Sustainable Operations and Computers最新文献_第3页

Unraveling the obstacles to transform agro-wastes into value-added bioproducts: Implications for sustainability 消除将农业废物转化为增值生物产品的障碍：对可持续性的影响

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-05-21 DOI: 10.1016/j.susoc.2025.05.002

Khandakar Shamit Raja, Ayesha Maliha, Sourav Paul, Md Shihab Shakur, A.B.M. Mainul Bari

The global population and economy heavily rely on agriculture. Various agricultural processes generate copious amounts of waste. Effectively managing these agricultural wastes, or agro-waste, is necessary to mitigate environmental pollution, reduce greenhouse gas emissions, and achieve carbon neutrality and sustainability. This has led to increased scientific interest in converting agro-waste into value-added bioproducts. However, the obstacles that hinder the adoption and implementation of this technology have still not been explored much. This study, therefore, employs a comprehensive novel integrated multi-criteria decision-making (MCDM) approach, combining the Interval-Valued Type 2 Intuitionistic Fuzzy (IVT2IF) theory with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate the obstacles. This approach aims to identify, rank, and analyze the interrelationships among the obstacles hindering agro-waste conversion into value-added bioproducts. Through literature review and expert validation, 17 obstacles were identified to be analyzed using the IVT2IF-DEMATEL method. The study’s findings suggest that "uncertainty about the return on investment (ROI) is the most influential obstacle, with a prominence value of 12.17, likely owing to its direct influence on investment decisions and the economic viability of agro-waste projects, followed by "lack of regulatory and legislative assistance" (12.02) and "necessity of a high initial investment" (11.77). The study’s findings have the potential to provide policymakers and decision-makers valuable insights for shaping investment decisions and developing effective long-term plans to facilitate and incentivize green agro-businesses that utilize agro-wastes and thus promote carbon neutrality and sustainable development in the agriculture-based industrial sectors.

全球人口和经济严重依赖农业。各种农业生产过程产生大量的废物。有效管理这些农业废物或农业废物对于减轻环境污染、减少温室气体排放以及实现碳中和和可持续性是必要的。这增加了科学对将农业废弃物转化为增值生物产品的兴趣。然而，阻碍采用和实施这项技术的障碍仍然没有被探索得太多。因此，本研究采用一种综合的新型集成多准则决策（MCDM）方法，将区间值型直觉模糊（IVT2IF）理论与决策试验与评估实验室（DEMATEL）方法相结合，对障碍进行评估。该方法旨在识别、排序和分析阻碍农业废弃物转化为增值生物产品的障碍之间的相互关系。通过文献回顾和专家验证，确定了17个障碍，使用IVT2IF-DEMATEL方法进行分析。研究结果表明，“投资回报率的不确定性是影响最大的障碍，其显著值为12.17，可能是因为其对投资决策和农业废物项目的经济可行性有直接影响，其次是”缺乏监管和立法援助“（12.02）和”需要高初始投资"（11.77）。研究结果有可能为政策制定者和决策者提供宝贵的见解，帮助他们制定投资决策和制定有效的长期计划，以促进和激励利用农业废弃物的绿色农业企业，从而促进以农业为基础的工业部门的碳中和和可持续发展。

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引用次数: 0

Sustainable economic production quantity optimization (SEPQ) considering food waste emission and water waste emission using genetic algorithm 基于遗传算法的考虑食物垃圾排放和水垃圾排放的可持续经济产量优化(SEPQ

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-05-21 DOI: 10.1016/j.susoc.2025.05.003

Windy Puspika , Ilyas Masudin , Thomy Eko Saputro , Salman Alfarisi , Dian Palupi Restuputri , S. Sarifah Radiah Shariff

This study presents a novel approach for optimizing Sustainable Economic Production Quantity (SEPQ) by incorporating food waste and water waste emissions into the production planning process through the use of a Genetic Algorithm (GA). The objective is to minimize the environmental impacts while maximizing output volumes and decreasing total production costs. The Genetic Algorithm was utilized in a small and medium-sized firm that specializes in the multi-products food manufacturing sector. The GA was applied to systematically seek solutions that were close to optimal through 100 simulations. By implementing the optimized parameters in a case study of the SME industry, the production quantities for the two products were determined optimally with minimal total annual costs. The sensitivity analysis has shown that the SEPQ model remains strong and unaffected by changes in warehouse capacity, costs of solid waste disposal, and costs of liquid waste sales. This highlights the significance of waste management in attaining cost-effectiveness. The study offers helpful insights into the equilibrium between economic efficiency and environmental sustainability in production systems, hence contributing to the continuous advancement of sustainable manufacturing strategies.

本文提出了一种利用遗传算法（GA）将食物浪费和水浪费排放纳入生产计划过程的可持续经济产量（SEPQ）优化方法。目标是尽量减少对环境的影响，同时最大限度地提高产量，降低总生产成本。遗传算法应用于一家专门从事多产品食品生产的中小型企业。通过100次模拟，应用遗传算法系统地寻找接近最优的解。通过在中小企业行业的案例研究中实施优化参数，以最小的年总成本确定了两种产品的最佳生产数量。敏感性分析表明，SEPQ模型仍然强大，不受仓库容量、固废处理成本和废液销售成本变化的影响。这突出了废物管理在取得成本效益方面的重要性。该研究对生产系统中经济效率和环境可持续性之间的平衡提供了有益的见解，从而有助于可持续制造战略的不断推进。

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引用次数: 0

ML2MAS: a multi-agent reinforcement learning and BNNs-GAN integration framework for smart manufacturing optimization ML2MAS：用于智能制造优化的多智能体强化学习和BNNs-GAN集成框架

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-07-07 DOI: 10.1016/j.susoc.2025.07.003

Shadia Yahya Baroud, Nor Adnan Yahaya

The rise of smart manufacturing, driven by advanced technological enablers, has transformed traditional production processes, creating new opportunities for operational efficiency and predictive maintenance (PdM). This study introduces ML2MAS, an innovative framework that integrates machine learning (ML) models within a multi-agent reinforcement learning (MAS-RL) system to enhance PdM capabilities in manufacturing environments. ML2MAS combines Bayesian Neural Networks (BNNs) for handling high-dimensional data and quantifying prediction uncertainty alongside Generative Adversarial Networks (GANs) for synthetic data generation, addressing the challenge of limited labelled datasets and improving model robustness. Integrating these components within MAS enables PdM to make decentralized, real-time decisions. Empirical results from a case study demonstrate substantial improvements, achieving a 99 % F1-score in predictive accuracy and notable reductions in maintenance costs. The proposed ML2MAS framework ensures a cohesive, adaptive PdM solution and contributes to more sustainable and efficient manufacturing operations.

在先进技术的推动下，智能制造的兴起改变了传统的生产流程，为运营效率和预测性维护（PdM）创造了新的机会。本研究介绍了ML2MAS，这是一个创新框架，将机器学习（ML）模型集成到多智能体强化学习（MAS-RL）系统中，以增强制造环境中的PdM能力。ML2MAS结合了贝叶斯神经网络（BNNs）处理高维数据和量化预测不确定性，以及生成对抗网络（GANs）合成数据生成，解决了有限标记数据集的挑战，提高了模型的鲁棒性。将这些组件集成到MAS中，使PdM能够做出分散的实时决策。案例研究的经验结果表明，该方法有了实质性的改进，在预测准确性方面达到了99%的f1分，并显著降低了维护成本。提议的ML2MAS框架确保了一个内聚的、自适应的PdM解决方案，并有助于更可持续和高效的制造运营。

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引用次数: 0

Decisions and coordination of authorized remanufacturing supply chain considering power structures and carbon tax policy 考虑权力结构和碳税政策的授权再制造供应链决策与协调

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-01-09 DOI: 10.1016/j.susoc.2025.01.001

Yanpei Cheng , Xiqiang Xia , Yanliang Zhang , Jingrui Zhang

The exponential expansion of the remanufacturing industry is propelled by a dual impetus of environmental conservation and economic advantages. Numerous original equipment manufacturers (OEMs) opt to entrust remanufacturing operations to authorized remanufacturers (ARs). Recognizing the power differences between an OEM and an AR, this paper establishes a game model based on three power structures: namely, OEM-led Stackelberg (NS model), AR-led Stackelberg (RS model) and Vertical Nash (VN model). We explore the effect of various power structures on pricing decisions and coordination in the authorized remanufacturing supply chain. Through equilibrium comparison, the following conclusions can be drawn: (1) In the RS model, the lowest authorized fee contributes to maximizing the market share of remanufactured products. However, the OEM's intellectual property is not adequately protected. In the VN model, heightened market competition will result in the lowest profit for both manufacturers. Furthermore, due to the latecomer advantage, the OEM achieves the highest profit in the RS model, while the AR earns the highest profit in the NS model. (2) In either model, implementing the carbon tax policy leads to an increase in price of both products, a decrease in the production of new products, an increase in the production of remanufactured products, and an overall reduction in carbon emissions. Notably, it is the NS model that exhibits the greatest environmental improvement. Conversely, the extent of environmental improvements in the VN and RS models depends on the consumers’ preferences for remanufactured products. (3) Through a cost-sharing contract, in which the AR collaborates with the OEM to share recycling costs, both the recovery rate of discarded products and the sales of remanufactured products increase significantly. A Pareto improvement in profits is observed when the sharing ratio exceeds a certain threshold. However, it's crucial to acknowledge that the overall profits of the supply chain do not reach the level that can be achieved through centralized decision-making. Based on this insight, a cost-sharing fixed authorized fee contract is introduced. In this arrangement, the AR compensates the OEM for its share of recycling costs by paying a fixed authorized fee, thereby facilitating supply chain coordination.

再制造产业的指数级扩张是在环境保护和经济优势的双重推动下实现的。许多原始设备制造商（oem）选择将再制造业务委托给授权的再制造商（ar）。认识到主机厂和AR之间的权力差异，本文建立了基于三种权力结构的博弈模型：主机厂主导的Stackelberg （NS模型）、AR主导的Stackelberg （RS模型）和垂直纳什（VN模型）。本文探讨了授权再制造供应链中不同权力结构对定价决策和协调的影响。通过均衡比较，可以得出以下结论：(1)在RS模型中，授权费用最低有利于再制造品市场份额最大化。然而，OEM的知识产权没有得到充分的保护。在VN模型中，市场竞争加剧将导致两家制造商的利润最低。此外，由于后发优势，OEM在RS模型中获得的利润最高，AR在NS模型中获得的利润最高。(2)在两种模型中，实施碳税政策均导致两种产品价格上涨，新产品产量减少，再制造产品产量增加，总体碳排放量减少。值得注意的是，NS模式显示出最大的环境改善。相反，VN和RS模型中环境改善的程度取决于消费者对再制造产品的偏好。(3)通过成本分担合同，AR与OEM共同分担回收成本，废旧产品的回收率和再制造产品的销量均显著提高。当分配比例超过某一阈值时，可以观察到利润的帕累托改善。然而，至关重要的是要承认，供应链的整体利润并没有达到通过集中决策可以实现的水平。在此基础上，引入了成本分担固定授权费合同。在这种安排下，代工商通过支付固定的授权费用来补偿OEM的回收成本份额，从而促进供应链协调。

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引用次数: 0

Design of sustainable supply chain networks for industrial production with the consideration of carbon emission reduction 考虑碳减排的工业生产可持续供应链网络设计

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-07-03 DOI: 10.1016/j.susoc.2025.07.001

Ying Li

With the increasing awareness of consumers’ low-carbon preferences and the implementation of government energy-saving and emission reduction policies, designing supply chain networks solely based on economic benefits is no longer sufficient to meet the development needs of enterprises. Moreover, the drawbacks of traditional supply chain network design that only considers the goal of maximizing profits or minimizing costs are becoming increasingly prominent. To this end, this study proposes a sustainable supply chain network structure for industrial production, which includes a dual-objective planning function model that maximizes profits and minimizes emissions, and a closed-loop supply chain network framework based on the product life cycle. The optimal solution of the function is obtained by using the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) based on elite strategies after the improvement of immune operators. Empirical results show that the average values of time index (198), convergence measure (0.34) and interval index (623) in the improved NSGA-II algorithm are better than in other algorithms and this algorithm has clearer explanation for the decisions it made. It is also effective in helping companies choose emission reduction technologies and low-cost production facilities. With the improved algorithm, the total cost as well as carbon emissions can be reduced. The research method provides a new perspective for carbon emission management and sustainable development, and offers practical solutions for enterprises to reduce their carbon footprint and allocate resources reasonably in production and supply chain management. It helps to improve supply chain resilience and achieve sustainable development goals under economic development.

随着消费者低碳偏好意识的增强和政府节能减排政策的实施，单纯从经济效益出发设计供应链网络已不足以满足企业的发展需求。此外，传统供应链网络设计只考虑利润最大化或成本最小化目标的弊端也日益突出。为此，本研究提出了一种可持续的工业生产供应链网络结构，包括以利润最大化和排放最小化为目标的双目标规划函数模型和基于产品生命周期的闭环供应链网络框架。在对免疫算子进行改进后，采用基于精英策略的非支配排序遗传算法- ii （NSGA-II）求解该函数的最优解。实证结果表明，改进后的NSGA-II算法的时间指标（198）、收敛测度（0.34）和区间指标（623）的平均值均优于其他算法，对所做决策的解释更加清晰。在帮助企业选择减排技术和低成本生产设施方面也很有效。改进后的算法可以降低总成本和碳排放。研究方法为碳排放管理和可持续发展提供了新的视角，为企业在生产和供应链管理中减少碳足迹、合理配置资源提供了切实可行的解决方案。它有助于提高供应链弹性，实现经济发展下的可持续发展目标。

{"title":"Design of sustainable supply chain networks for industrial production with the consideration of carbon emission reduction","authors":"Ying Li","doi":"10.1016/j.susoc.2025.07.001","DOIUrl":"10.1016/j.susoc.2025.07.001","url":null,"abstract":"<div><div>With the increasing awareness of consumers’ low-carbon preferences and the implementation of government energy-saving and emission reduction policies, designing supply chain networks solely based on economic benefits is no longer sufficient to meet the development needs of enterprises. Moreover, the drawbacks of traditional supply chain network design that only considers the goal of maximizing profits or minimizing costs are becoming increasingly prominent. To this end, this study proposes a sustainable supply chain network structure for industrial production, which includes a dual-objective planning function model that maximizes profits and minimizes emissions, and a closed-loop supply chain network framework based on the product life cycle. The optimal solution of the function is obtained by using the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) based on elite strategies after the improvement of immune operators. Empirical results show that the average values of time index (198), convergence measure (0.34) and interval index (623) in the improved NSGA-II algorithm are better than in other algorithms and this algorithm has clearer explanation for the decisions it made. It is also effective in helping companies choose emission reduction technologies and low-cost production facilities. With the improved algorithm, the total cost as well as carbon emissions can be reduced. The research method provides a new perspective for carbon emission management and sustainable development, and offers practical solutions for enterprises to reduce their carbon footprint and allocate resources reasonably in production and supply chain management. It helps to improve supply chain resilience and achieve sustainable development goals under economic development.</div></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"6 ","pages":"Pages 229-245"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic path optimization of cold chain logistics based on improved ant colony algorithm from a low-carbon perspective 低碳视角下基于改进蚁群算法的冷链物流动态路径优化

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-10-04 DOI: 10.1016/j.susoc.2025.10.001

Hua Wang

Cold chain logistics face challenges such as high energy consumption, high carbon emissions, and high rates of cargo spoilage during transportation. These issues increase the operating costs of enterprises and also negatively affect the ecological environment. Therefore, this study constructs a cold chain logistics distribution path optimization model that comprehensively considers time window constraints, carbon emission costs, and cargo damage costs. Based on this, an I-ACO algorithm is proposed to achieve low-carbon and efficient distribution path planning. The I-ACO algorithm enhances the accuracy and adaptability of path selection by introducing dynamic time warping technology and chaos theory, thereby improving the search ability and solution quality of the algorithm. The results indicated that the I-ACO had a very high overall sample fitting degree, with a correlation coefficient of 0.99 between the actual output value and the expected value. Its accuracy reached 95.2 %, with fast convergence speed and a carbon emission of only 0.04 kg, significantly lower than other algorithms. The I-ACO could effectively reduce logistics costs, decrease carbon emissions, and improve delivery efficiency and customer satisfaction. The research algorithm provides an efficient, accurate, and environmentally friendly solution for optimizing the distribution path of cold chain logistics. This is beneficial for cold chain logistics enterprises to achieve economic benefits and better fulfill social responsibilities, and promote the sustainable development of the industry.

冷链物流面临着高能耗、高碳排放、运输过程中货物腐败率高等挑战。这些问题增加了企业的经营成本，也对生态环境产生了负面影响。因此，本研究构建了综合考虑时间窗约束、碳排放成本和货物损坏成本的冷链物流配送路径优化模型。在此基础上，提出了一种I-ACO算法，实现低碳高效的配送路径规划。该算法通过引入动态时间翘曲技术和混沌理论，提高了路径选择的准确性和自适应性，从而提高了算法的搜索能力和求解质量。结果表明，I-ACO的整体样本拟合程度非常高，实际产出值与期望值的相关系数为0.99。其精度达到95.2%，收敛速度快，碳排放量仅为0.04 kg，明显低于其他算法。I-ACO可以有效降低物流成本，减少碳排放，提高配送效率和客户满意度。研究算法为冷链物流配送路径优化提供了高效、准确、环保的解决方案。这有利于冷链物流企业实现经济效益，更好地履行社会责任，促进行业的可持续发展。

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引用次数: 0

Ethics statement and consent to participate regarding previously published articles 关于先前发表的文章的伦理声明和参与同意

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-08-20 DOI: 10.1016/j.susoc.2025.07.004

引用次数: 0

Corrigendum to “An Integrated Pollution-Generating Technology and Temperature Alignment Rating Model for Eco-Efficiency Estimation” [Sustainable Operations and ComputersVolume 6, 2025, Pages 246-255, Article: 123] “生态效率评估的综合污染产生技术和温度校准评级模型”的更正[可持续操作和计算机]卷6,2025，页246-255，条款：123]

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-12-04 DOI: 10.1016/j.susoc.2025.11.001

引用次数: 0

Carbon information disclosure mechanism considering extended producer responsibility and low carbon preference under outsourcing remanufacturing 考虑生产者延伸责任和外包再制造下的低碳偏好的碳信息披露机制

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-02-03 DOI: 10.1016/j.susoc.2025.02.001

Xiukun Zhao , Yajiao Liu , Jinhui Zou , Qianzhou Deng , Rong Han

The implementation of EPR information disclosure responsibility is one of the four key tasks in the implementation of EPR. In order to explore the impact mechanism of carbon information disclosure behavior of manufacturers and remanufacturers considering extended producer responsibility (EPR) and low-carbon preference on corporate profits of outsourced remanufacturing closed-loop supply chain, this study constructs a two-level supply chain composed of manufacturers and remanufacturers. This paper integrates corporate carbon disclosure with recycling and remanufacturing activities and develop four models according to whether enterprises fulfill EPR information disclosure responsibilities. This study analyzes the equilibrium strategies of manufacturers and remanufacturers in four modes through Stackelberg game and finds that: Firstly, the implementation of EPR is more conducive to enhancing the overall profitability of enterprises compared to a scenario without EPR. Concurrently, enterprises will focus more on consumer low-carbon preferences and market dynamics when implementing EPR policies, thereby striving to enhance their competitive edge. Secondly, manufacturers and remanufacturers should collaborate actively in carbon information disclosure activities to achieve coordinated development between upstream and downstream enterprises. Such collaboration is beneficial for increasing the product recovery rate of remanufacturers. Lastly, social welfare is maximized under a full EPR scenario. The state of social welfare varies with changes in cost subsidy disclosure and consumer low-carbon preferences. Notably, the disclosure efforts of remanufacturers contribute more significantly to social welfare than those of manufacturers.

落实EPR信息披露责任是实施EPR的四大关键任务之一。为探讨生产商和再制造企业在考虑生产者延伸责任（EPR）和低碳偏好的情况下，其碳信息披露行为对外包再制造闭环供应链企业利润的影响机制，本研究构建了由生产商和再制造企业组成的两级供应链。本文将企业碳信息披露与回收再制造活动相结合，并根据企业是否履行 EPR 信息披露责任建立了四个模型。本研究通过斯塔克尔伯格博弈分析了制造商和再制造商在四种模式下的均衡策略，结果表明首先，与未实施 EPR 的情景相比，实施 EPR 更有利于提高企业的整体盈利能力。同时，企业在实施 EPR 政策时会更加关注消费者的低碳偏好和市场动态，从而努力提升自身的竞争优势。其次，生产企业和再制造企业应在碳信息披露活动中积极合作，实现上下游企业的协调发展。这种合作有利于提高再制造企业的产品回收率。最后，在完全 EPR 的情况下，社会福利最大化。社会福利状况随着成本补贴披露和消费者低碳偏好的变化而变化。值得注意的是，与制造商相比，再制造商的披露努力对社会福利的贡献更大。

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引用次数: 0

Green investment in a supply chain with manufacturer competition: Pricing and strategy selection 具有制造商竞争的供应链绿色投资：定价与策略选择

Sustainable Operations and Computers

Pub Date : 2025-01-01 Epub Date: 2025-10-24 DOI: 10.1016/j.susoc.2025.10.005

Jing Yu , Mingming Shen , Chi Zhou , Yaoyang Li , Zi Qin

This research delves into green investment, a strategy for manufacturers to attain a competitive advantage, to examine its impact on their competitive decision-making. This study constructs a game-theoretic model to analyze the impact of two competing manufacturers’ choices regarding green investments when selling their products through a common retailer. The analysis is divided into three scenarios: Model N (neither manufacturer makes a green investment), Model S (only one manufacturer makes a green investment), and Model B (both manufacturers make green investments). By deriving and comparing the equilibrium prices and profits for each scenario, the paper reveals that retailers consistently gain advantages from manufacturers’ green investments. However, a manufacturer’s decision to invest in green initiatives is contingent upon multiple factors, including the strategies of its competitors, the efficiency of the green investment, the level of product greenness, and the proportion of customers willing to pay a premium for environmentally friendly products.

本研究探讨绿色投资作为制造商获得竞争优势的策略，对其竞争决策的影响。本文构建了一个博弈论模型，分析两家竞争厂商在通过同一零售商销售产品时对绿色投资选择的影响。将分析分为三种情景：模型N（两家制造商都不进行绿色投资）、模型S（只有一家制造商进行绿色投资）和模型B（两家制造商都进行绿色投资）。通过推导和比较每种情景下的均衡价格和均衡利润，本文揭示了零售商从制造商的绿色投资中持续获得优势。然而，制造商投资绿色倡议的决定取决于多种因素，包括竞争对手的策略、绿色投资的效率、产品的绿色程度以及愿意为环保产品支付溢价的客户比例。

引用次数: 0