Study on Sustainable Combined Location-Inventory-Routing Problem Based on Demand Forecasting

Tingting Ji (), Shoufeng Ji (), Yuanyuan Ji and Hongyu Liu
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Tingting Ji: School of Business Administration, Northeastern University, Shenyang 110167, China
Shoufeng Ji: School of Business Administration, Northeastern University, Shenyang 110167, China
Yuanyuan Ji: School of Business Administration, Northeastern University, Shenyang 110167, China
Hongyu Liu: School of Business Administration, Northeastern University, Shenyang 110167, China

Sustainability, 2022, vol. 14, issue 23, 1-21

Abstract: The sustainable combined location-inventory-routing problem (CLIRP) based on demand forecasting is studied in this paper. Based on the construction of a multi-stage demand forecasting model, five parts of total logistics costs: the costs of trunk transportation and regional transportation, the fixed costs of distribution center construction, the inventory holding costs, shortage costs, and salvage, are comprehensively considered. The existing CLIRP model does not consider the environmental influence. Thus, a sustainable CLIRP model considering carbon emission is established with minimum logistics costs and emission as the objective function. A heuristic algorithm gives the initial solution, and then a hybrid heuristic algorithm combining the tabu search algorithm with the simulated annealing algorithm is proposed to find the global near-optimal solution. Finally, a numerical example of a garment chain enterprise is given to illustrate the solving process of the model. The results show that using the proposed algorithm determines the optimal locations of RDCs, and the transportation routes with each region are obtained with the minimum total logistics costs and carbon emission. The model realizes the combination of location, inventory, and routing problems of the large garment enterprises and finally realizes the goal of optimizing the sustainable logistics distribution network of the garment industry, which verifies the effectiveness of the model. Moreover, a comparison is made to show the efficiency of the proposed algorithm; the results show that the proposed algorithm in this paper optimizes the route and selections of RDCs.

Keywords: demand forecasting; sustainable combined location-inventory-routing problem model; hybrid heuristic algorithm; global near-optimal solution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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