A New Efficient Genetic Algorithm-Taguchi-Based Approach for Multi-Period Inventory Routing Problem.

The inventory routing problem arises from the combination of the vehicle routing problem and the vendor-managed inventory problem. In this paper, we present a mathematical model and a novel genetic algorithm for solving the multiperiod inventory routing problem. The objective is to supply products to scattered customers within a given time horizon while managing customer inventories to avoid shortages and minimize total inventory and transportation costs. To represent solutions for this problem, we introduce a new chromosomal structure. This structure offers simplicity in encoding and decoding solutions, maintains feasibility after crossover and mutation operations, addresses both routing and inventory management in a single step, and consolidates information about each solution method comprehensively. The algorithm parameters, including crossover and mutation rates, population size, number of iterations, and selection pressure, are fine-tuned using the Taguchi method. To assess algorithm efficiency, we utilize standard instances from the literature. Our results demonstrate that the proposed algorithm performs favorably compared to previous approaches. [ABSTRACT FROM AUTHOR]