Details

Autor(en) / Beteiligte

• Amorim, Pedro
• Curcio, Eduardo
• Almada-Lobo, Bernardo
• Barbosa-Póvoa, Ana P.F.D.
• Grossmann, Ignacio E.

Titel

Supplier selection in the processed food industry under uncertainty

Ist Teil von

• European journal of operational research, 2016-08, Vol.252 (3), p.801-814

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2016

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •We present a stochastic programming model for supplier selection.•We incorporate uncertainty in the demand and in the supplier operation.•We propose an enhanced Benders decomposition algorithm for this problem.•Sourcing actions vary between using an integrated and a decoupled approach.•Solving a single master problem in the Benders decomposition is more efficient. 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.