Optimizing Multi Cross-Docking Systems with a Multi-Objective Green Location Routing Problem Considering Carbon Emission and Energy Consumption

Meidutė‐Kavaliauskienė, Ieva; Sütütemiz, Nihal; Yıldırım, Figen; Ghorbani, Shahryar; Činčikaitė, Renata

doi:10.3390/en15041530

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…Electric vehicles were born in response to environmental sustainability, but the location and development of charging facilities have also become the biggest problem faced by electric vehicles in use. The ELRP, as a new type of LRP problem, its solution directly affects the original intention of whether electric vehicles can achieve environmental sustainability [26,27]. Now, there are more and more research achievements in this area.…”

Section: Literature Reviewmentioning

confidence: 99%

An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem

Cai,

Wang,

et al. 2023

Sustainability

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Electric vehicles invented for environmental sustainability are prone to adverse impacts on environmental sustainability due to the location and construction of their charging facilities. In this article, an artificial Physarum polycephalum colony is proposed to solve the novel challenging problem. First, the electric location-routing problem is established as a multi-objective network panning model with electric constraints to provide the optimal charging infrastructure layout, electric vehicle maintenance costs, and traffic conditions. The electric facility location problem and vehicle routing problem are integrated by integer programming, which considers the total distance, total time, total cost, total number of electric vehicles, and order fill rate. Second, an artificial Physarum polycephalum colony is introduced to solve the complex electric location-routing problem and includes the two basic operations of expansion and contraction. In the expansion operation, the optimal parent individuals will generate more offspring individuals, so as to expand the population size. In the contraction operation, only individuals with high fitness will be selected to survive through a merge sorting algorithm, resulting in a decrease in population size to the initial value. Through the iterative computing of the two main operations, the proposed artificial Physarum polycephalum colony can finally find the optimal solution to the objective function. Third, a benchmark test is designed for the electric location-routing problem by extracting the real road network from Tokyo, and the experimental results prove the effectiveness and applicability of this work.

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Section: Literature Reviewmentioning

confidence: 99%

An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem

Cai,

Wang,

et al. 2023

Sustainability

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“…Implementing the cross-dock strategy enhances transportation efficiency and cost-effectiveness without the overheads associated with traditional warehousing systems. Also, cross-docking reduces lead times and can impact improving customer order delivery time [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Two Lagrangian relaxation algorithms were proposed in [19] to define a reliable cross-dock network even in situations of disruptions. Meidute-Kavaliauskiene et al [5] focused on several problems regarding the cross-docks. Namely, the authors proposed an algorithm based on the implementation of the non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) for targeting the following three issues: cost optimization, scheduling incoming and outgoing vehicles, and green supply chains (SC) consisting of several cross-docks.…”

mentioning

confidence: 99%

Optimizing Cross-Dock Terminal Location Selection: A Multi-Step Approach Based on CI-DEA–IDOCRIW–MABAC for Enhanced Supply Chain Efficiency—A Case Study

Wang,

Wen,

Pajić

et al. 2024

Mathematics

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Thedistribution of products stands out as one of the pivotal activities for logistics companies in recent years, particularly in the aftermath of the COVID-19 pandemic and other geopolitical events. Intense competition compels companies to efficiently execute their logistical processes, with cross-docking emerging as a frequently applied solution. However, the location of cross-dock terminals in urban areas remains a problem insufficiently addressed in the literature, with a dearth of studies and models tackling this issue. This paper introduces a novel and innovative model for locating cross-dock terminals based on the CI-DEA–IDOCRIW–MABAC (Composite Indicators–Data Envelopment Analysis-Integrated Determination of Objective Criteria Weights–Multi-Attributive Border Approximation Area Comparison) methods. In the process of defining input indicators, the following three sources were utilized: relevant literature, practical insights from logistics experts, and the knowledge and experience of the authors. Eight inputs and three outputs were considered (the number of users in the observed channel; the area served by the channel; the average distance a vehicle travels in one delivery; the required number of vehicles; labor availability; competition; construction, and expansion possibilities; proximity to the main infrastructure and traffic facilities; the average number of deliveries; average delivered quantity; and service level). The model underwent testing in a case study analyzing nine distribution channels (areas within the observed urban zone). The results indicated that alternative A4 (in the southwest area) ranked the highest since it was the best-ranked in accordance with the most important criteria, suggesting that the terminal is best located in the southwest zone. The accuracy of the results was confirmed by company management. By developing a completely new model and addressing the identified gap in the literature, this paper provides unequivocal scientific contributions.

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Presenting an optimization model for multi cross-docking rescheduling location problem with metaheuristic algorithms

Sahebi,

Toufighi,

Azzavi

et al. 2023

OPSEARCH

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Optimizing Multi Cross-Docking Systems with a Multi-Objective Green Location Routing Problem Considering Carbon Emission and Energy Consumption

Cited by 9 publications

References 41 publications

An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem

An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem

Optimizing Cross-Dock Terminal Location Selection: A Multi-Step Approach Based on CI-DEA–IDOCRIW–MABAC for Enhanced Supply Chain Efficiency—A Case Study

Presenting an optimization model for multi cross-docking rescheduling location problem with metaheuristic algorithms

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