Two-stage Bio-inspired Optimization Algorithm for Stochastic Job Shop Scheduling Problem

The stochastic job shop scheduling problem (SJSSP) is a kind of stochastic programming problem which transformed from job shop scheduling problem. The SJSSP is an NP-hard problem. Current solutions for the SJSSP can be classified as analytic and heuristic. However, these two methods ignored characteristics of SJSSP, which lead to large computation times and inefficient solutions. In order to efficiently solve the SJSSP, a two-stage bio-inspired optimization algorithm is proposed to find a good enough schedule in a reasonable computation time. The proposed algorithm consists of exploration stage and exploitation stage. In exploration stage, we employ the ant colony system (ACS) to select N candidate solutions. In exploitation stage, we look for a good enough solution from the N candidate solutions with the optimal computing budget allocation (OCBA). First, the SJSSP is formulated as a constraint stochastic simulation optimization problem. Next, the proposed algorithm is used to find a good enough schedule of the SJSSP with the objective of minimizing the makespan using limited computation time. Finally, the proposed algorithm is applied to a SJSSP comprising 6 jobs on 6 machines with random processing time in truncated normal, uniform, and exponential distributions. Test results demonstrate that the obtaining good enough schedule is successful in the aspects of solution quality and computational efficiency. Keywordsstochastic job shop scheduling problem, ant colony system, ordinal optimization, extreme learning machine, optimal computing budget allocation.

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