A Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery

The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection  Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company

A new three phase method (SDP method) for the multi-objective vehicle routing problem with simultaneous delivery and pickup (VRPSDP)

Transportation service operators are witnessing a growing demand for bi-directional movement of goods. Given this, the following thesis considers an extension to the vehicle routing problem (VRP) known as the delivery and pickup transportation problem (DPP), where delivery and pickup demands may occupy the same route. The problem is formulated here as the vehicle routing problem with simultaneous delivery and pickup (VRPSDP), which requires the concurrent service of the demands at the customer location. This formulation provides the greatest opportunity for cost savings for both the service provider and recipient. The aims of this research are to propose a new theoretical design to solve the multi-objective VRPSDP, provide software support for the suggested design and validate the method through a set of experiments. A new real-life based multi-objective VRPSDP is studied here, which requires the minimisation of the often conflicting objectives: operated vehicle fleet size, total routing distance and the maximum variation between route distances (workload variation). The former two objectives are commonly encountered in the domain and the latter is introduced here because it is essential for real-life routing problems. The VRPSDP is defined as a hard combinatorial optimisation problem, therefore an approximation method, Simultaneous Delivery and Pickup method (SDPmethod) is proposed to solve it. The SDPmethod consists of three phases. The first phase constructs a set of diverse partial solutions, where one is expected to form part of the near-optimal solution. The second phase determines assignment possibilities for each sub-problem. The third phase solves the sub-problems using a parallel genetic algorithm. The suggested genetic algorithm is improved by the introduction of a set of tools: genetic operator switching mechanism via diversity thresholds, accuracy analysis tool and a new fitness evaluation mechanism. This three phase method is proposed to address the shortcoming that exists in the domain, where an initial solution is built only then to be completely dismantled and redesigned in the optimisation phase. In addition, a new routing heuristic, RouteAlg, is proposed to solve the VRPSDP sub-problem, the travelling salesman problem with simultaneous delivery and pickup (TSPSDP). The experimental studies are conducted using the well known benchmark Salhi and Nagy (1999) test problems, where the SDPmethod and RouteAlg solutions are compared with the prominent works in the VRPSDP domain. The SDPmethod has demonstrated to be an effective method for solving the multi-objective VRPSDP and the RouteAlg for the TSPSDP

Solución del problema de enrutamiento de vehículos con entregas y recogidas simultáneas : Una nueva matheurística

Este libro, producto de mi tesis doctoral, presenta una metodología para resolver el problema de enrutamiento de vehículos homogéneos con recogidas y entregas simultáneas (VRPSPD) utilizando matheurística formada por el algoritmo genético especializado Chu -Beasley y técnicas exactas de programación lineal de enteros mixtos, basadas en el procedimiento Branch -and- Bound, aplicado a la mejor configuración obtenida del algoritmo genético con el apoyo de métodos heurísticos constructivos en la determinación de los subproblemas, que hacen parte de la generación de la población inicial, necesaria en la etapa de mejora local. El problema considera un conjunto de clientes, cuyas demandas de recogida y entrega de productos o personas son conocidas, y cuyo objetivo es obtener el conjunto de rutas de costo mínimo, que permitan satisfacer la demanda de los clientes, considerando las respectivas limitaciones del sistema y los vehículos necesarios para completar el mismo. En su desarrollo se consideraron los siguientes aspectos: · Fundamentación teórica del problema de ruteo de vehículos con entregas y recogidas simultáneas. · Diseño metodológico para el desarrollo del contenido del libro. · Experimentos computacionales: La metodología desarrollada se implementa en C ++, y se utiliza un software de resolución CPLEX para encontrar la solución. La eficiencia de la implementación del algoritmo se verifica con el uso de instancias de prueba disponibles en la literatura especializada, obteniendo buenos resultados en las pruebas en tiempos de cómputo relativamente cortos