Gaussian Matheuristics Optimization Model For Aggregate Production Planning Under Fuzzy Stochastic Environment: A Case Study Of Abc Company

Abstract

The aim of the study is to investigate the aggregate production planning (APP) problem with fuzzy stochastic demand. A novel fuzzy interval representation, Gaussian process, and chance-constraint programing are employed to transform the fuzzy stochastic APP problem into an equivalent Mixed Integer Linear Programming (MILP) model. Matheuristics are proposed as the solution approach, utilizing a Genetic Algorithm for the single-objective problem and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for the multi-objective problem. Large-scale computational experiments have also been conducted to demonstrate that the proposed method not only yields promising results to both single and multi-objective MILP approaches but also significantly reduces computation time. In real life application, a case study of ABC company is also employed to show the effectiveness of the proposed approach.