Abstract
Supply-chain network design is a complex task because there are many decisions involved, and presently, global networks involve many actors and variables, for example, in the automotive, pharmaceutical, and electronics industries. This research addresses a supply-chain network design problem with four levels: suppliers, factories, warehouses, and customers. The problem considered decides on the number, locations, and capacities of factories and warehouses and the transportation between levels in the supply chain. The problem is modeled as a mixed-integer linear program. The main contribution of this work is the proposal of two matheuristic algorithms to solve the problem. Matheuristics are algorithms that combine exact methods and heuristics, attracting interest in the literature because of their fast execution and high-quality solutions. The matheuristics proposed to select the warehouses and their capacities following heuristic rules. Once the warehouses and their capacities are fixed, the algorithms solve reduced models using commercial optimization software. Medium and large instances were generated based on a procedure described in the literature. A comparison is made between the algorithms and the results obtained, solving the model with a time limit. The algorithms proposed are successful in obtaining better results for the largest instances in shorter execution times.
Highlights
An optimal supply chain is a fundamental part of any company’s success; a good design and administration represent a competitive advantage or even a requirement for market participation
Cantú et al [21] propose a matheuristic for the design of sustainable hydrogen supply chains using a multi-objective perspective, but the model is focused on a single product and transportation mode
The generated instances were resolved with the mixed-integer linear programming model programmed in OPL in the IBM ILOG CPLEX 12.8.0 IDE, and the case of the matheuristics, an application in C + + Concert Technology was programmed with the use of CPLEX
Summary
An optimal supply chain is a fundamental part of any company’s success; a good design and administration represent a competitive advantage or even a requirement for market participation. One way to address the decision-making problem in the design of supply chains has been to propose optimization models based on mathematical programming. The mixed-integer linear programming models that have been widely used in the optimization of the supply chain are mostly NP-hard [1], making it impossible to obtain optimal solutions in reasonable times for instances of size similar to those found in real problems. The main contribution of the work presented in this paper is to advance in the research of matheuritics, proposing two algorithms to solve a complex problem efficiently for supply-chain network design with good quality solutions in a reasonable time. The instances presented range from 100 to 200 clients, and the results of the mixed-integer linear programming model will be analyzed and compared with those of the matheuristic algorithms proposed.
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