OPTIMASI BIAYA DISTRIBUSI RANTAI PASOK TIGA TINGKAT DENGAN MENGGUNAKAN ALGORITMA GENETIKA ADAPTIF DAN TERDISTRIBUSI

Supply chain management is critical in business area. The main core of supply chain management is the process of distribution. Distribution is the process to move and store goods ranging from the level of the supplier to the customer level in the supply chain. Optimal distribution will be the key to...

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Bibliographic Details
Main Authors: , ZULFAHMI INDRA, , Prof. Drs. Subanar , Ph.D.
Format: Thesis
Published: [Yogyakarta] : Universitas Gadjah Mada 2014
Subjects:
ETD
Description
Summary:Supply chain management is critical in business area. The main core of supply chain management is the process of distribution. Distribution is the process to move and store goods ranging from the level of the supplier to the customer level in the supply chain. Optimal distribution will be the key to the company's success in running a business, because the distribution process will directly impact on supply chain costs. One issue is the distribution of decision strategies in determining the allocation of the number of products that must be moved from the level of the supplier to the customer level. This study take optimization of three levels distribution from factory � supplier � distributor � retailer. The approach taken is adaptive and distributed genetic algorithm. Solution in the form of allocation of the number of products delivered at each level will be modeled as a chromosome. Genetic parameters such as the number of chromosomes in the population, crossover probability and adaptive mutation probability will change adaptively according to conditions on the population of that generation. This study used 3 sub-populations that exchange individuals at any time in accordance with the probability of migration. The results of research conducted 30 times for each value of the parameter genetic fusion showed that the lowest cost value obtained is 80.910, which occurs at the crossover probability 0,4, mutation probability 0,1, the probability of migration 0,1 and migration rate 0,1. This result has shown that adaptive and distributed genetic algorithm is better than stepping stone method that obtained 89.825.