Logistics Distribution Path Optimization Using Simulated Annealing and Genetic Algorithms

This article provides a detailed exploration of logistics distribution path optimization using simulated annealing and genetic algorithms. First, let's examine the core concepts: Simulated annealing is an optimization technique that mimics the physical process of metal cooling and crystal formation, utilizing temperature variation simulations to escape local optima and approach global solutions. Genetic algorithms are evolutionary computation methods inspired by biological evolution, employing selection, crossover, and mutation operations to evolve solutions over generations. For logistics distribution path problems, these algorithms can determine optimal delivery routes to minimize both operational costs and time expenditures.

In practical implementation using simulated annealing, we typically begin by generating an initial solution set (e.g., random route sequences). The algorithm progressively refines solutions through an energy function (often representing total distance or cost) while controlling search intensity via a temperature parameter that gradually decreases according to a cooling schedule. A key implementation aspect is the acceptance probability function exp(-ΔE/T), which allows temporary acceptance of worse solutions to avoid local optima. For genetic algorithm implementation, we encode delivery routes as chromosomes (e.g., using permutation-based representations). The algorithm then performs crossover operations (like ordered crossover for path problems) and mutation operations (such as swap or inversion mutations) to generate new solutions. Fitness evaluation functions calculate route efficiency, while selection mechanisms (e.g., tournament selection) preserve high-quality solutions through iterative generations.

In conclusion, logistics distribution path optimization using simulated annealing and genetic algorithms presents a significant and challenging computational problem. Through strategic implementation of these algorithms - including proper parameter tuning, neighborhood structures for SA, and specialized genetic operators for path encoding - we can achieve optimized delivery routes that enhance logistics efficiency, reduce operational costs, and improve service quality. The hybrid approach often yields superior results by combining SA's local search capabilities with GA's global exploration characteristics.