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

A comparison of recombination operators for capacitate vehicle routing problem

The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles, and the sequencing of stops for each vehicle to minimize transportation costs. In this paper we study the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. Taking a basic GA to solve the CVRP, we propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX). A comparison of its performance is carried out with respect to other two classical recombination operators. Also we conduct a study of different mutations in order to determine the best combination of genetic operators for this problem. The results show that the use of our specialized BRBAX recombination outperforms the others more generic on all problem instances used in this work for all the metrics tested.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

A distributed evolutionary algorithm with a superlinear speedup for solving the vehicle routing problem

In this paper we present a distributed evolutionary algorithm for solving the capacitated vehicle routing problem. Our algorithm consists of autonomous processes that create heterogeneous evolutionary environments, perform evolution on separate populations of chromosomes, and communicate asynchronously through occasional migrations of chromosomes. The paper also presents experiments where the algorithm has been tested on some benchmark problem instances. By measuring the effects of distribution on solution quality and on computing time, the experiments confirm that the algorithm achieves a superlinear speedup

Quantum annealing for vehicle routing and scheduling problems

Metaheuristic approaches to solving combinatorial optimization problems have many attractions. They sidestep the issue of combinatorial explosion; they return good results; they are often conceptually simple and straight forward to implement. There are also shortcomings. Optimal solutions are not guaranteed; choosing the metaheuristic which best fits a problem is a matter of experimentation; and conceptual differences between metaheuristics make absolute comparisons of performance difficult. There is also the difficulty of configuration of the algorithm - the process of identifying precise values for the parameters which control the optimization process. Quantum annealing is a metaheuristic which is the quantum counterpart of the well known classical Simulated Annealing algorithm for combinatorial optimization problems. This research investigates the application of quantum annealing to the Vehicle Routing Problem, a difficult problem of practical significance within industries such as logistics and workforce scheduling. The work devises spin encoding schemes for routing and scheduling problem domains, enabling an effective quantum annealing algorithm which locates new solutions to widely used benchmarks. The performance of the metaheuristic is further improved by the development of an enhanced tuning approach using fitness clouds as behaviour models. The algorithm is shown to be further enhanced by taking advantage of multiprocessor environments, using threading techniques to parallelize the optimization workload. The work also shows quantum annealing applied successfully in an industrial setting to generate solutions to complex scheduling problems, results which created extra savings over an incumbent optimization technique. Components of the intellectual property rendered in this latter effort went on to secure a patent-protected status

AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned Evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process

A comparison of recombination operators for capacitate vehicle routing problem

The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles, and the sequencing of stops for each vehicle to minimize transportation costs. In this paper we study the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. Taking a basic GA to solve the CVRP, we propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX). A comparison of its performance is carried out with respect to other two classical recombination operators. Also we conduct a study of different mutations in order to determine the best combination of genetic operators for this problem. The results show that the use of our specialized BRBAX recombination outperforms the others more generic on all problem instances used in this work for all the metrics tested.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI