Abstract
This article has studied a full truckload transportation problem in the context of an empty return scenario, particularly an order selection and vehicle routing problem with full truckload, multiple depots and time windows (SFTMDVRPTW). The aim is to develop a solution where a set of truck routes serves a subset of selected transportation demands from a number of full truckload orders to maximize the total profit obtained from those orders. Each truck route is a chain of selected demands to serve, originating at a departure point and terminating at an arriving point of trucks in a way that respects the constraints of availability and time windows. It is not mandatory to serve all orders, and only the profitable ones are selected. In this study, we have formulated the SFTMDVRPTW as a mixed-integer linear programming (MILP) model. Finally, Computational results are conducted on a new data set that contains thirty randomly generated problem instances ranging from 16 to 30 orders using the CPLEX software. The findings prove that our model has provided good solutions in a reasonable time.
Highlights
Transport has always been a pioneering sector in the economy of the market
We present a variant of the full truckload (FTL) transportation problem that is inspired by logistic transportation in the context of an empty return scenario, which is not treated in the literature
The seventh column represents the gap between UBMILP and LBMILP provided by CPLEX
Summary
Transport has always been a pioneering sector in the economy of the market. because of the progress of the globalization of international exchanges and the needs of carriers to meet the demands of the giant shippers, companies face everyday transport challenges with maximum efficiency of cost-competitive operations. The fundamental problem, TSP, permits to visit a set of customers with one single truck. It plans it's tour by finding the running sequence of clients at a minimal cost. The trucking company can serve its customers using a homogeneous fleet of trucks from more than one warehouse (multi-depot VRP, MDVRP) (Lahyani et al, 2019; Marín Moreno et al, 2019). These vehicles can be heterogeneous at distinct levels: the fixed vehicle utilization costs, loading capacity, variable operational costs, etc. It is not obligatory to visit all customers, and only profitable ones are served within a certain time period
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