Optimization of the Location, Inventory and Routing of Capacity Vehicles with Interval Uncertainty

A traditional logistics decision model mainly studies the location decision of logistics distribution centers, storage inventory management, vehicle scheduling, and transportation routes. The logistics location-inventory-routing problem (LIRP) is an integrated optimization of the three problems—a comprehensive optimization problem for the whole logistics system. This review paper uses the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method to review the literature on LIRP systematically. A total of 112 LIRP-related studies published between 2010 and 2021 are reviewed and classified based on 10 abstract and citation databases. The classification includes four aspects: problem characteristics, demand data types, model-based solutions, and application fields. From this systematic review, a few observations are recorded. First, the most popular problems among researchers are the multi-period multi-product problem, the multi-echelon single-link problem, and the multi-depot multi-retailer problem. Based on the objective function, the minimization of total supply chain cost is the primary concern of the LIRP literature. Researchers also favor other problem characteristics such as multi-objective programming, inventory control replenishment policy, and a homogeneous fleet of vehicles. We found that stochastic data are a common factor in an uncertain environment and have broad coverage. When dealing with the LIRP, heuristic and metaheuristic algorithms are the most widely used solution methodologies in the literature. In the application field of LIRP, the perishable products logistics network is mentioned in most applications. Finally, we discuss and emphasize the challenges of and recommendations for future work. This paper provides a systematic review of the literature on LIRP based on the PRISMA method, which contributes vital support and valuable information for researchers interested in LIRP.

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“…In addition, Ref. [130] innovatively used the YALMIP toolbox algorithm, a free MATLAB toolbox developed by [142] in 2004, to solve the MIP model.…”

Section: Solution Methods Of Mip Modelmentioning

confidence: 99%

“…The model was run on real-world data using GAMS software. The model in [130] adopted a MILP model and a robust optimization method to optimize the problem under uncertainty conditions. In order to solve the model and demonstrate its feasibility, GAMS software was used, and the results from MATLAB software were compared.…”

Section: Solution Methods Of Milp Modelmentioning

confidence: 99%