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.
Highlights
Open vehicle routing problems (OVRPs) have gained much attention recently since they represent a problem type that needs to be solved by many production companies
In order to evaluate the performance of mixed integer programming (MIP) and genetic algorithm (GA), we use the real-world data which are supplied from the 3PL provider
The gap% for the MIP models is calculated by the branch and bound algorithm in CPLEX 12.4 as in (11), where the best solution is represented by z and the best integer solution is represented by z∗
Summary
Open vehicle routing problems (OVRPs) have gained much attention recently since they represent a problem type that needs to be solved by many production companies. Companies choose to use a hired vehicle fleet for distributing their goods In this way, they do not have to endure the extra cost for returning vehicles since they use the resources of a third-party logistics (3PL) provider such as trucks or TIRs [1]. They do not have to endure the extra cost for returning vehicles since they use the resources of a third-party logistics (3PL) provider such as trucks or TIRs [1] As consequences of such benefits, the companies have to accept some restrictions defined by the 3PL providers. The 3PL provider can restrict the number of customers visited by a particular vehicle Such specifications require some changes in the classical OVRP structure to make them more applicable for real-world problems
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