Three Machines Flowshop Scheduling Model with Bicriterion Objective Function

Tyagi, Neelam; Tripathi, R. P.; Chandramouli, A. B.

doi:10.17485/ijst/2016/v9i48/103012

Cited by 3 publications

(1 citation statement)

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“…Rajendran [15] also presented a similar procedure along with two heuristic approaches for the 2-machine flowshop scheduling problem with two objectives: minimization of TFT subject to optimal makespan. Similarly two different methodologies (one is based on a Branch and Bound (B&B) technique of exact algorithms and other one is based on Palmer approach of heuristic algorithms) are used [16] to find the optimum solution for minimization of bi-criterion (makespan, weighted mean flowtime) objective function of three machines FSSP with transportation times and weight of the jobs. Recently a production scheduling problem in hybrid shops has been solved by Mousavi et al [17], by assuming some realistic assumptions.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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

Panda

Dehuri

Jagadev

2020

IJCAI

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Retrieval of optimal solution(s) for a Permutation Flow-Shop Scheduling Problem (PFSSP) within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criteria like makespan, total flowtime, earliness, tardiness, etc for obtaining a set of Pareto solutions in the process of Multi-Objective Optimization (MOO). This paper remodels a Discrete Artificial Bee Colony Algorithm (DABC) from a single objective optimization method to a multiobjective optimization one to solve the PFSSP executed and explored through the alternative and combined use of two local search algorithms named as: Iterated Greedy Search Algorithm (IGRS) and Iterated Local Search Algorithm (ILS). The algorithm has been classified into three different scenarios raised with the analysis of time complexity measure of applied local search methods prioritized through the insertion and swap operation of neighborhood structures that intensifies the local optima in the search space. The results of the DABC algorithm are summarized with respect to Total Completion Time (TCT), Mean Weighted Tardiness (MWT), and Mean Weighted Earliness (MWE). Based on the time complexity measure of the obtained results a Multi-Objective Artificial Bee Colony Algorithm (MOABC) has been proposed by adopting the simplest local search method of all in order to reflect the enhanced version of previously remodeled DABC algorithm. 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-Criteria Decision Making (MCDM) domain. Povzetek: Članek se ukvarja z NP problemom večkriterijske optimizacije izdelave urnika z imenom Permutation Flow-Shop Scheduling Problem (PFSSP). Uvede Multi-Objective Artificial Bee Colony Algorithm (MOABC), tj. več-kriterijski algoritem z umetno čebeljo kolonijo in pokaže izboljšane rezultate.

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Section: Introduction and Related Workmentioning

confidence: 99%