Ordinal Optimization: Soft Computing for Hard Problems (International Series on Discrete Event Dynamic Systems)
Robotics and Computer-Integrated Manufacturing
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Multi-objective rule mining using a chaotic particle swarm optimization algorithm
Knowledge-Based Systems
Modular design of a hybrid genetic algorithm for a flexible job-shop scheduling problem
Knowledge-Based Systems
A hybrid particle swarm optimization approach for clustering and classification of datasets
Knowledge-Based Systems
Hybrid particle swarm optimization for flow shop scheduling with stochastic processing time
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Game team balancing by using particle swarm optimization
Knowledge-Based Systems
An efficient knowledge-based algorithm for the flexible job shop scheduling problem
Knowledge-Based Systems
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Real-world manufacturing systems are influenced by various random factors, which must be taken into consideration in order to obtain an effective schedule. However, compared with the extensive research on the deterministic model, the stochastic job shop scheduling problem (SJSSP) has not been sufficiently studied. In this paper, we propose a two-stage particle swarm optimization (PSO) algorithm for SJSSP with the objective of minimizing the expected total weighted tardiness. In the first-stage PSO, a performance estimate is used for quick evaluation of the solutions, and a local search procedure is embedded for accelerating the convergence to promising regions in the solution space. The second-stage PSO continues the search process, but applies a more accurate solution evaluation policy, i.e. the Monte Carlo simulation. In order to reduce the computational burden, the optimal computing budget allocation (OCBA) method is used in this stage. Finally, the computational results on different-scale test problems validate the effectiveness of the proposed approach.