An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
A multi-objective PSO for job-shop scheduling problems
Expert Systems with Applications: An International Journal
Environmentally constrained economic dispatch using Pareto archive particle swarm optimisation
International Journal of Systems Science
Short Communication: An effective TPA-based algorithm for job-shop scheduling problem
Expert Systems with Applications: An International Journal
Fault diagnosis in assembly processes based on engineering-driven rules and PSOSAEN algorithm
Computers and Industrial Engineering
A hybrid particle swarm optimization algorithm for high-dimensional problems
Computers and Industrial Engineering
Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling
Applied Soft Computing
Inventory based two-objective job shop scheduling model and its hybrid genetic algorithm
Applied Soft Computing
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In this paper, we present a particle swarm optimization for multi-objective job shop scheduling problem. The objective is to simultaneously minimize makespan and total tardiness of jobs. By constructing the corresponding relation between real vector and the chromosome obtained by using priority rule-based representation method, job shop scheduling is converted into a continuous optimization problem. We then design a Pareto archive particle swarm optimization, in which the global best position selection is combined with the crowding measure-based archive maintenance. The proposed algorithm is evaluated on a set of benchmark problems and the computational results show that the proposed particle swarm optimization is capable of producing a number of high-quality Pareto optimal scheduling plans.