Evolutionary algorithm for stochastic job shop scheduling with random processing time

Abstract

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.