Resource portfolio planning of make-to-stock products using a constraint programming-based genetic algorithm

The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expe...

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Published in:	Omega (Oxford) Vol. 35; no. 2; pp. 237 - 246
Main Authors:	Wang, S.M., Chen, J.C., Wang, K.-J.
Format:	Journal Article
Language:	English
Published:	Exeter Elsevier Ltd 01.04.2007 Elsevier Elsevier Science Publishers Pergamon Press Inc
Series:	Omega
Subjects:	Applied sciences Capacity planning Capacity planning Resource portfolio Genetic algorithm Capital assets Decision theory. Utility theory Exact sciences and technology Genetic algorithm Genetic algorithms High technology Inventory control Management Manufacturing Materials handling Mathematical programming Operational research and scientific management Operational research. Management science Portfolio theory Resource portfolio Studies Resource portfolio Genetic algorithm Capacity planning Backorder Procurement Multiple machine Make to order Decision making Resource allocation Linear programming Mixed integer programming Capital Long term Constrained optimization Innovation Stock exchange Budget Profit Revenue Production capacity Planning Make to stock Timing Investment
ISSN:	0305-0483, 1873-5274
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Abstract	The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiple-function machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently.
AbstractList	The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiplefunction machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently. The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiple-function machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently. The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiplefunction machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently. Keywords: Capacity planning; Resource portfolio; Genetic algorithm The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiple-function machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently. [PUBLICATION ABSTRACT]
Audience	Trade Academic
Author	Chen, J.C. Wang, S.M. Wang, K.-J.
Author_xml	– sequence: 1 givenname: S.M. surname: Wang fullname: Wang, S.M. organization: Department of Industrial Engineering, Chung-Yuan Christian University, Chung Li 320, Taiwan, ROC – sequence: 2 givenname: J.C. surname: Chen fullname: Chen, J.C. organization: Department of Industrial Engineering, Chung-Yuan Christian University, Chung Li 320, Taiwan, ROC – sequence: 3 givenname: K.-J. surname: Wang fullname: Wang, K.-J. email: kjwang@mail.ndhu.edu.tw organization: Department of Business Administration, National Dong Hwa University, Hualien 974, Taiwan, ROC
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Keywords	Resource portfolio Genetic algorithm Capacity planning Backorder Procurement Multiple machine Make to order Decision making Resource allocation Linear programming Mixed integer programming Capital Long term Constrained optimization Innovation Stock exchange Budget Profit Revenue Production capacity Planning Make to stock Timing Investment
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SubjectTerms	Applied sciences Capacity planning Capacity planning Resource portfolio Genetic algorithm Capital assets Decision theory. Utility theory Exact sciences and technology Genetic algorithm Genetic algorithms High technology Inventory control Management Manufacturing Materials handling Mathematical programming Operational research and scientific management Operational research. Management science Portfolio theory Resource portfolio Studies
Title	Resource portfolio planning of make-to-stock products using a constraint programming-based genetic algorithm
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