Optimization of Multi-objective Job-shop Scheduling under Uncertain Environment

Abstract

Job-shop production often faces many uncertainties arising from the interaction between various resources. The schedule must be robust enough to ensure smooth production under the uncertain environment. In this paper, the multi-objective job-shop scheduling problem (JSP) is optimized based on the tradeoff between time, cost and robustness. Firstly, a multi-objective optimization model was constructed, and the tradeoffs between three objectives, namely, time, cost and robustness, were discussed in details. After that, a genetic algorithm (GA) coupling non-dominated ranking was designed to solve the multi-objective JSP. Finally, the proposed model was verified using an example of JSP and the objective tradeoffs were validated through sensitivity analysis. The research findings shed important new light on multi-objective JSP under various constraints.