Decomposition heuristics for multiobjective problems. The Food bank network redesign case

Abstract

Large MO-MILP problems are often very hard to solve by exact methods. In this study we consider the Food bank network redesign (FBNR) problem to introduce three decompose-and-fix heuristics that successfully solve large real-life based instances of the problem. The FBNR problem is modeled as a multi-period, multi-product supply chain redesign problem that accounts for economic, environmental and social objectives in three distinct functions. Each new heuristic decomposes the FBNR problem into two MO-MILP problems, which are sequentially solved following the lexicographic concept. Decomposition observe the nature and terms of the decisions involved. The first MO-MILP problem concerns only the longer term decisions. After the corresponding decisions are fixed, the second MO-MILP problem referring to shorter term decisions is solved respecting the ranking of objectives followed for each solution obtained in the first problem. The difference among the heuristics lies in what is considered as longer or shorter term decisions. Besides solving large instances of the problem, which the exact method used could not do, CPU time savings generated by the heuristics range from 80% to 97% of the time required by the exact method. Compromise between the computational effort and the quality of solutions obtained by each heuristic is discussed. The solving methodology proposed in this study can be adapted to other large multiobjective problems.