Multi-Objective Artificial Bee Colony Algorithms and Chaotic-TOPSIS Method for Solving Flowshop Scheduling Problem and Decision Making

摘要

Retrieval of optimal solution(s) for a permutation flowshop scheduling problem within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criterions like makespan, total flowtime, earliness, tardiness, etc and obtaining a Pareto solution for final decision making. This paper remodels a discrete artificial bee colony algorithm for permutation flowshop scheduling problem executed through three different scenarios raised the analysis of time complexity measure. To enhance the search procedure, we have explored the alternative and combined use of two local search algorithms named as: iterated greedy search algorithm and iterated local search algorithm in our discrete artificial bee colony algorithm and the results are summarized with respect to completion time, mean weighted tardiness, and mean weighted earliness. The two algorithms are prioritised on insertion and swap of neighbourhood structures which will intensify the local optima in the search space. Further the performance of the algorithm is compared with the test results of multi-objective artificial bee colony algorithm. The result of our optimization process concludes with a set of non-dominated solutions lead to different Pareto fronts. Finally, we propose a chaotic based technique for order of preference by similarity to ideal solution (chaotic-TOPSIS) using a suitable chaotic map for criteria adaptation in order to enhance the decision accuracy in the multi-objective problem domain. Retrieval of optimal solution(s) for a permutation flowshop scheduling problem within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criterions like makespan, total flowtime, earliness, tardiness, etc and obtaining a Pareto solution for final decision making. This paper remodels a discrete artificial bee colony algorithm for permutation flowshop scheduling problem executed through three different scenarios raised the analysis of time complexity measure. To enhance the search procedure, we have explored the alternative and combined use of two local search algorithms named as: iterated greedy search algorithm and iterated local search algorithm in our discrete artificial bee colony algorithm and the results are summarized with respect to completion time, mean weighted tardiness, and mean weighted earliness. The two algorithms are prioritised on insertion and swap of neighbourhood structures which will intensify the local optima in the search space. Further the performance of the algorithm is compared with the test results of multi-objective artificial bee colony algorithm. The result of our optimization process concludes with a set of non-dominated solutions lead to different Pareto fronts. Finally, we propose a chaotic based technique for order of preference by similarity to ideal solution (chaotic-TOPSIS) using a suitable chaotic map for criteria adaptation in order to enhance the decision accuracy in the multi-objective problem domain.