A fast nondominated sorting-based MOEA with convergence and diversity adjusted adaptively

The Journal of Supercomputing, Год журнала: 2023, Номер 80(2), С. 1426 - 1463

Опубликована: Июль 18, 2023

Язык: Английский

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Big data-driven optimization for sustainable reverse logistics network design

Journal of Ambient Intelligence and Humanized Computing, Год журнала: 2022, Номер 14(8), С. 10867 - 10882

Опубликована: Авг. 4, 2022

Язык: Английский

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Developing a sustainable forward supply chain configuration for construction industry under uncertainty condition: a case study

Clean Technologies and Environmental Policy, Год журнала: 2023, Номер 26(4), С. 1197 - 1225

Опубликована: Дек. 13, 2023

Язык: Английский

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A Novel Approach to Sustainable Grain Transportation: A Neutrosophic Bi-Objective Model for Cost and Time Optimization

IEEE Access, Год журнала: 2024, Номер 12, С. 68497 - 68510

Опубликована: Янв. 1, 2024

As the global economy continues to grow, need for transportation also grows. Transportation researchers are developing new methods integrating technologies into existing systems and addressing associated challenges. This paper addressed bi-objective fixed charge solid problem that consider two objectives minimizing total cost, including variable costs, time. It is a challenging optimization problem, as it difficult find solution simultaneously minimizes both objectives. Additionally, with fixed-charge (BOFCSTP) under uncertainty neutrosophic concept presented here. constructed all parameters such fixed-charge, source availability requirements values. Neutrosophic sets efficiently handling indeterminacy imprecise data in many fields single-valued extension simpler form of NS. Further, convert values crisp ranking function used. To solve considered BOFCSTP different approaches employed namely, linear programming, goal fuzzy programming get compromised problem. real-life given numerical example results compared approaches.

Язык: Английский

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Factor analysis of environmental effects in circular closed‐loop supply chain network design and modelling under uncertainty in the manufacturing industry

Environmental Quality Management, Год журнала: 2024, Номер 34(1)

Опубликована: Апрель 2, 2024

Abstract Sustainable development and competitive advantage are impacted by strategic choices that maximize resource value reduce waste. Numerous instances of thriving OEM remanufacturing can be observed, predominantly in the business‐to‐business domain. The significance Closed‐Loop Supply Chain (CLSC) generating managing recovery process is widely acknowledged within supply chain industry. Manufacturing companies now have to come up with green design strategies response recent changes environmental regulations. This study designates specific features circular closed‐loop supply‐chain considering end‐of‐life products. Uncertainty various aspects, such as acquisition, processing, market stages, impeding progress economies also sustainable chains (CLSCs). has led increased complexity processes decreased efficiency. To address this issue, proposes a comprehensive, integrative approach for establishing CLSC network adapts fluctuating demand through questionnaire analysis. Moreover, introduces multi‐objective optimization model dual‐channel network, aiming enhance flow. considers both economic objectives achieve efficient system. determine ideal design, research linear programming mixed integer, where take place one three ways: material recovery, component or product recovery. findings from thorough analysis data backup inform managers ways improve returns terms quantity quality. uncertainty problem, developed fuzzy credibility constraint technique Simulated Annealing algorithm. Using cutting‐edge methods, it explored compared how sensitivity results, impact altering problem parameters, performance suggested respectively.

Язык: Английский

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A rolling horizon heuristic approach for a multi-stage stochastic waste collection problem

European Journal of Operational Research, Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Язык: Английский

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Fuzzy multi-objective optimization model to design a sustainable closed-loop manufacturing system

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2591 - e2591

Опубликована: Янв. 13, 2025

Republicans and Democrats practically everywhere have been demonstrating concerns about environmental conservation to achieve sustainable development goals (SDGs) since the turn of century. To promote fuel (energy) savings a reduction in amount carbon dioxide CO 2 emissions several enterprises, actions taken based on concepts described. This study proposes an environmentally friendly manufacturing system designed minimize impacts. Specifically, it aims develop process that accounts for energy consumption from direct indirect sources. A multi-objective mathematical model has formulated, incorporating financial constraints, overall costs, consumption, within framework. The input parameters real-world situations are generally unpredictable, so fuzzy will be developed as way handle it. validity proposed ecological industrial design tested using scenario-based approach. Results demonstrate high reliability, applicability, effectiveness network when analyzed techniques.

Язык: Английский

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Optimization of Logistics Distribution Centers Based on Economic Efficiency and Sustainability: Data Support from the Hohhot–Baotou–Ordos–Ulanqab Urban Agglomeration

Sustainability, Год журнала: 2025, Номер 17(7), С. 3273 - 3273

Опубликована: Апрель 7, 2025

This study proposes a nonlinear 0-1 mixed-integer programming model for optimizing the location of logistics distribution centers within Hohhot–Baotou–Ordos–Ulanqab urban agglomeration, integrating transportation costs, carbon emissions, and operational coefficients. The optimization problem is solved using genetic algorithm (GA), whose robustness systematically validated through comparative analyses with linear (LP) alternative heuristic methods including simulated annealing (SA) particle swarm (PSO). Comprehensive sensitivity are conducted on critical parameters—including demand fluctuations, pricing mechanisms, center capacity, land use impact, water resource constraints—to evaluate model’s adaptability under diverse scenarios. research methodology incorporates environmental impact factors, emission utilization, management, thereby extending traditional frameworks to address region-specific ecological concerns. empirical results demonstrate that optimized configuration significantly reduces costs while simultaneously enhancing both economic efficiency sustainability, thus fostering regional coordination. makes several key contributions: (1) developing an integrated decision-making framework balances sustainability; (2) incorporating factors into model; (3) establishing calibration specifically tailored ecologically sensitive regions; (4) demonstrating potential synergistic objectives strategic network planning.

Язык: Английский

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Towards low-carbon e-commerce return logistics: optimization for simultaneous pickup and delivery

Industrial Management & Data Systems, Год журнала: 2025, Номер unknown

Опубликована: Апрель 15, 2025

Purpose The rapid expansion of e-commerce platforms has made collaborative logistics networks essential. This study tackles the intricate challenge optimizing pickup and delivery activities in constrained areas, striving to meet ever-increasing demands while maintaining tight control over distribution costs. Design/methodology/approach develops a vehicle routing problem with simultaneous time windows (VRPSPDTW) model validates it using simulated annealing (SA) hybrid algorithm combining tabu search (SA-TS). Random requests are generated following uniform evaluate model’s performance. Findings research VRPSPDTW SA SA-TS algorithms, demonstrating their superior flexibility comprehensiveness compared traditional methods. findings demonstrate effectiveness managing dynamic demands, first-mile last-mile coordination, improving efficiency logistics. Research limitations/implications To validate model, this employed simplified demand patterns distribution. Future could integrate broader range constraints test scalability across various scenarios enhance practical applicability. Practical implications proposed optimizes routes centers, enhancing network reducing operational costs efficiency, significant value. Originality/value provides an innovative solution for networks. By addressing pickups deliveries heuristic fosters demonstrates approaches challenges

Язык: Английский

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Deep reinforcement learning-based optimisation of reverse logistics transport in closed-loop supply chain

Proceedings of the Institution of Civil Engineers - Transport, Год журнала: 2025, Номер unknown, С. 1 - 18

Опубликована: Апрель 21, 2025

Traditional optimisation methods often struggle to address the stochastic and dynamic characteristics of reverse logistics. Deep reinforcement learning (DRL), with its ability learn adaptive policies under uncertainty, is well suited this challenge. This study investigates green transportation strategies in logistics systems through a DRL framework. By formulating problem as Markov decision process, integrates economic profitability, carbon dioxide emissions reduction efficiency into reward function. The experimental results basic model extended show that superior performance DRL-based approach, which significantly surpasses traditional such genetic algorithms random adaptability effectiveness. These findings provide theoretical foundations practical insights for advancing sustainable practices complex systems.

Язык: Английский

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