A Hybrid Algorithm Approach to Designing a Three-Echelon Supply Chain Network Model

Abstract

This study addresses a large-scale location–allocation problem in a three-echelon automotive supply chain comprising 382 suppliers, candidate distribution centers, and six assembly plants. The planning task is to redesign the inbound consolidation network while minimizing transportation and distribution center operating costs, enforcing a 480 km supplier-to-center service radius, and achieving at least 90% demand-weighted coverage. We formulate a mixed discrete-continuous model with supplier-to-center assignment, center location, throughput, and flow decisions. A feasibility-oriented hybrid algorithm uses a genetic algorithm as the main search engine, ant colony construction to seed solutions near the feasible region, adaptive mutation and simulated annealing to preserve exploration and refine elite solutions, and an online neural surrogate to avoid a subset of costly exact fitness evaluations. The design differs from a simple collection of metaheuristics: all components share one variable-length encoding, the same feasibility metrics, and periodic exact reevaluation of candidate solutions. Using the competition case data, the redesigned network reduces total cost by 27.0% relative to the six-center baseline, decreases the demand-weighted average supplier-to-center distance from 461.3 km to 53.0 km, lowers the maximum distance from 2807.22 km to 441.78 km, and raises coverage from 45.0% to 100%. Across ten independent runs, the hybrid method obtains a mean cost 10.3% below that of a standard genetic algorithm, with lower run-to-run dispersion. The results show that feasibility-aware initialization, adaptive search, and selective surrogate evaluation can support practical redesign of a strongly constrained, national-scale inbound logistics network. The directly attached reproducibility package provides the MATLAB implementation and the seven supplied input workbooks used by the reported model. The evidence is limited to one deterministic competition instance, a fixed cost schedule, and fixed-topology sensitivity calculations; generalization under demand uncertainty, facility disruption, and alternative road conditions remains to be tested.