A multi-objective optimization model of truck scheduling problem using cross-dock in supply chain management: NSGA-II and NRGA

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-02-05 DOI:10.1108/jm2-06-2023-0130
Ahsan Haghgoei, Alireza Irajpour, Nasser Hamidi
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Abstract

Purpose

This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors.

Design/methodology/approach

Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment.

Findings

Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods.

Originality/value

This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.

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供应链管理中使用交叉堆场的卡车调度问题的多目标优化模型:NSGA-II 和 NRGA
目的 本文旨在开发一个多目标问题,用于调度进入和离开交叉码头的卡车操作,在这种情况下,卡车卸载或装载产品的数量是模糊逻辑的。第一个目标函数使接收产品的最长时间最小化。第二个目标函数使卡车的排放成本最小化。最后,第三个目标函数使分配到入口和出口的卡车数量最小化。第一步,利用 GAMS 软件,通过最小目标函数以及遗传算法(GA)和粒子群优化,解决了两个小尺寸随机数值示例。在第二步中,由于问题的维度和计算复杂度不断增加,相关问题属于 NP-Hard 问题,因此使用了多目标元启发式算法,并进行了验证和参数调整。然后,根据评价标准得出的结果,采用 TOPSIS 方法对每个问题的算法进行排序。对结果的分析证实了所提模型和求解方法的适用性。 原创性/价值 本文针对一个实际问题提出了卡车调度数学模型,其中包括在供应链中扮演重要角色的交叉码头,因为它们可以减少订单交付时间、库存持有成本和运输成本。为了解决所提出的多目标数学模型,由于该问题具有 NP 难度,因此采用了多目标元启发式算法,并进行了验证和参数调整。因此,采用 NSGA-II 和 NRGA 来解决 30 个高维随机问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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