SOLUSI OPTIMAL DENGAN PENDEKATAN MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM PADA SISTEM VENDOR MANAGED INVENTORY

• Main Authors: , nurul firdausi, , Prof. Subanar, Ph.D
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2012
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/97866/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=54538
• Institution: Universitas Gadjah Mada

This study presents an integrated model by combining the control decisions such as economic order quantity, safety stock and other inventory decisions into facility location model that is used to solve design problems of distribution networks. Approach to simultaneously model was developed to consider the stochastic demand, and risk assessment modeling. Multi-objective decision analysis is adopted to calculate the system performance including cost, consumer services, and flexibility. Accounting system provides a comprehensive system of supply chain management system with a traditional approach to system goals. Multi-objective integer linear programming optimization of multi-criteria evaluation count presented as a result of better computing. Multi-objective evolutionary algorithm is developed to consider the facility location and facility inventory control parameters to obtain the best results of several conflicting criteria. The results of the algorithm with 25 retail and 15 candidates Indomaret Distribution Center will be obtained a number of alternatives that can improve efficiency for the completion of a difficult problem. By using the MOEA of these trials can be analyzed that the effect of population size on all types of data to a stable value of the smallest to the largest population size. While the average population size in large data values will tend to be the minimum with increasing population size. Computing time is directly proportional to population size. While computing the size of a maximum size of generation computing time is proportional to the size of the maximum generation.