This paper introduces a hybrid metaheuristic, the Variable Neighborhood Particle Swarm Optimization (VNPSO), consisting of a combination of the Variable Neighborhood Search (VNS) and Particle Swarm Optimization(PSO). The proposed VNPSO method is used for solving the multi-objective Flexible Job-shop Scheduling Problems (FJSP). The details of implementation for the multi-objective FJSP and the c...

Due to varying user demands in bicycle sharing systems, operators need to actively shift bikes between stations by a fleet of vehicles. We address the problem of finding efficient vehicle tours by an extended version of an iterated greedy construction heuristic following the concept of the PILOT method and GRASP and applying a variable neighborhood descend (VND) as local improvement. Computatio...

Using a heuristic optimization module based upon Variable Neighborhood Search (VNS), the system AutoGraphiX’s main feature is to find extremal or near extremal graphs, i.e., graphs that minimize or maximize an invariant. From the so obtained graphs, conjectures are found either automatically or interactively. Most of the features of the system relies on the optimization that must be efficient b...

In this paper we present two major approaches to solve the car sequencing problem, in which the goal is to find an optimal arrangement of commissioned vehicles along a production line with respect to constraints of the form “no more than lc cars are allowed to require a component c in any subsequence of mc consecutive cars”. The first method is an exact one based on integer linear programming (...

In this work we consider the generalized vehicle routing problem with stochastic demands (GVRPSD) being a combination of the generalized vehicle routing problem, in which the nodes are partitioned into clusters, and the vehicle routing problem with stochastic demands, where the exact demands of the nodes are not known beforehand. It is an NP-hard problem for which we propose a variable neighbor...

In the One-Commodity Pickup and Delivery Problem (1PDP), a single commodity type is collected from a set of pickup customers to be delivered to a set of delivery customers, and the origins and destinations of the goods are not paired. We introduce a new adaptive hybrid VNS/SA (Variable Neighborhood Search/Simulated Annealing) approach for solving the 1-PDP. We perform sequences of VNS runs, whe...

In this paper, a new hybrid metaheuristic learning algorithm is introduced to choose the best parameters for the multi-criteria decision making algorithm (MCDA) called PROAFTN. PROAFTN requires values of several parameters to be determined prior to classification. These parameters include boundaries of intervals and relative weights for each attribute. The proposed learning algorithm, identifie...

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Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley. It was first proposed in 1997 and has since then rapidly developed both in its methods and its applications. In the present paper, these t...