Previously published as: Integrated Manufacturing Systems
Online from: 2004
Subject Area: Operations and Logistics Management
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|Title:||An advanced overlapping production planning model in manufacturing supply chain|
|Author(s):||Li-Chih Wang, (Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan, Republic of China), Hung-Lin Shih, (Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan, Republic of China)|
|Citation:||Li-Chih Wang, Hung-Lin Shih, (2011) "An advanced overlapping production planning model in manufacturing supply chain", Journal of Manufacturing Technology Management, Vol. 22 Iss: 7, pp.870 - 890|
|Keywords:||Finite capacity planning, Genetic algorithm (GA), Manufacturing industries, Overlapping production planning, Supply chain management, Supply chain planning|
|Article type:||Research paper|
|DOI:||10.1108/17410381111160942 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
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.
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