Integer programming models for mid-term production planning for high-tech low-volume supply chains

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This paper studies the mid-term production planning of high-tech low-volume industries. Mid-term production planning (6 to 24 months) allocates the capacity of production resources to different products over time and coordinates the associated inventories and material inputs so that known or predicted demand is met in the best possible manner. High-tech low-volume industries can be characterized by the limited production quantities and the complexity of the supply chain. To model this, we introduce a mixed integer linear programming model that can handle general supply chains and production processes that require multiple resources. Furthermore, it supports semi-flexible capacity constraints and multiple production modes. Because of the integer production variables, size of realistic instances and complexity of the model, this model is not easily solved by a commercial solver. Applying Benders’ decomposition results in alternative capacity constraints and a second formulation of the problem. Where the first formulation assigns resources explicitly to release orders, the second formulation assures that the available capacity in any subset of the planning horizon is sufficient. Since the number of alternative capacity constraints is exponential, we first solve the second formulation without capacity constraints. Each time an incumbent is found during the branch and bound process a maximum flow problem is used to find missing constraints. If a missing constraint is found it is added and the branch and bound process is restarted. Results from a realistic test case show that utilizing this algorithm to solve the second formulation is significantly faster than solving the first formulation.

LanguageEnglish
Pages984-997
Number of pages14
JournalEuropean Journal of Operational Research
Volume269
Issue number3
Early online date5 Apr 2018
DOIs
StatePublished - 16 Sep 2018

Fingerprint

Production Planning
Integer programming
Integer Programming
Supply Chain
Supply chains
Programming Model
Capacity Constraints
Planning
Formulation
Branch-and-bound
Resources
Industry
Benders Decomposition
Maximum Flow
Mixed Integer Linear Programming
Alternatives
Assign
Production planning
Supply chain
High-tech

Keywords

  • Branch and bound
  • Integer programming
  • Production

Cite this

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title = "Integer programming models for mid-term production planning for high-tech low-volume supply chains",
abstract = "This paper studies the mid-term production planning of high-tech low-volume industries. Mid-term production planning (6 to 24 months) allocates the capacity of production resources to different products over time and coordinates the associated inventories and material inputs so that known or predicted demand is met in the best possible manner. High-tech low-volume industries can be characterized by the limited production quantities and the complexity of the supply chain. To model this, we introduce a mixed integer linear programming model that can handle general supply chains and production processes that require multiple resources. Furthermore, it supports semi-flexible capacity constraints and multiple production modes. Because of the integer production variables, size of realistic instances and complexity of the model, this model is not easily solved by a commercial solver. Applying Benders’ decomposition results in alternative capacity constraints and a second formulation of the problem. Where the first formulation assigns resources explicitly to release orders, the second formulation assures that the available capacity in any subset of the planning horizon is sufficient. Since the number of alternative capacity constraints is exponential, we first solve the second formulation without capacity constraints. Each time an incumbent is found during the branch and bound process a maximum flow problem is used to find missing constraints. If a missing constraint is found it is added and the branch and bound process is restarted. Results from a realistic test case show that utilizing this algorithm to solve the second formulation is significantly faster than solving the first formulation.",
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Integer programming models for mid-term production planning for high-tech low-volume supply chains. / de Kruijff, J.T.; Hurkens, C.A.J.; de Kok, A.G.

In: European Journal of Operational Research, Vol. 269, No. 3, 16.09.2018, p. 984-997.

Research output: Contribution to journalArticleAcademicpeer-review

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