Exploring Heuristics for the Vehicle Routing Problem with Split Deliveries and Time Windows

This dissertation investigates the Vehicle Routing Problem with Split Deliveries and Time Windows. This problem assumes a depot of homogeneous vehicles and set of customers with deterministic demands requiring delivery. Split deliveries allow multiple visits to a customer and time windows restrict the time during which a delivery can be made. Several construction and local search heuristics are tested to determine their relative usefulness in generating solutions for this problem. This research shows a particular subset of the local search operators is particularly influential on solution quality and run time. Conversely, the construction heuristics tested do not significantly impact either. Several problem features are also investigated to determine their impact. Of the features explored, the ratio of customer demand to vehicle ratio revealed a significant impact on solution quality and influence on the effectiveness of the heuristics tested. Finally, this research introduces an ant colony metaheuristic coupled with a local search heuristic embedded within a dynamic program seeking to solve a Military Inventory Routing Problem with multiple-customer routes, stochastic supply, and deterministic demand. Also proposed is a suite of test problems for the Military Inventory Routing Problem

A robust enhancement to the Clarke-Wright savings algorithm

We address the Clarke and Wright (CW) savings algorithm proposed for the Capacitated Vehicle Routing Problem (CVRP). We first consider a recent enhancement which uses the put first larger items idea originally proposed for the bin packing problem and show that the conflicting idea of putting smaller items first has a comparable performance. Next, we propose a robust enhancement to the CW savings formulation. The proposed formulation is normalized to efficiently solve different problems, independent from the measurement units and parameter intervals. To test the performance of the proposed savings function, we conduct an extensive computational study on a large set of well-known instances from the literature. Our results show that the proposed savings function provides shorter distances in the majority of the instances and the average performance is significantly better than previously presented enhancements

SR-2: A Hybrid Algorithm for the Capacitated Vehicle Routing Problem

During the last decades a lot of work has been devoted to develop algorithms that can provide near-optimal solutions for the capacitated vehicle routing problem (CVRP). Most of these algorithms are designed to minimize an objective function, subject to a set of constraints, which typically represents aprioristic costs. This approach provides adequate theoretical solutions, but they do not always fit real-life needs since there are some important costs and some routing constraints or desirable properties that cannot be easily modeled. In this paper, we present a new approach which combines the use of Monte Carlo simulation and parallel and grid computing techniques to provide a set of alternative solutions to the CVRP. This allows the decision-maker to consider multiple solution characteristics other than just aprioristic costs. Therefore, our methodology offers more flexibility during the routing selection process, which may help to improve the quality of service offered to clients

Nuevas técnicas de construcción de rutas para el caso del Vehicle Routing Problem with Time Windows

[Abstract] In the last decades, the well-known problem of vehicle routing has derived into a number of different variants. One of these variants is the Vehicle Routing Problem with Time Windows. It is in 1987 when the bottom line of the different solutions techniques is established, since in that date, Solomon (1987) presents a state of the art research, as well as a number of benchmark problems. Since then, the development of solution techniques has increased exponentially, although most of these are based on the previous works of Solomon. In this paper we examine a number of new techniques in order to build up the routes from scratch, integrating dispersed customers into the routes the vehicles must conduct, and respecting at all times the capacity and time requirements restrictions. By using these new techniques we outperform previous methods in some of the benchmark problems, as well as the computation time needed

How good are distributed allocation algorithms for solving urban search and rescue problems? A comparative study with centralized algorithms

In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO

Sistemas de gestión de rutas de transportes: nuevas técnicas de solución

En este trabajo se analizan los procesos de planificación de rutas de transporte para resolver los problemas logísticos a nivel operativo. En los últimos años se han producido avances notables en diferentes campos tecnológicos y científicos que han permitido el desarrollo de potentes Sistemas de Información Logística. Los Sistemas de Posicionamiento Global o GPS, los Sistemas de Información Geográfica o GIS, y los avances conseguidos en los métodos de cálculo de rutas de transporte han permitido el desarrollo de avanzadas herramientas de software que automatizan las tareas de planificación de rutas de transporte. Todos estos logros han provocado que cada vez el entorno de las empresas se torne más dinámico y competitivo agudizando la necesidad de sistemas más avanzados y que consigan mayores reducciones de costes en todo el proceso de planificación. En este sentido se presenta en este trabajo un nuevo método para las primeras fases de construcción de rutas de transporte, en el problema conocido como Vehcicle Routing Problem with Time Windows, y que resumen en mayor o menor medida la problemática a la que se enfrentan las empresas que tienen esta problemática. Seguidamente se analizan las técnicas heurísticas más conocidas para resolver esta problemática, y partiendo de estos procedimientos se presenta un nuevo método para la fase inicial de construcción de rutas. En la tercera parte de este trabajo se presentan los resultados del método desarrollado, comparado con otros métodos recogidos en la literatura, y mostrando que se mejoran los resultados de algunos de los mismos

Ant Colony Optimization for the Electric Vehicle Routing Problem

Ant colony optimization (ACO) algorithms have proved to be powerful tools to solve difficult optimization problems. In this paper, ACO is applied to the electric vehicle routing problem (EVRP). New challenges arise with the consideration of electric vehicles instead of conventional vehicles because their energy level is affected by several uncertain factors. Therefore, a feasible route of an electric vehicle (EV) has to consider visit(s) to recharging station(s) during its daily operation (if needed). A look ahead strategy is incorporated into the proposed ACO for EVRP (ACO-EVRP) that estimates whether at any time EVs have within their range a recharging station. From the simulation results on several benchmark problems it is shown that the proposed ACO-EVRP approach is able to output feasible routes, in terms of energy, for a fleet of EVs