Abstract
This paper presents a model for solving a multiobjective vehicle routing problem with soft time-window constraints that specify the earliest and latest arrival times of customers. If a customer is serviced before the earliest specified arrival time, extra inventory costs are incurred. If the customer is serviced after the latest arrival time, penalty costs must be paid. Both the total transportation cost and the required fleet size are minimized in this model, which also accounts for the given capacity limitations of each vehicle. The total transportation cost consists of direct transportation costs, extra inventory costs, and penalty costs. This multiobjective optimization is solved by using a modified genetic algorithm approach. The output of the algorithm is a set of optimal solutions that represent the trade-off between total transportation cost and the fleet size required to service customers. The influential impact of these two factors is analyzed through the use of a case study.
Highlights
In a competitive environment, obtaining the maximum profit plays a key role in the success of an enterprise
This paper presents a model for solving a multiobjective vehicle routing problem with soft time-window constraints that specify the earliest and latest arrival times of customers
The transportation costs of logistics enterprises are influenced by the fixed costs and variable costs involved in the transportation process
Summary
In a competitive environment, obtaining the maximum profit plays a key role in the success of an enterprise. Alvarenga et al [8] proposed a robust heuristic approach to vehicle routing problems with time windows (VRPTW), using travel distance as the main objective through an efficient genetic algorithm and a set partitioning formulation. Ghoseiri and Ghannadpour [9] presented a new model and solution for multiobjective VRPTW using goal programming and genetic algorithm, in which decision makers specify optimistic aspiration levels to objectives and deviations from those aspirations are minimized They used a direct interpretation of VRPTW as a multiobjective problem, in which both total required fleet size and total traveling distance were minimized, while capacity and time-window constraints were secured. The objectives were to simultaneously minimize the number of vehicles and the total distance Their approach was based on an evolutionary algorithm and it aims to find a set of Pareto optimal solutions. Every customer on the route must be visited only once by one of the vehicles
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
