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.

An RFID Data Matrices-Based Evaluation Method for Process Logistics State

At present, radio frequency identification (RFID) technology has been widely applied in manufacturing industry. How to use collected RFID data to effectively evaluate process logistics state is an urgent problem. Firstly, process logistics state model based on extended disjunctive graph was established. Secondly, configuration scheme of RFID readers/tags and production elements was proposed according to the time and space characteristics of process logistics. Then, process logistics state matrices including jobs, warehouses, buffers, machine tools, and vehicles were constructed. The real-time process logistics states can be deduced by operation of above data matrices. Finally, a case study was proposed to verify the feasibility of the proposed methods.