integration through traceable production, refine cooperative concession mechanisms to enhance brand participation, and harness market signals to promote green transformation and co-production among suppliers. Therefore, the effective management of the green packaging supply chain necessitates the establishment and ongoing refinement of a tripartite active cooperation mechanism. Additionally, cultivating consumer preferences for traceability is essential for advancing the long-term sustainable development of the supply chain. DOI: 10.5267/j.ijiec.2025.4.009 Keywords: Evolutionary game, Traceability, Green packaging supply chain, Cooperative development, Consumer traceability preference | |
Open Access Article | |
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Multi-objective artificial bee colony algorithm for energy-efficient scheduling of unrelated parallel batch processing machines with flexible preventive maintenance
, Available Online, April, 22, 2025 Yarong Chen, Longlong Xu, Mudassar Rauf, Pei Li and Jabir Mumtaz ![]() |
Abstract: The parallel batch-processing machine scheduling problem is widely present in industries such as manufacturing, service, and healthcare, and becomes more complex when incorporating flexible preventive maintenance (FPM). This paper presents a mixed-integer programming (MIP) model and a multi-objective artificial bee colony (MOABC) algorithm to tackle the unrelated parallel batch-processing machine scheduling problem with flexible preventive maintenance (UPBPM-FPM). The objective is to simultaneously minimize the makespan, earliness and tardiness, and total energy consumption, providing a comprehensive solution to optimize both scheduling efficiency and energy use while incorporating preventive maintenance considerations. The MOABC algorithm integrates three key innovations: (1) a novel processing power-feature information (PP-FI) heuristic to generate high-quality initial solutions, (2) a hybrid selection strategy combining the hypervolume index and roulette wheel approach to improve diversity and convergence, and (3) a set of random and goal-oriented neighborhood search methods to enhance Pareto frontier. Experimental results demonstrate that the MOABC algorithm outperforms three classical algorithms, NSGA-III, ABC, and PSO, in terms of convergence, diversity, and robustness of the Pareto solutions. This study provides a robust framework for energy-efficient scheduling in complex manufacturing environments. DOI: 10.5267/j.ijiec.2025.4.008 Keywords: Artificial bee colony algorithm, Multi-objective optimization, Parallel batch-processing machine, Energy-efficient scheduling, Flexible preventive maintenance | |
Open Access Article | |
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A dual-layer BOM change control model for efficiency improvement in ETO manufacturing
, Available Online, April, 9, 2025 Chunhua Wan, Yufei Zeng, Ji Ma, Tao Wang and Kaiyang Zhong ![]() |
Abstract: To address the frequent changes, dynamic evolution, and complex collaboration of BOM (Bill of Materials) under ETO (Engineer-to-Order) mode, this paper proposes a dual-layer BOM-based change control model. First, to enable model definition and change expression throughout the product lifecycle, a version control-based BOM model is defined by introducing material revision, material relationship links, and a multi-view mechanism, while also constructing a general BOM structure system. Then, to ensure traceability of product structural changes and cross-view consistency in the ETO mode, we design a dual-layer change traceability model. This model features vertical version chains and horizontal view collaboration traceability as its core components. Finally, an ETO-oriented BOM change operation model is constructed to standardize both in-view change operations and cross-view cooperative operations. This standardization enhances change control capability and lifecycle traceability efficiency of product structures in ETO manufacturing environments. The application of this model in a large equipment manufacturing enterprise shows that it significantly improves the change response efficiency and provides strong support for the digital transformation and supply chain collaboration of ETO enterprises. DOI: 10.5267/j.ijiec.2025.4.007 Keywords: ETO (Engineer-to-Order), BOM (Bill of Materials), Change control, Dual-layer traceability, Supply chain collaboration | |
Open Access Article | |
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A game-theoretic model for renewable and conventional energy generators under tradable green certificate mechanism
, Available Online, April, 9, 2025 Pin-Bo Chen, Cheng Zhuang, Qianyu Hua and Peng Zhang ![]() |
Abstract: This paper explores the strategic behavior of power generators under green certificate trading policies, considering both renewable and conventional energy generators. Using game theory, we construct a Nash equilibrium model that incorporates the unit price of green certificates, the required quantity of certificates, and the cap on the quantity. By applying the Karush-Kuhn-Tucker conditions, we reform this Nash equilibrium problem as a mixed complementarity system, which can be solved by MATLAB software. Furthermore, we conduct sensitivity analysis and numerical tests on a number of important parameters. The results reveal that, under certain conditions, the unit price of green certificates does not affect the number obtained by renewable energy generators or purchased by conventional energy generators. However, as the required number of certificates for conventional energy generators increases, both the quantity of certificates that renewable generators obtained and conventional generators purchased increase proportionally. Additionally, the outcomes of limiting the quantity of green certificates awarded to renewable energy generators align with government regulations on the purchase requirements for conventional energy generators. This research provides new insights for power generators in ensuring financial viability and optimizing operations under green certificate trading policies. By enhancing carbon emission reduction capacity, these findings may contribute to the effective management of the electrical supply chain. DOI: 10.5267/j.ijiec.2025.4.006 Keywords: Tradable green certificate mechanism, Conventional energy generators, Game theory, Renewable energy generators, Sensitivity analysis | |
Open Access Article | |
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MILP model for simultaneous batching, production and distribution operations in single-stage multiproduct batch plants
, Available Online, April, 4, 2025 Aldana S. Tibaldo, Jorge M. Montagna and Yanina Fumero ![]() |
Abstract: Traditionally, the short-term production and distribution activities have been addressed with a decoupled and sequential methodology. Although this approach simplifies the problem, there are several environments where it generates inefficiencies or is simply not applicable. Consequently, the integration of both problems is very valuable in a variety of industrial applications, especially in industries where final products must be delivered to customers shortly after production. This paper presents a mixed-integer linear optimization model that simultaneously solves the production and distribution scheduling in a single-stage multi-product batch facility with multiple non-identical units operating in parallel, where transportation operations are carried out with a heterogeneous fleet of vehicles. As operations are performed in a batch environment, the production and distribution problems also integrate decisions related to the number and size of batches required to meet the demand for multiple products. The capabilities of the proposed approach are illustrated through several cases of study. Finally, these examples are solved with a two-stage approach and the superiority of the solutions using the integrated approach is demonstrated. DOI: 10.5267/j.ijiec.2025.4.005 Keywords: Production and distribution, Short-term, Batch environment, MILP, Integrated approach | |
Open Access Article | |
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An adaptive local search for large-scale parallel machine scheduling in textile production with release dates and sequence-dependent setup times
, Available Online, April, 4, 2025 Mariane Emanuelle Pessoa Santos, Yuri Laio Teixeira Veras Silva and Maria Creuza Borges de Araújo ![]() |
Abstract: This study proposes an adaptive local search heuristic to solve a real-world large-scale parallel machine scheduling problem with release dates and setup times, aiming to minimize total tardiness. The complexity of the problem stems from the need to synchronize machine availability, job release dates, and setup durations, which are crucial for meeting production deadlines and ensuring operational efficiency. Traditional optimization approaches often struggle to deliver timely solutions for large-scale industrial applications. Our heuristic method effectively explores the search space to identify schedules that significantly reduce total tardiness while adhering to the constraints of the production system. The approach was tested using real production data, and the results indicate that the heuristic consistently generated high-quality solutions within short computational times. The approach proved viable and efficient, offering a practical tool for improving scheduling performance and minimizing total tardiness in industries with similar operational constraints. DOI: 10.5267/j.ijiec.2025.4.004 Keywords: Scheduling, Parallel machines, Local search, Total tardiness minimization, Release dates | |
Open Access Article | |
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The non-stationary stochastic lot-sizing with joint replenishment under (R, S) policy and the heuristics
, Available Online, April, 2, 2025 Jufeng Yang and Sujian Li ![]() |
Abstract: This study investigates for the first time the non-stationary stochastic lot-sizing problem involving multi-dealer joint replenishment under the policy (R, S) without fill rate constraints. The planning horizon for each dealer is divided into the replenishment cycle series, accounting for the lead time associated with each joint replenishment cycle. A shortest path model is developed. Through mathematical analysis, the safety stock variables are eliminated, and the multiple variables are reduced to replenishment variables only. The stochastic problem is converted to the deterministic dynamic lot-sizing through expectation analysis. Furthermore, the MLS-MRS heuristic is proposed based on Robinson's Left-Right shift (LS-RS) heuristic by adding a module, the positive cost-saving family shifts. This algorithm improves the optimal solution and notably greatly increases the search speed. DOI: 10.5267/j.ijiec.2025.4.003 Keywords: Lot-sizing, Non-stationary stochastic demands, Joint replenishment, (R, S) policy, Heuristic | |
Open Access Article | |
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Game-theoretic modeling of sustainable intermodal freight transportation: Optimal pricing and energy efficiency strategies under government intervention and fuzzy uncertainty
, Available Online, April, 2, 2025 Qian Long and Qunqi Wu ![]() |
Abstract: Sustainable freight transportation plays a pivotal role in addressing pressing environmental challenges while simultaneously fostering socio-economic development. Governmental entities worldwide are increasingly implementing strategic policy interventions to enhance the sustainability of freight transportation systems. A comprehensive understanding of the complex interactions and dynamics between these policy measures and transportation operations is essential for developing effective sustainable transportation strategies. This study aims to explore the impact of government intervention on pricing strategies, and energy-saving level determination in the transportation sector under conditions of fuzzy uncertainty. While the government looks into three distinct strategies, each with two decision variables, transportation enterprises are considering two alternate scenarios for decision-making. It means that twelve distinct scenarios are being considered by the government. Our analyses reveal that: (1) The government's goals of maximizing social welfare and energy saving cannot be aligned with the enterprises' goals of maximizing profits, regardless of whether decision-making is decentralized or centralized. (2) The carbon cap-and-trade mechanism emerges as the most effective strategy for governmental regulation, whereas transportation enterprises demonstrate optimal responsiveness to subsidy-based policy interventions. (3) Centralized decision-making by transportation enterprises yields superior outcomes across multiple dimensions, including LSSC profitability, social welfare enhancement, and energy conservation efficiency, when contrasted with decentralized decision-making paradigms. (4) The implementation of a carbon cap-and-trade policy by the government, combined with increased investments in environmental awareness and centralized decision-making by transportation enterprises, significantly advances both profit objectives and energy-saving targets. DOI: 10.5267/j.ijiec.2025.4.002 Keywords: Fuzzy uncertainties, Sustainable freight transportation, Road-rail transport, Pricing and energy | |
Open Access Article | |
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Authorization or outsourcing? Investigating remanufacturing decisions under carbon trading policies and remanufacturing subsidies considering trade-in programs
, Available Online, March, 21, 2025 Huijuan Qiao, Xueguo Xu and Xue Lei ![]() |
Abstract: With the introduction of carbon emission policies, subsidy policies, and the promotion of "trade-in" programs worldwide, determining the optimal remanufacturing strategy under various policy environments has become a critical issue. We develop six models to evaluate the effects of three policy combinations—carbon trading alone, carbon trading with consumer subsidies, and carbon trading with remanufacturer subsidies—under authorization and outsourcing remanufacturing strategies. The results show that dual policy of carbon emission trading and government subsidies more effectively promotes remanufacturing than a single carbon trading policy. When consumer subsidies reach a certain threshold, all supply chain members can achieve a win-win outcome, regardless of whether the remanufacturing strategy is authorization or outsourcing. The environmental cost is primarily influenced by carbon emissions from new products. If emissions are high, remanufacturer subsidies should be prioritized; if emissions are lower, consumer subsidies are more effective. Without subsidies, authorization has pricing advantages with low emissions, while outsourcing is more economical with high emissions or under market uncertainty. High carbon trading prices and subsidies increase overall supply chain profits but exhibit diminishing returns as excessive carbon prices increase corporate costs and reduce consumer surplus and social welfare. Moderate subsidies can mitigate these negative effects. DOI: 10.5267/j.ijiec.2025.4.001 Keywords: Carbon emissions trading policy, Remanufacturing subsidy, Trade-in programs, Authorized remanufacturing, Outsourced remanufacturing | |
Open Access Article | |
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A metaheuristic algorithm co-driven by Q-learning and a learning mechanism for the distributed blocking flowshop scheduling problem with preventive maintenance and sequence-dependent setup times
, Available Online, March, 21, 2025 Congcong Sun, Hongyan Sang, Li Yuan, Jinfeng Gong, Hongmin Zhu ![]() |
Abstract: Drawing inspiration from manufacturing production processes like chemical and steel manufacturing, the distributed blocking flowshop scheduling problem with preventive maintenance and sequence-dependent setup times (DBFSP/PM/SDST) is studied. First, it is described by a mixed-integer linear programming model with the objective of minimizing the total flowtime. Second, we propose a Q-learning and learning mechanism co-driven approach, integrating it into the discrete grey wolf optimization algorithm (DGWO_Q). In the algorithm, the neighborhood search structure is adjusted using Q-learning based on dynamic feedback from the environment. The balance between exploration and exploitation can be improved by introducing learning mechanisms in the search phase that can guide the grey wolf as it approaches the prey. Furthermore, a differential hunting strategy is designed to prevent the algorithm from falling into local optima. Third, a heuristic that enhances the quality of the initial solution is proposed for the problem characteristics. Finally, the proposed DGWO_Q is compared with four conventional efficient algorithms in numerical experiments on 225 instances of different sizes. Experimental results show that the DGWO_Q algorithm demonstrates excellent performance across test cases of various scales, effectively reducing production cycle time, setup times and the impact of maintenance downtime on production efficiency. It provides an efficient intelligent optimization approach for solving the complex scheduling problem. DOI: 10.5267/j.ijiec.2025.3.006 Keywords: Distributed blocking flowshop scheduling problem, Preventive maintenance, Sequence-dependent setup times, Discrete grey wolf optimization algorithm, Q-learning | |
Open Access Article | |
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A hybrid time series analysis-genetic algorithm-support vector machine model for enhanced landslide prediction
, Available Online, March, 14, 2025 Chao He, Junwen Peng, Wenhui Jiang, Chaofan Wang, Junting Li and Zefu Tan ![]() |
Abstract: Landslide prediction is a critical task for ensuring public safety and preventing economic loss in regions prone to such natural disasters. Traditional models for landslide prediction often lack accuracy and precision because of the intricate interactions between various factors that lead to landslide events. To tackle this issue, we introduce an innovative hybrid approach for landslide prediction that combines Time Series Analysis (TSA), Genetic Algorithm (GA), and Support Vector Machine (SVM). TSA decomposes landslide displacement data into trend, seasonal, and residual components, improving the clarity of the data. GA optimizes the hyperparameters of SVM, ensuring the most effective application of the SVM. Finally, the SVM is trained on detrended data, producing a model capable of accurately predicting future landslides. Our experimental outcomes manifest that the TSA-GA-SVM model we advanced performs far better than the individual TSA and SVM models when it comes to forecasting landslide displacement. The hybrid model achieved a mean absolute error of 0.15 m compared to 0.42 m for TSA and 0.38 m for SVM alone. Sensitivity analysis revealed that increasing GA population size improved model stability, while higher mutation rates led to more variable predictions. The model showed good generalization ability, performing well across different regions and under various geological and hydrological conditions. This research not only advances the state of the art in landslide prediction but also provides a practical tool for authorities to implement in their disaster prevention and management strategies. DOI: 10.5267/j.ijiec.2025.3.005 Keywords: Landslide prediction, Genetic algorithm, Support vector machine, Optimization, Regional analysis, Machine learning | |
Open Access Article | |
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Performance investigation of metaheuristics for the just-in-time single-machine under different time windows and setup restrictions
, Available Online, March, 14, 2025 Miguel Gonçalves de Freitas, Alex Paranahyba Abreu, Fábio José Ceron Branco, Helio Yochihiro Fuchigami and Rian Tavares de Mello ![]() |
Abstract: In this paper, we assess the performance of five metaheuristics for the single-machine under different time windows and sequence-dependent setup times, optimizing the total weighted earliness and tardiness: Iterated Greedy Algorithm (IGA), Artificial Bee Colony (ABC), Bat Algorithm (BA), Particle Swarm Optimization (PSO), and Fireworks Algorithm (FWA). Many real-world situations require delivery in a specific time interval, analogous to optimization problems with a time window in the Just-in-Time philosophy. Also, several practical situations require different time intervals to prepare the environment to process the activities depending on what was immediately done and what will be executed next, characterizing the sequence-dependent setup problem. These cases are common among operations handling materials of diverse colors, different temperatures, or high demands on sterilization requirements. Statistical results highlight the superiority of the FWA, with the best results in all the problem dimensions analyzed, especially in the larger-size instances, with only 1.23% average relative deviation against 61.18% of the known Iterated Greedy algorithm. DOI: 10.5267/j.ijiec.2025.3.004 Keywords: Scheduling, Fireworks algorithm, Earliness-tardiness, Time windows, Sequence-dependent setup, Metaheuristics | |
Open Access Article | |
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To cooperate or not? The cooperation conditions of different new energy vehicle manufacturers on power battery under government subsidy
, Available Online, March, 5, 2025 Yiwen Zhang and Qi Wang ![]() |
Abstract: To stimulate the new energy vehicle (NEV) market, China has raised the bar for NEV subsidies so that only NEVs with high endurance are eligible for subsidies. As a result, the NEV manufacturers may cooperate on power batteries, which makes their relationship shift from competition to downstream competition and upstream cooperation, i.e. co-opetition. Based on this, this paper investigates the cooperation conditions between the leading NEV manufacturer and the emerging NEV manufacturer on power batteries under the revised subsidy policy. By establishing a Cournot model, we first analyze the optimal decisions of the two manufacturers under government subsidy policy in competition and co-opetition scenarios, respectively. By comparing the profits of NEV manufacturers in these two scenarios, we derive the conditions under which they can cooperate on power batteries. The results show that whether the NEV manufacturers can cooperate depends on the power battery cost of the emerging NEV manufacturer. DOI: 10.5267/j.ijiec.2025.3.003 Keywords: Co-opetition supply chain, New energy vehicle, Power battery, Cooperation conditions |
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