A new method for generating initial solutions of capacitated vehicle routing problems

In vehicle routing problems, the initial solutions of the routes are important for improving the quality and solution time of the algorithm. For a better route construction algorithm, the obtained initial solutions must be basic, fast, and flexible with reasonable accuracy. In this study, initial solutions improvement for CVRP is introduced based on a method that is introduced in the literature. Using a different formula for addressing the gravitational forces, a new method is introduced and compared with the previous physics inspired algorithm. By using the initial solutions of the proposed method and using them as RTR and SA initial routes, it is seen that better results are obtained when compared with various algorithms from the literature. Also, in order to fairly compare the algorithms executed on different machines, a new comparison scale for the solution quality of vehicle routing problems is proposed that depends on the solution time and the deviation from the best known solution. The obtained initial solutions are then input to Record-to-Record and Simulated Annealing algorithms to obtain final solutions. Various test instances and CVRP solutions from the literature are used for comparison. The comparisons with the proposed method have shown promising results

Combining probabilistic algorithms, Constraint Programming and Lagrangian Relaxation to solve the vehicle routing problem

This paper presents an original hybrid approach to solve the Capacitated Vehicle Routing Problem (CVRP). The approach combines a Probabilistic Algorithm with Constraint Programming (CP) and Lagrangian Relaxation (LR). After introducing the CVRP and reviewing the existing literature on the topic, the paper proposes an approach based on a probabilistic Variable Neighbourhood Search (VNS) algorithm. Given a CVRP instance, this algorithm uses a randomized version of the classical Clarke and Wright Savings constructive heuristic to generate a starting solution. This starting solution is then improved through a local search process which combines: (a) LR to optimise each individual route, and (b) CP to quickly verify the feasibility of new proposed solutions. The efficiency of our approach is analysed after testing some well-known CVRP benchmarks. Benefits of our hybrid approach over already existing approaches are also discussed. In particular, the potential flexibility of our methodology is highlighted

Combining probabilistic algorithms, Constraint Programming and Lagrangian Relaxation to solve the vehicle routing problem

This paper presents an original hybrid approach to solve the Capacitated Vehicle Routing Problem (CVRP). The approach combines a Probabilistic Algorithm with Constraint Programming (CP) and Lagrangian Relaxation (LR). After introducing the CVRP and reviewing the existing literature on the topic, the paper proposes an approach based on a probabilistic Variable Neighbourhood Search (VNS) algorithm. Given a CVRP instance, this algorithm uses a randomized version of the classical Clarke and Wright Savings constructive heuristic to generate a starting solution. This starting solution is then improved through a local search process which combines: (a) LR to optimise each individual route, and (b) CP to quickly verify the feasibility of new proposed solutions. The efficiency of our approach is analysed after testing some well-known CVRP benchmarks. Benefits of our hybrid approach over already existing approaches are also discussed. In particular, the potential flexibility of our methodology is highlighted

Optimización de rutas para la recolección de desechos sólidos en la ciudad de Ibarra provincia de Imbabura

Diseñar un aplicativo Web de optimización de rutas para la recolección de desechos sólidos, utilizando tecnologías de posicionamiento global y sistemas de información geográfica.El presente proyecto se refiere sobre una optimización de rutas para la recolección de desechos sólidos en la ciudad de Ibarra provincia de Imbabura, realizado con herramientas libres como Framework Laravel con base de datos PostgreSQL, y más componentes que son descritas en los siguientes capítulos. El aplicativo de optimización de rutas consiste en optimizar la recolección de desechos sólidos en la ciudad de Ibarra, por consiguiente, el diseño e implementación de los módulos de registro de usuarios, registro de rutas, registro de contenedores, representación de zonas, seguimiento de vehículos recolectores, medición de volumen y peso de contenedores, y dar reportes detallados del estado de cada uno de los contenedores y vehículos de recolección. En la Introducción, se detalla el planteamiento de problema, situación actual, prospectiva, objetivos, alcance y justificación para el inicio del proyecto de la tesis. En el Capítulo I, se muestra el Marco Teórico referente a las competencias municipales articuladas a los objetivos del Plan Nacional y a sus Objetivos de desarrollo sustentable, así como también las herramientas a ser aplicadas, así como también la metodología de optimización de rutas. En el Capítulo II, se realiza el desarrollo del Aplicativo Web de optimización de rutas para la recolección de los desechos sólidos, empleando las fases de la metodología XP. En el Capítulo III, se presenta los resultados obtenidos de la aplicación y su respectiva validación. En el capítulo IV, se define las conclusiones, recomendaciones del aplicativo con los resultados en la tesis

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement