考虑环境标准的混合动力汽车医疗物资配送和药物废物收集的人工蜂群算法

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-11-06 DOI:10.1108/jm2-04-2022-0100

Javad Behnamian, Z. Kiani

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引用次数: 0

摘要

目的:研究在现实条件下插电式混合动力汽车的医疗物资配送问题和药物废弃物收集问题。在本研究中，考虑替代能源和同时取货和交货分别导致温室气体排放和配送成本的减少。在这里，这个问题被建模为混合整数线性规划，目标函数为交通和能源消耗成本。GAMS用于小尺寸实例的模型求解。由于本研究的问题是NP-hard问题，不可能在合理的时间内解决实际规模的问题，因此本研究采用了人工蜂群算法。然后，将算法结果与最近在文献中提出的模拟退火算法进行比较。最后，对精确解和元启发式算法得到的结果进行了比较、分析和报告。结果表明，该人工蜂群算法具有良好的性能。本文研究了药物废弃物回收的医疗物资配送问题。本文的重点是插电式混合动力汽车同时取货和交付。这个问题是用环境标准来模拟的。将交通成本和能源消耗成本作为目标函数。

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An artificial bee colony algorithm for medical goods distribution and pharmacological waste collection by hybrid vehicles considering environmental criteria

Purpose This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively. Design/methodology/approach Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used. Findings Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance. Originality/value In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

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.