Heuristic Algorithms: An Application to the Truck Loading Problem

Heuristic Algorithms: An Application to the Truck Loading Problem

Laura Cruz Reyes, Claudia Gómez Santillán, Marcela Quiroz, Adriana Alvim, Patricia Melin, Jorge Ruiz Vanoye, Vanesa Landero Najera
DOI: 10.4018/978-1-4666-0297-7.ch009
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This chapter approaches the Truck Loading Problem, which is formulated as a rich problem with the classic one dimensional Bin Packing Problem (BPP) and five variants. The literature review reveals that related work deals with three variants at the most. Besides, few efforts have been done to combine the Bin Packing Problem with the Vehicle Routing Problem. For the solution of this new Rich BPP a heuristic-deterministic algorithm, named DiPro, is proposed. It works together with a metaheuristic algorithm to plan routes, schedules and loads. The objective of the integrated problem, called RoSLoP, consists of optimizing the delivery process of bottled products in a real application. The experiments show the performance of three version of the Transportation System. The best version achieves a total demand satisfaction, an average saving of three vehicles and a reduction of the computational time from 3 hrs to two minutes regarding their manual solution. For the large scale the authors have develop a competitive genetic algorithm for BPP. As future work, it is intended integrate the approximation algorithm to the transportation system.
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Roslop: Routing, Scheduling And Loading Problem

Despite progress in developing solution methods for the transportation logistics, in recent decades most efforts have been aimed at extremely relaxed environments, partially addressing the overall problem. This simplification involves only a small number of the extensive range of constraints, and this partially covering real business situation, this due to the inherent complexity. This section describes RoSLoP, an integral model of a real transportation problem.

The transportation of bottle products formulated with RoSLoP was specified by our industrial partner and involves three tasks: routing, scheduling and loading. In RoSLoP three optimization objectives must be achieved: satisfy the demands of all clients, minimize the number of used vehicles and reduce the total time of the trip. Scheduling and Routing are modeled using VRP, while loading is formulated with BPP. Figure 1 shows RoSLoP and its relation with VRP and BPP.

Figure 1.

Optimization Problems involves in RoSLoP


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