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Hybrid simulation and MIP based heuristic algorithm for the production and distribution planning in the soft drink industry

Authors
Journal
Journal of Manufacturing Systems
0278-6125
Publisher
Elsevier
Volume
33
Issue
3
Identifiers
DOI: 10.1016/j.jmsy.2014.01.002
Keywords
  • Supply Chain Management
  • Production And Distribution Planning
  • Mixed Integer Linear Programming
  • Hybrid Approach
  • Simulation
  • Rolling Horizon Heuristics
Disciplines
  • Computer Science
  • Mathematics

Abstract

Abstract This study addresses the production and distribution planning problem in the soft drink industry. The problem involves the allocation of production volumes among the different production lines in the manufacturing plants, and the delivery of products to the distribution centers (DCs). A mixed integer linear programming (MILP) model is developed for the problem. In this paper, we present a hybrid solution methodology combining simulation and mixed integer programming (MIP) based Fixed and Optimize heuristic to solve the considered problem. First, MIP based Fix and Relax (F&R), Fix and Optimize (F&O) heuristics are proposed. The solution quality and performance of the proposed heuristics are analyzed with the randomly generated demand figures for the three granularity categories and various capacity load scenarios. Computational performances of these heuristic procedures are compared with the standard MIP results. The computational experiments carried out on a large set of instances have shown that the F&O heuristic algorithm provides good quality solutions in a reasonable amount of time. Second, simulation model is introduced to represent the problem with stochastic machine failures. Hybrid methodology combining the MIP based F&O heuristic and simulation model is implemented. The optimization model uses an F&O heuristic to determine the production and delivered quantity. Subsequently the simulation model is applied to capture the uncertainty in the production rate. Numerical studies from the data which have a tight production capacity and high demand granularity demonstrate that the developed hybrid approach is capable of solving real sized instance within a reasonable amount of time and demonstrate the applicability of the proposed approach.

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