Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Expert Systems with Applications: An International Journal
Ordinal Optimization and Quantification of Heuristic Designs
Discrete Event Dynamic Systems
Computers and Operations Research
A hybrid approach to large-scale job shop scheduling
Applied Intelligence
A simulation-optimization approach for integrated sourcing and inventory decisions
Computers and Operations Research
Computational Intelligence in Flow Shop and Job Shop Scheduling
Computational Intelligence in Flow Shop and Job Shop Scheduling
Job shop scheduling by pheromone approach in a dynamic environment
International Journal of Computer Integrated Manufacturing
Ordinal Optimization: Soft Optimization for Hard Problems
Ordinal Optimization: Soft Optimization for Hard Problems
Computers and Industrial Engineering
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In this paper, an evolutionary algorithm of embedding evolutionary strategy (ES) in ordinal optimization (OO), abbreviated as ESOO, is proposed to solve for a good enough schedule of stochastic job shop scheduling problem (SJSSP) with the objective of minimizing the expected sum of storage expenses and tardiness penalties using limited computation time. First, a rough model using stochastic simulation with short simulation length will be used as a fitness approximation in ES to select N roughly good schedules from search space. Next, starting from the selected N roughly good schedules we proceed with goal softening procedure to search for a good enough schedule. Finally, the proposed ESOO algorithm is applied to a SJSSP comprising 8 jobs on 8 machines with random processing time in truncated normal, uniform, and exponential distributions. The simulation test results obtained by the proposed approach were compared with five typical dispatching rules, and the results demonstrated that the obtaining good enough schedule is successful in the aspects of solution quality and computational efficiency.