Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops
This paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assign...
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Islamic Azad University, Qazvin Branch
2019-01-01
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doaj-c7837c124efe458886a4105267d773ae2020-11-24T21:12:56ZengIslamic Azad University, Qazvin BranchJournal of Optimization in Industrial Engineering2251-99042423-39352019-01-0112110311710.22094/JOIE.2018.542997.1510 Economic Lot Sizing and Scheduling in Distributed Permutation Flow ShopsMohammad Alaghebandha0Bahman Naderi1Mohammad Mohammadi2Department of Industrial Engineering, Faculty of Engineering, Kharazmi University,Tehran, IranDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University,Tehran, IranDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, IranThis paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs and finished products inventory costs per unit of time. Since the proposed model is NP-hard, an efficient Water Cycle Algorithm is proposed to solve the model. To justify proposed WCA, Monarch Butterfly Optimization (MBO), Genetic Algorithm (GA) and combination of GA and simplex are utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed.http://www.qjie.ir/article_543808_0fc31a4e546e4653fa2fedd4cb00f620.pdfLot sizingDistributed permutation flow shopsLinearizationWater Cycle AlgorithmMonarch butterfly optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohammad Alaghebandha Bahman Naderi Mohammad Mohammadi |
spellingShingle |
Mohammad Alaghebandha Bahman Naderi Mohammad Mohammadi Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops Journal of Optimization in Industrial Engineering Lot sizing Distributed permutation flow shops Linearization Water Cycle Algorithm Monarch butterfly optimization |
author_facet |
Mohammad Alaghebandha Bahman Naderi Mohammad Mohammadi |
author_sort |
Mohammad Alaghebandha |
title |
Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops |
title_short |
Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops |
title_full |
Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops |
title_fullStr |
Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops |
title_full_unstemmed |
Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops |
title_sort |
economic lot sizing and scheduling in distributed permutation flow shops |
publisher |
Islamic Azad University, Qazvin Branch |
series |
Journal of Optimization in Industrial Engineering |
issn |
2251-9904 2423-3935 |
publishDate |
2019-01-01 |
description |
This paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs and finished products inventory costs per unit of time. Since the proposed model is NP-hard, an efficient Water Cycle Algorithm is proposed to solve the model. To justify proposed WCA, Monarch Butterfly Optimization (MBO), Genetic Algorithm (GA) and combination of GA and simplex are utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed. |
topic |
Lot sizing Distributed permutation flow shops Linearization Water Cycle Algorithm Monarch butterfly optimization |
url |
http://www.qjie.ir/article_543808_0fc31a4e546e4653fa2fedd4cb00f620.pdf |
work_keys_str_mv |
AT mohammadalaghebandha economiclotsizingandschedulingindistributedpermutationflowshops AT bahmannaderi economiclotsizingandschedulingindistributedpermutationflowshops AT mohammadmohammadi economiclotsizingandschedulingindistributedpermutationflowshops |
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1716749408373047296 |