The Pickup and Delivery Problem with Time Windows (PDPTW) is a generalization of the well studied Vehicle Routing Problem with Time Windows (VRPTW). Since it models several typical planning situations in operational transportation logistics and public transit, the PDPTW has attracted growing interest in recent years. This paper proposes a Grouping Genetic Algorithm (GGA) for solving the PDPTW which features a group-oriented genetic encoding in which each gene represents a group of requests instead of a single request. The GGA is subject to a comparative test on the basis of two publicly available benchmark problem sets that comprise 9 and 56 PDPTW instances, respectively. The results show that the proposed GGA is competitive.

A Grouping Genetic Algorithm for the Pickup and Delivery Problem with Time Windows

This paper is a survey of the literature on applications of evolutionary algorithms for vehicle routing problems. It reports on genetic algorithms, evolution strategies, and particle swarm optimization when applied to the classical capacitated vehicle routing problem and many of its variants. The performance of evolutionary algorithms is also compared with the best alternative problem-solving approaches on benchmark instances.

State-of-the Art Review—Evolutionary Algorithms for Vehicle Routing

An Indexed Bibliography of Genetic Algorithms in Operations Research

Dial-a-ride problem (DARP) is an optimization problem which deals with the minimization of the cost of the provided service where the customers are provided a door-to-door service based on their requests. This optimization model presented in earlier studies, is considered in this study. Due to the non-linear nature of the objective function the traditional optimization methods are plagued with the problem of converging to a local minima. To overcome this pitfall we use metaheuristics namely Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Immune System (AIS). From the results obtained, we conclude that Artificial Immune System method effectively tackles this optimization problem by providing us with optimal solutions.

Pickup and delivery problem using metaheuristics techniques

This paper is a survey of the literature on applications of evolutionary algorithms for vehicle routing problems. It reports about genetic algorithms, evolution strategies and particle swarm optimization, when applied to the classical capacitated vehicle routing problem and many of its variants. The performance of evolutionary algorithms is also compared with the best alternative problem-solving approaches on benchmark instances.

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Evolutionary Algorithms for Vehicle Routing

Many transportation problems, such as the travelling salesman problem, are computationally hard but often solvable quickly, although with less certainty, by heuristic methods. Genetic algorithms fall into this category and generate results with favourable scaling behaviour. We apply a two-level genetic algorithm to an advanced transportation problem, an example of the General Pickup and Delivery Problem. We discuss the formulation of the problem as an evolutionary one, show that the results scale well with size and that application to real-world situations is within reach.

T. Bossomaier

Papers

Solving vehicle routing problems with genetic algorithms