A mixed integer nonlinear multiperiod model for supply chain management of a company in the retail sector

Author:

Teixeira AnaORCID,Costa e Silva ElianaORCID,Lopes CristinaORCID

Abstract

The fluctuations in the business environment and seasonal variations characteristic of food supply chains contribute greatly to the increasing complexity of the entire Supply Chain planning. In the present paper, quantitative models are applied to support the decision-making purchasing management department of a retail company. Specifically, a multiperiod mathematical model was developed with the aim of optimizing decision-making of the purchasing managers. The developed model consists of a multiperiod Mixed Integer Nonlinear Programming model, with the objective to minimize the ratio between how much is costing the company to move the products along the Supply Chain and the products’ costs. It is discussed how to order the product, what is the most advantageous storage mode and whether it is preferable to order once or twice a week. Real instances, provided by a Portuguese retail company, regarding the demand for one year are tested for two scenarii, which are used currently by the company. The results show that the proposed model can reduce, on both scenarios, the ratio between operational costs and merchandise costs, for almost all products, and therefore it can be an important tool for supporting decision-making of the purchasing manager.

Funder

FCT - Fundação para a Ciência e Tecnologia

Publisher

EDP Sciences

Subject

Management Science and Operations Research,Computer Science Applications,Theoretical Computer Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimisation of Timber Supply Chains: Mathematical Model and Analysis of Regional Sources Using the Example of Primorsky Region;World of Transport and Transportation;2024-04-17

2. Evaluation of E-commerce Supply Chain Cost Management Based on Big Data Intelligent Platform Processing;Lecture Notes on Data Engineering and Communications Technologies;2024

3. Optimizing seasonal grain intakes with non-linear programming: An application in the feed industry;An International Journal of Optimization and Control: Theories & Applications (IJOCTA);2022-06-12

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