Advances in Production Engineering & Management
Volume 18 | Number 1 | March 2023 | pp 19–31
https://doi.org/10.14743/apem2023.1.454
A matheuristic approach combining genetic algorithm and mixed integer linear programming model for production and distribution planning in the supply chain
Guzman, E.; Poler, R.; Andres, B.
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A B S T R A C T
A number of research studies have addressed supply chain planning from various perspectives (strategical, tactical, operational) and demonstrated the advantages of integrating both production and distribution planning (PDP). The globalisation of supply chains and the fourth industrial revolution (Industry 4.0) mean that companies must be more agile and resilient to adapt to volatile demand, and to improve their relation with customers and suppliers. Hence the growing interest in coordinating production-distribution processes in supply chains. To deal with the new market’s requirements and to adapt business processes to industry’s regulations and changing conditions, more efforts should be made towards new methods that optimise PDP processes. This paper proposes a matheuristic approach for solving the PDP problem. Given the complexity of this problem, combining a genetic algorithm and a mixed integer linear programming model is proposed. The matheuristic algorithm was tested using the Coin-OR Branch & Cut open-source solver. The computational outcomes revealed that the presented matheuristic algorithm may be used to solve real sized problems.
A R T I C L E I N F O
Keywords • Production and distribution planning; Supply chain; Matheuristic; Genetic algorithm; Mixed integer linear programming model
Corresponding author • Guzman, E.
Article history • Received 29 April 2022, Revised 21 December 2022, Accepted 27 December 2022
Published on-line • 29 March 2023
E X P O R T C I T A T I O N
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