Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes

This paper addresses the complex job shop scheduling problem with the consideration of non-identical job sizes. By simultaneously considering practical constraints of sequence dependent setup times, incompatible job families and job dependent batch processing time, we formulate this problem into a simulation optimization problem based on the disjunctive graph representation. In order to find scheduling policies that minimise the expectation of mean weighted tardiness, we propose a genetic programming based hyper heuristic to generate efficient dispatching rules. And then, based on the nested partition framework together with the optimal computing budget allocation technique, a hybrid rule selection algorithm is proposed for searching machine group specified rule combinations. Numerical results show that the proposed algorithms outperform benchmark algorithms in both solution quality and robustness.

Concept for the Analysis of Disruption Consequences in a Complex Job Shop Manufacturing

In this paper, an approach to quantify the effects of disruptions in a complex job shop manufacturing is introduced. Key figures within this approach are the effects of disruptions on logistic targets. With the help of discrete event simulation, the consequences of several disruptions are quantified and visualized.