Modeling and solving a five-echelon location-inventory-routing problem for red meat supply chain Case study in Iran

Purpose In this paper, multi-period location-inventory-routing problem (LIRP) considering different vehicles with various capacities has been investigated for the supply chain of red meat. The purpose of this paper is to reduce variable and fixed costs of transportation and production, holding costs of red meat, costs of meeting livestock needs and refrigerator rents. Design/methodology/approach The considered supply chain network includes five echelons. Demand considered for each customer is approximated as deterministic using historical data. The modeling is performed on a real case. The presented model is a linear mixed-integer programming model. The considered model is solved using general algebraic modeling system (GAMS) software for data set of the real case. Findings A real-world case is solved using the proposed method. The obtained results have shown a reduction of 4.20 per cent in final price of red meat. Also, it was observed that if the time periods changed from month to week, the final cost of meat per kilogram would increase by 43.26 per cent. Originality/value This paper presents a five-echelon LIRP for the meat supply chain in which vehicles are considered heterogeneous. To evaluate the capability of the presented model, a real case is solved in Iran and its results are compared with the real conditions of a firm, and the rate of improvement is presented. Finally, the impact of the changed time period on the results of the solution is examined.

Automated summary

This paper investigates a multi-period location-inventory-routing problem (LIRP) in the red meat supply chain considering different vehicles with various capacities. The purpose is to reduce transportation and production costs, holding costs of red meat, livestock needs costs, and refrigerator rents. The study includes a five-echelon supply chain network, with demand for each customer approximated as deterministic. A real case is modeled using a linear mixed-integer programming model and solved using GAMS software. Results show a 4.20% reduction in final red meat price and a 43.26% increase in cost per kilogram if time periods change from month to week.