A META-HEURISTIC APPROACH FOR IPPS PROBLEM

In this paper, we focused on the Integrated Process Planning and Scheduling (IPPS) which was an example of job-shop scheduling problem. Several approaches were proposed to solve this problem and Ant Colony Optimization (ACO) was one of the widely used approaches. Examining the articles in which ACO algorithm was described and applied to the IPPS problem gave us an insight of current performance of optimization algorithms to this problem. We then proposed a Genetic Algorithm (GA) for the problem and implemented both algorithms, ACO and GA, in Javascript. According to the results, increasing the running time of GA leaded to more optimal results than ACO. In addition, GA found better results compared to ACO in small-scale problems. On the other hand, ACO performed better than GA in limited time or in bigger problems. In this paper, we proposed a GA approach for IPPS problems. Our chromosome model had 2 parts; first part represented machines of processes and second part showed the orders of the jobs. We applied different mutation/crossover types to these parts and then determined better parameters with numerous experiences. Also, we created an iOS application for visually comparing this GA approach with an ACO algorithm previously proposed. Our GA approach gave better results in some problem types. Our application could be downloaded in the following link (iPad was recommended): https://itunes.apple.com/co/app/ipps-solver/id876097527? l=en&mt=8