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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Main Authors: Mohammad Alaghebandha, Bahman Naderi, Mohammad Mohammadi
Format: Article
Language:English
Published: Islamic Azad University, Qazvin Branch 2019-01-01
Series:Journal of Optimization in Industrial Engineering
Subjects:
Online Access:http://www.qjie.ir/article_543808_0fc31a4e546e4653fa2fedd4cb00f620.pdf
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spelling 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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