Supplier selection in the processed food industry under uncertainty

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Pedro Amorim (Author) FEUP; Eduardo Curcio (Author) FEUP; Bernardo Almada Lobo (Author) FEUP; Ana Barbosa-Póvoa (Author) Other; Ignacio Grossmann (Author) Other

Journal

Title: European Journal of Operational Research

Vol. 252

Pages: 801-814

ISSN: 0377-2217

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 49 Citations

ISI Web of Science

Scopus - 70 Citations

Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Associated Projects

WholeChain - Ferramenta Quantitativa para a Gestão da Cadeia de Abastecimento de Produtos Alimentares Perecíveis

Other information

Authenticus ID: P-00K-91N

DOI: 10.1016/j.ejor.2016.02.005

Resumo (PT): This paper addresses an integrated framework for deciding about the supplier selection in the processed food industry under uncertainty. The relevance of including tactical production and distribution planning in this procurement decision is assessed. The contribution of this paper is three-fold. Firstly, we propose a new two-stage stochastic mixed integer programming model for the supplier selection in the process food industry that maximizes profit and minimizes risk of low customer service. Secondly, we reiterate the importance of considering main complexities of food supply chain management such as: perishability of both raw materials and final products; uncertainty at both downstream and upstream parameters; and age dependent demand. Thirdly, we develop a solution method based on a multi-cut Benders decomposition and generalized disjunctive programming. Results indicate that sourcing and branding actions vary significantly between using an integrated and a decoupled approach. The proposed multi-cut Benders decomposition algorithm improved the solutions of the larger instances of this problem when compared with a classical Benders decomposition algorithm and with the solution of the monolithic model.

Abstract (EN): This paper addresses an integrated framework for deciding about the supplier selection in the processed food industry under uncertainty. The relevance of including tactical production and distribution planning in this procurement decision is assessed. The contribution of this paper is three-fold. Firstly, we propose a new two-stage stochastic mixed-integer programming model for the supplier selection in the process food industry that maximizes profit and minimizes risk of low customer service. Secondly, we reiterate the importance of considering main complexities of food supply chain management such as: perishability of both raw materials and final products; uncertainty at both downstream and upstream parameters; and age dependent demand. Thirdly, we develop a solution method based on a multi-cut Benders decomposition and generalized disjunctive programming. Results indicate that sourcing and branding actions vary significantly between using an integrated and a decoupled approach. The proposed multi-cut Benders decomposition algorithm improved the solutions of the larger instances of this problem when compared with a classical Benders decomposition algorithm and with the solution of the monolithic model.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14