14,884 research outputs found
Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows
The well-known Vehicle Routing Problem with Time Windows (VRPTW) aims to
reduce the cost of moving goods between several destinations while
accommodating constraints like set time windows for certain locations and
vehicle capacity. Applications of the VRPTW problem in the real world include
Supply Chain Management (SCM) and logistic dispatching, both of which are
crucial to the economy and are expanding quickly as work habits change.
Therefore, to solve the VRPTW problem, metaheuristic algorithms i.e. Particle
Swarm Optimization (PSO) have been found to work effectively, however, they can
experience premature convergence. To lower the risk of PSO's premature
convergence, the authors have solved VRPTW in this paper utilising a novel form
of the PSO methodology that uses the Roulette Wheel Method (RWPSO). Computing
experiments using the Solomon VRPTW benchmark datasets on the RWPSO demonstrate
that RWPSO is competitive with other state-of-the-art algorithms from the
literature. Also, comparisons with two cutting-edge algorithms from the
literature show how competitive the suggested algorithm is
A new exact algorithm for the multi-depot vehicle routing problem under capacity and route length constraints
This article presents an exact algorithm for the multi-depot vehicle routing problem (MDVRP) under capacity and route length constraints. The MDVRP is formulated using a vehicle-flow and a set-partitioning formulation, both of which are exploited at different stages of the algorithm. The lower bound computed with the vehicle-flow formulation is used to eliminate non-promising edges, thus reducing the complexity of the pricing subproblem used to solve the set-partitioning formulation. Several classes of valid inequalities are added to strengthen both formulations, including a new family of valid inequalities used to forbid cycles of an arbitrary length. To validate our approach, we also consider the capacitated vehicle routing problem (CVRP) as a particular case of the MDVRP, and conduct extensive computational experiments on several instances from the literature to show its effectiveness. The computational results show that the proposed algorithm is competitive against stateof-the-art methods for these two classes of vehicle routing problems, and is able to solve to optimality some previously open instances. Moreover, for the instances that cannot be solved by the proposed algorithm, the final lower bounds prove stronger than those obtained by earlier methods
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