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 multi-period 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.