A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain

This study considers the scheduling of products and vehicles in a two-stage supply chain environment. The first stage contains m suppliers with different production speeds, while the second stage is composed of l vehicles, each of which may have a different speed and different transport capacity. In addition, it is assumed that the various output products occupy different percentages of each vehicle’s capacity. We model the situation as a mixed integer programming problem, and, to solve it, we propose a gendered genetic algorithm (GGA) that considers two different chromosomes with non-equivalent structures. Our experimental results show that GGA offers better performance than standard genetic algorithms that feature a unique chromosomal structure. In addition, we compare the GGA performance with that of the most similar problem reported to date in the literature as proposed by Chang and Lee [Chang, Y., & Lee, C. (2004). Machine scheduling with job delivery coordination. European Journal of Operational Research, 158(2), 470–487]. The experimental results from our comparisons illustrate the promising potential of the new GGA approach.