Optimal schedule on a single machine using various due date determination methods
Computers in Industry
Scheduling in job shops with machine breakdowns: an experimental study
Computers and Industrial Engineering
A very fast TS/SA algorithm for the job shop scheduling problem
Computers and Operations Research
Ant colony optimization combined with taboo search for the job shop scheduling problem
Computers and Operations Research
Robotics and Computer-Integrated Manufacturing
A hybrid genetic algorithm for no-wait job shop scheduling problems
Expert Systems with Applications: An International Journal
A variable neighborhood search for job shop scheduling with set-up times to minimize makespan
Future Generation Computer Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Computers and Industrial Engineering
A multi-objective particle swarm optimization for project selection problem
Expert Systems with Applications: An International Journal
Multi-objective scheduling of dynamic job shop using variable neighborhood search
Expert Systems with Applications: An International Journal
A multi-objective PSO for job-shop scheduling problems
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
A computer simulation model for job shop scheduling problems minimizing makespan
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
An efficient job-shop scheduling algorithm based on particle swarm optimization
Expert Systems with Applications: An International Journal
A high performing metaheuristic for job shop scheduling with sequence-dependent setup times
Applied Soft Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Hi-index | 12.05 |
This paper presents a new mathematical model for a bi-objective job shop scheduling problem with sequence-dependent setup times and ready times that minimizes the weighted mean flow time (F@?"w) and total penalties of tardiness and earliness (E/T). Obtaining an optimal solution for this complex problem especially in large-sized problem instances within reasonable computational time is cumbersome. Thus, we propose a new multi-objective Pareto archive particle swarm optimization (PSO) algorithm combined with genetic operators as variable neighborhood search (VNS). Furthermore, we use a character of scatter search (SS) to select new swarm in each iteration in order to find Pareto optimal solutions for the given problem. Some test problems are examined to validate the performance of the proposed Pareto archive PSO in terms of the solution quality and diversity level. In addition, the efficiency of the proposed Pareto archive PSO, based on various metrics, is compared with two prominent multi-objective evolutionary algorithms, namely NSGA-II and SPEA-II. Our computational results show the superiority of our proposed algorithm to the foregoing algorithms, especially for the large-sized problems.