Abstract-The TSP, VRP and OP problems with time constraints have one common sub-problem -the task of finding the minimum route duration for a given order of customers. While much work has been done on routing and scheduling problems with time windows, to this date only few articles considered problems with multiple time windows. Moreover, since the assumption of constant travel time between two locations at all times is very unrealistic, problems with time-dependent travel were introduced and studied. Finally, it is also possible to imagine some situations, in which the service time changes during the day. Again, both issues have been investigated only in conjunction with single time windows. In this paper we propose a novel algorithm for computing minimum route duration in traveling salesman problem with multiple time windows and time-dependent travel and service time. The algorithm can be applied to wide range of problems in which a traveler has to visit a set of customers or locations within specified time windows taking into account the traffic and variable service/visit time. Furthermore, we compare three metaheuristics for computing a one-day schedule for this problem, and show that it can be solved very efficiently.

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Developing effective, fairness-preserving optimization algorithms is of considerable importance in systems which serve many users. In this paper we show the results of the threshold accepting procedure applied to extremely difficult problem of fair resource allocation in wireless mesh networks (WMN). The fairness is modeled by allowing preferences with regard to distribution of Internet traffic between network participants. As aggregation operator we utilize weighted ordered weighted averaging (WOWA). In the underlaying optimization problem, the physical medium properties cause strong interference among simultaneously operating node devices, leading to nonlinearities in the mixed-integer pricing subproblem. That is where the threshold accepting procedure is applied. We show that, the threshold accepting heuristic performs much better than the widely utilized simulated annealing algorithm.

The ordered weighted averaging (OWA) operator uses the weights assigned to the ordered values of the attributes. This allows one to model various aggregation preferences characterized by the so-called orness measure. The determination of the OWA operator weights is a crucial issue of applying the operator for decision making. In this paper, for a given orness value, monotonic weights of the OWA operator are determined by minimization of the maximum absolute deviation inequality measure. This leads to a linear programming model which can also be solved analytically.