Abstract

This paper presents a differential evolution approach for job shop scheduling problems. The job shop scheduling problem, a class of combinatorial optimization problem that involves discrete optimization over discrete variables and the most important objective is makes to pan minimization. The differential evolution approach is a stochastic based effective adaptive scheme for global optimization over continuous space. In order to apply the differential evolution algorithm for the job shop scheduling problem, a suitable encoding mechanism is required to generate an operation schedule. In the present work, the operation schedule is generated using random keys encoding scheme that deals with floating point vectors and a local search heuristic is used to achieve the best optimal solution. The proposed approach is extensively tested on a set of standard job shop scheduling instances and the results are compared with the best- known solutions. It is observed that the proposed approach is performing well on all the test problems

Authors and Affiliations