The shrinking and expanding heuristic for the fleet size and mix vehicle routing problem

The FSMVRP (Fleet Size and Mix Vehicle Routing Problem) is a variant of the Classical Capacitated Vehicle Routing Problem, CVRP. We suggest a new methodology, called the Shrinking and Expanding Heuristic (SEH) which is incorporated in a standard tabu search. To determine an appropriate fleet mix is a major challenge in this type of problem and the SEH technique is especially developed to find a good combination of vehicles by introducing a mechanism for changing the existing fleet mix during the search, thus also changing the underlying route structure. The SEH utilizes the concept of depletion and expansion of routes depending upon the filling degree of a vehicle. This strategy is tested on standard problem instances and good quality solutions are obtained.acceptedVersio

A Parallel multi-neighborhood cooperative tabu search for capacitated vehicle routing problems

This paper presents a parallel tabu search algorithm that utilizes several different neighborhood structures for solving the capacitated vehicle routing problem. Single neighborhood or neighborhood combinations are encapsulated in tabu search threads and they cooperate through a solution pool for the purpose of exploiting their joint power. The computational experiments on 32 large scale benchmark instances show that the proposed method is highly effective and competitive, providing new best solutions to four instances while the average deviation of all best solutions found from the collective best results reported in the literature is about 0.22%. We are also able to associate the beneficial use of special neighborhoods with some test instance characteristics and uncover some sources of the collective power of multi-neighborhood cooperation.acceptedVersio

A Hybrid approach for milk collection using trucks and trailers

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A cooperative parallel metaheuristic for the capacitated vehicle routing problem

This paper introduces a cooperative parallel metaheuristic for the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best-found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information identified from the solution clusters is applied to guide the intensification or diversification of the tabu search threads. Computational experiments on two sets of large-scale benchmark instance sets from the literature demonstrate that the suggested metaheuristic is highly competitive, providing new best solutions to ten of those well-studied instances.acceptedVersio

An Attribute Based Similarity Function for VRP Decision Support

When solving problems in the real world using optimization tools, the model solved by the tools is often only an approximation of the underlying, real, problem. In these circumstances, a decision maker (DM) should consider a diverse set of good solutions, not just an optimal solution as produced using the model. On the other hand, the same DM will only be interested in seeing a few of the alternative solutions, and not the plethora of solutions often produced by modern search techniques. There is thus a need to distinguish between good solutions using the attributes of solutions. We develop a distance function of the type proposed in the Psychology literature by Tversky (1977) for the class of VRP problems. We base our difference on the underlying structure of solutions.A DM is often interested in focusing on a set of solutions fulfilling certain conditions that are of specific importance that day, or in general, like avoiding a certain road due to construction that day. This distance measure can also be used to generate solutions containing these specific classes of attributes, as the normal search process might not supply enough of these interesting solutions. We illustrate the use of the functions in a Multiobjective Decision Support System (DSS) setting, where the DM might want to see the presence (or absence) of certain attributes, and show the importance of identifying solutions not on the Pareto front. Our distance measure can use any attributes of the solutions, not just those defined in the optimization model

AN EXTRACTION TRAIL GENERATOR USING LIDAR DATA AND TABU SEARCH

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