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Improvement to an Existing Multi-Lev...

Hasan, Rifat Bin,

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Improvement to an Existing Multi-Level Capacitated Lot Sizing Problem with Setup Carryover, Backlogging, and Emission Control /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improvement to an Existing Multi-Level Capacitated Lot Sizing Problem with Setup Carryover, Backlogging, and Emission Control // Rifat Bin Hasan.
作者:	Hasan, Rifat Bin,
面頁冊數:	1 electronic resource (108 pages)
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Industrial engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30524318
ISBN:	9798379695132

Improvement to an Existing Multi-Level Capacitated Lot Sizing Problem with Setup Carryover, Backlogging, and Emission Control /
Hasan, Rifat Bin,

Improvement to an Existing Multi-Level Capacitated Lot Sizing Problem with Setup Carryover, Backlogging, and Emission Control /Rifat Bin Hasan. - 1 electronic resource (108 pages)

Source: Masters Abstracts International, Volume: 84-12.

This paper presents a multi-level, multi-item, multi-period capacitated lot-sizing problem. The lot-sizing problem studies can obtain production quantities, setup decisions, and inventory levels in each period fulfilling the demand requirements with limited capacity resources, considering the Bill of Material (B.O.M.) structure while simultaneously minimizing the production, inventory, and machine setup costs. The paper proposes an exact solution to Chowdhury et al. (2018)'s developed model, which considers the backlogging cost, setup carryover & greenhouse gas emission control to its model complexity. The problem contemplates the Dantzig-Wolfe (D.W.) decomposition to decompose the multi-level capacitated problem into a single-item uncapacitated lot-sizing sub-problem. To avoid the infeasibilities of the weighted problem (W.P.), an artificial variable is introduced, and the Big-M method is employed in the D.W. decomposition to produce an always feasible master problem. In addition, Wagner & Whitin's forward recursion algorithm is also incorporated in the solution approach for both end and component items to provide the minimum cost production plan. Introducing artificial variables in the D.W. decomposition method is a novel approach to solving the MLCLSP model. A better performance was achieved regarding reduced computational time (reduced by 50%) and optimality gap (reduced by 97.3%) in comparison to Chowdhury et al. (2018)'s developed model.

English

ISBN: 9798379695132Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Artificial variable

Improvement to an Existing Multi-Level Capacitated Lot Sizing Problem with Setup Carryover, Backlogging, and Emission Control /
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520 $a This paper presents a multi-level, multi-item, multi-period capacitated lot-sizing problem. The lot-sizing problem studies can obtain production quantities, setup decisions, and inventory levels in each period fulfilling the demand requirements with limited capacity resources, considering the Bill of Material (B.O.M.) structure while simultaneously minimizing the production, inventory, and machine setup costs. The paper proposes an exact solution to Chowdhury et al. (2018)'s developed model, which considers the backlogging cost, setup carryover & greenhouse gas emission control to its model complexity. The problem contemplates the Dantzig-Wolfe (D.W.) decomposition to decompose the multi-level capacitated problem into a single-item uncapacitated lot-sizing sub-problem. To avoid the infeasibilities of the weighted problem (W.P.), an artificial variable is introduced, and the Big-M method is employed in the D.W. decomposition to produce an always feasible master problem. In addition, Wagner & Whitin's forward recursion algorithm is also incorporated in the solution approach for both end and component items to provide the minimum cost production plan. Introducing artificial variables in the D.W. decomposition method is a novel approach to solving the MLCLSP model. A better performance was achieved regarding reduced computational time (reduced by 50%) and optimality gap (reduced by 97.3%) in comparison to Chowdhury et al. (2018)'s developed model.
546 $a English
590 $a School code: 0115
650 4 $a Industrial engineering. $3 526216
650 4 $a Applied physics. $3 3343996
650 4 $a Materials science. $3 543314
653 $a Artificial variable
653 $a Capacitated lot sizing
653 $a Dantzig-Wolfe decomposition
653 $a Emission control
653 $a Multi-level capacitated lot-sizing
690 $a 0546
690 $a 0794
690 $a 0215
710 2 $a University of Windsor (Canada). $b INDUSTRIAL ENGINEERING (MASc). $e degree granting institution. $3 3823881
720 1 $a Azab, Ahmed $e degree supervisor.
720 1 $a Baki, Fazle $e degree supervisor.
773 0 $t Masters Abstracts International $g 84-12.
790 $a 0115
791 $a M.A.Sc.
792 $a 2023
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