NDLI: Modified Genetic Algorithm to Solve Time-varying Lot Sizes Economic Lot Scheduling Problem

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Modified Genetic Algorithm to Solve Time-varying Lot Sizes Economic Lot Scheduling Problem

Content Provider	Semantic Scholar
Author	Elvira, Bethany Satria, Yudi Eka Rusin, Dan Rahmi
Copyright Year	2014
Abstract	Economic Lot Scheduling Problem (ELSP) is the problem of scheduling several items on a single machine in order to meet the demand without any backorder, so as to minimize the total cost (sum of inventory holding cost and setup cost). In this problem, a product is made from combination of some types of items. The purpose of solving ELSP is to determine the duration of processing same item (called as run length or lot size) and determine the sequence of the lots (called as production sequence) that can minimize the total cost. One of the ELSP approaches is Time-varying Lot Sizes Approach, which is an approach which different lot sizes is possible for any item in the production sequence. There are three main steps to solve Time-varying Lot Sizes ELSP: (1) Determine the production frequency of each item; (2) Round-off the production frequency of each item; (3) Determine the production sequence which minimizes the total cost. Time-varying Lot Sizes ELSP is known as NP-hard problem and there are numerous research on heuristic algorithms to solve this problem. This paper proposes Modified Genetic Algorithm which is a modification of Hybrid Genetic Algorithm (3) and Two-Level Genetic Algorithm (Moon & Choi, 2002). Numerical experiments show that Modified Genetic Algorithm outperforms Dobson's heuristic (4) and has the same result with Hybrid Genetic Algorithm. Moreover, Modified Genetic Algorithm could have shorter computation time than Two-Level Genetic Algorithm because in Modified Genetic Algorithm there is only one level of Genetic Algorithm.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://core.ac.uk/download/pdf/33525023.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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