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An advanced overlapping production planning model in manufacturing supply chain

An advanced overlapping production planning model in manufacturing supply chain Purpose – The purpose of this paper is to develop a new approach called advanced overlapping production planning (AOPP) model which considers multi‐site process selection, sequential constraints, and capacity constraints in a manufacturing supply chain environment (MSCE). AOPP model may determine the capacity plan and order margin allocation for each site and machines in an MSCE and provide the capacity information for a production planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources. Design/methodology/approach – First, an AOPP model is presented to model the production scheduling problem in a supply chain with the objective of minimizing the fulfilling cycle time of each order and the overloads of each machine group. Second, a genetic algorithm (GA)‐based approach for solving the AOPP model is developed. Finally, a heuristic adjustment approach is proposed for planners to adjust the production plan whenever there is an exception of production occurring. Findings – The production schedule obtained from the GA‐based AOPP approach retains order margins in each operation against other overlapping operations, and it satisfies the capacity constraints of each machine group in an MSCE and results in a better performance in process planning and production planning with finite capacity. In practice, the overloading problem can be solved by adding a work shift or working overtime. The GA‐based AOPP model provides useful information for production planners to make such decisions. Practical implications – Production planners need a more flexible production plan with order margins to compensate for the uncertainties which frequently occur in the supply and demand sides. This research develops a model to help planners manage the order margin of production planning in an MSCE and showing that order margins become a crucial factor for achieving effective production objective in terms of short OTD (or order cycle) time. Originality/value – The overlapping production planning approach is a useful finite capacity planning approach for handling the capacity and order margin management in certain manufacturing environment (e.g. flow shop), but less on overcoming multi‐site process selection, sequential constraints, and capacity constraints in an MSCE. In addition, the capacity plan and order margin allocation information for each site and facilities are very important for a planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources. This research addresses both issues. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Manufacturing Technology Management Emerald Publishing

An advanced overlapping production planning model in manufacturing supply chain

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References (18)

Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
1741-038X
DOI
10.1108/17410381111160942
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to develop a new approach called advanced overlapping production planning (AOPP) model which considers multi‐site process selection, sequential constraints, and capacity constraints in a manufacturing supply chain environment (MSCE). AOPP model may determine the capacity plan and order margin allocation for each site and machines in an MSCE and provide the capacity information for a production planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources. Design/methodology/approach – First, an AOPP model is presented to model the production scheduling problem in a supply chain with the objective of minimizing the fulfilling cycle time of each order and the overloads of each machine group. Second, a genetic algorithm (GA)‐based approach for solving the AOPP model is developed. Finally, a heuristic adjustment approach is proposed for planners to adjust the production plan whenever there is an exception of production occurring. Findings – The production schedule obtained from the GA‐based AOPP approach retains order margins in each operation against other overlapping operations, and it satisfies the capacity constraints of each machine group in an MSCE and results in a better performance in process planning and production planning with finite capacity. In practice, the overloading problem can be solved by adding a work shift or working overtime. The GA‐based AOPP model provides useful information for production planners to make such decisions. Practical implications – Production planners need a more flexible production plan with order margins to compensate for the uncertainties which frequently occur in the supply and demand sides. This research develops a model to help planners manage the order margin of production planning in an MSCE and showing that order margins become a crucial factor for achieving effective production objective in terms of short OTD (or order cycle) time. Originality/value – The overlapping production planning approach is a useful finite capacity planning approach for handling the capacity and order margin management in certain manufacturing environment (e.g. flow shop), but less on overcoming multi‐site process selection, sequential constraints, and capacity constraints in an MSCE. In addition, the capacity plan and order margin allocation information for each site and facilities are very important for a planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources. This research addresses both issues.

Journal

Journal of Manufacturing Technology ManagementEmerald Publishing

Published: Sep 11, 2011

Keywords: Supply chain planning; Genetic algorithm (GA); Overlapping production planning; Finite capacity planning; Supply chain management; Manufacturing industries

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