Using Guided Initial Chromosome of Genetic Algorithm for Scheduling Production-Distribution System

Abstract

Production and distribution system in a company should be managed carefully. Delay in product delivery not only results in a late penalty due to customer dissatisfaction or breach of contract, but also causes a supply chain failure. Of course, all these impacts will also reduce the reputation of a company. Scheduling integrated production-distribution is classified as NP-Hard problem. Genetic algorithm can be used to solve complex problem. In this paper, genetic algorithm is used for scheduling production-distribution in make to order system where each job has a different deadline and volume (size). This problem is represented on mixed integer programing model. We verify the genetic algorithm’s performance by comparing the results with the total cost calculated by lower bounds of the problems. Experiments show that the traditional initial random cannot produce good result with more than 15 job size problem. We proposed guided initial chromosome to tackle this problem. From further experiments shows that the proposed method approach can increase the performances of genetic algorithm in more than 15 job size problem. In general, proposed genetic algorithm with guided initial chromosome shows better solution quality and better time efficiency compared to previous related research.

Algoritma Penanganan Constraint pada Persoalan Penjadwalan Perkuliahan Universitas di Lingkungan Pendidikan Tinggi Keagamaan Islam (PTKI)

“…Kegunaan lain dari algoritma yang dipaparkan dalam paper ini adalah bahwa berbagai algoritma tersebut dapat digunakan untuk mendapatkan nilai objektif bila persoalan penjadwalan perkuliahan ini diselesaikan dengan menggunakan pendekatan meta-heuristik [9] [10] atau berbasis riset operasi [11] [12]. Metode meta-heuristik banyak digunakan untuk berbagai masalah penjadwalan karena kemampuanya menanganai constraint yang kompleks [13] [14]. Misalnya untuk mengetahui berapa banyak pelanggaran constraint ruang yang terjadi pada sebuah solusi yang diperoleh dengan algoritma genetika atau simulated annealing.…”

Application Research of Soft Computing Based on Machine Learning Production Scheduling

“…Based on the characteristics of these genetic algorithms, the scope is expanding, and research on the optimization and improvement of genetic algorithms is becoming more sophisticated. In recent years, production planning based on genetic algorithms has been analyzed in the literature using various software [44][45][46][47][48][49][50][51][52][53][54]. Our article contributes to the existing literature in that we did not use existing software.…”

An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II

“…A permutation chromosome representation employed to solve multiple sequences of alignment of bioinformatics [8]. NSGA-II with a new problem-specific chromosome makes significant performance improvement in oil refinery scheduling [9] and production-distribution optimization [10]. Better performance also produced by problem-specific chromosome on the base station placement in cellular networks [11].…”