Introducing the MCHF/OVRP/SDMP: Multicapacitated/Heterogeneous Fleet/Open Vehicle Routing Problems with Split Deliveries and Multiproducts

Abstract

In this paper, we analyze a real-world OVRP problem for a production company. Considering real-world constrains, we classify our problem as multicapacitated/heterogeneous fleet/open vehicle routing problem with split deliveries and multiproduct (MCHF/OVRP/SDMP) which is a novel classification of an OVRP. We have developed a mixed integer programming (MIP) model for the problem and generated test problems in different size (10–90 customers) considering real-world parameters. Although MIP is able to find optimal solutions of small size (10 customers) problems, when the number of customers increases, the problem gets harder to solve, and thus MIP could not find optimal solutions for problems that contain more than 10 customers. Moreover, MIP fails to find any feasible solution of large-scale problems (50–90 customers) within time limits (7200 seconds). Therefore, we have developed a genetic algorithm (GA) based solution approach for large-scale problems. The experimental results show that the GA based approach reaches successful solutions with 9.66% gap in 392.8 s on average instead of 7200 s for the problems that contain 10–50 customers. For large-scale problems (50–90 customers), GA reaches feasible solutions of problems within time limits. In conclusion, for the real-world applications, GA is preferable rather than MIP to reach feasible solutions in short time periods.

Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints

“…Khoukhi, Yaakoubi, Bojji and Bensouda (2019) implemented GA to solve pick-up and delivery in a hospital, where two-cut points crossover and two-opts mutation are integrated to ensure the exploration and diversity of the population. The stopping cost or the cost of overtime of the mission time is considered in Ayadi and Benadada (2013) and Eroglu, Gencosman, Cavdur and Ozmutlu (2014). Ayadi and Benadada (2013) used a local search algorithm after each newborn child generated by the crossover and mutation process to minimize the maximum overtime of vehicles and routing costs, while Eroglu et al (2014) proposed a hybrid genetic-local search algorithm applied to the fitness function calculation process to eliminate infeasible solutions.…”