具有模糊价格的共生供应链中新型回购协调合同的多目标数学模型:一种数据驱动的决策方法

IF 1.8 Q3 MANAGEMENT
Mobina Belghand, Amirhosein Asadi, Mohammad Alipour-Vaezi, Fariborz Jolai, Amir Aghsami
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引用次数: 0

摘要

本研究的目的是在共生供应链中制定一种新的回购协调合同。本文通过提出一种新的回购协调合同,鼓励制造商以最优方式订购产品,作为回报,供应商承诺以回购价格从制造商处购买未售出的产品。通过使用数据驱动决策和多目标决策,并考虑到共生行业的现有条件,本文提出了一种能保证供应链成员利润的合同。研究结果在本文中,作者发现了如何在共生供应链中确定订货量、回购价格和批发价格,从而使供应商和制造商都能获利。据作者所知,这是第一篇通过考虑不确定需求和多目标模型,在共生供应链中定义新的回购协调合同的论文。由于环境问题的重要性、公司和组织之间资源共享以及合作的必要性,各行业都在向共生产业发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multiobjective mathematical model for a novel buy-back coordination contract in the symbiotic supply chain with fuzzy price: a data-driven decision approach

Purpose

The purpose of this study is developing a new buy-back coordination contract in the symbiotic supply chain. In this new contract, the goal of the supply chain members (profit maximization) is realized.

Design/methodology/approach

This paper encourages the manufacturer to order products optimally by presenting a new buy-back coordination contract, and in return, the supplier undertakes to buy the unsold products from the manufacturer at the buy-back price. By using data-driven decision-making and multiobjective decision-making and considering the existing conditions in the symbiosis industry, a contract has been presented that guarantees the profits of supply chain members.

Findings

In this paper, it was found out how the authors can determine the order quantity, buy-back price and wholesale price in a symbiotic supply chain in such a way that it makes a profit for both the supplier and the manufacturer. In other words, how to determine these variables to encourage the manufacturer to order more quantity to the supplier so that both will benefit.

Originality/value

To the best of the authors’ knowledge, this is the first paper that defines a new buy-back coordination contract in the symbiotic supply chain by considering uncertain demand and a multiobjective model. Due to the importance of environmental issues, the sharing of resources by companies and organizations with each other, and the necessity of their cooperation, industries are moving toward a symbiosis industry.

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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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