A Multi-Objective Optimization Model for Production and Transportation Planning in a Marine Shrimp Farming Supply Chain Network

Chaimongkol Limpianchob Masahiro Sasabe Shoji Kasahara

In International Journal of Operational Research, 2022

Abstract

The traditional operation of marine shrimp farming is widely practiced in Southeast Asia. Giant freshwater prawn farming is one of the main types of marine shrimp farming that also still operates traditionally. Many of these farms operate without advanced techniques for production planning, inventory control, and transportation strategic decisions throughout the supply chain network which are among the most important managerial activities in commercial farming. Maintaining product freshness is of vital importance for aquaculture product. Therefore, this paper develops a multi-objective mixed-integer linear programming model for a marine shrimp farming supply chain network design problem. The problem is to plan production and control inventory according to constraints while maximize total profit surplus and minimize shortest route. A multi-echelon, multi-facility, and multi-period mathematical model is proposed such that real conditions are considered. In the end, some numerical illustrations are provided to show the proper Pareto solutions considering all of the objectives for the decision maker.

Downloads

Text Reference

Chaimongkol Limpianchob, Masahiro Sasabe, Shoji Kasahara, A Multi-Objective Optimization Model for Production and Transportation Planning in a Marine Shrimp Farming Supply Chain Network, International Journal of Operational Research, 45(1), pp.1-28, September 2022.

BibTex Reference

@article{limpianchob22MultiObjectiveOptimizationModel,
    author = "Limpianchob, Chaimongkol and Sasabe, Masahiro and Kasahara, Shoji",
    title = "A {{Multi-Objective Optimization Model}} for {{Production}} and {{Transportation Planning}} in a {{Marine Shrimp Farming Supply Chain Network}}",
    year = "2022",
    month = "September",
    journal = "International Journal of Operational Research",
    volume = "45",
    number = "1",
    pages = "1--28",
    doi = "10.1504/IJOR.2020.10034355"
}