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Designing a public transport timetable that maximizes passenger service, measured in weighted travel time, is an intricate problem. The weighted travel time depends on the free route choice of passengers. Passenger route choice depends on the timetable. In turn, the timetable that minimizes weighted travel time depends on the route choice of passengers—and therefore requires passenger route choice information. Consequently, a sequential approach where timetables are designed provided pre-fixed passenger assignment to routes may not find the optimal timetable. This paper aims to integrate passenger route choice and timetabling. It addresses the problem of designing maximal passenger service public transport timetables in systems with free route choice within a budget for operating costs. Operating costs are defined by the minimal cost vehicle schedule required to operate the timetable. The proposed methodology integrates a matheuristic for timetabling and vehicle scheduling with a passenger assignment model in an iterative framework, where different forms of integration are evaluated. Focus is on long- to medium-term timetabling, provided an initial timetable. The results for a realistic case study in the Greater Copenhagen area indicate that our approach consistently leads, at no additional cost, to timetables that represent a reduction in passenger weighted travel time in comparison with both an initial timetable and a non-integrated timetabling method that receives a single-passenger assignment as input. [ABSTRACT FROM AUTHOR]

In this paper, we present a solution method for the multidimensional knapsack problem (MKP) and the knapsack problem with forfeit sets (KPFS) using a population-based matheuristic approach. Specifically, the learning mechanism of the fixed set search (FSS) metaheuristic is combined with the use of integer programming for solving subproblems. This is achieved by introducing a new ground set of elements that can be used for both the MKP and the KPFS that aim to maximize the information provided by the fixed set. The method for creating fixed sets is also adjusted to enhance the diversity of generated solutions. Compared to state-of-the-art methods for the MKP and the KPFS, the proposed approach offers an implementation that can be easily extended to other variants of the knapsack problem. Computational experiments indicate that the matheuristic FSS is highly competitive to best-performing methods from the literature. The proposed approach is robust in the sense of having a good performance for a wide range of parameter values of the method. [ABSTRACT FROM AUTHOR]

We consider the swap body vehicle routing problem, an extension of the capacitated vehicle routing problem, in which intermediate locations can be used to change the configuration of a vehicle. Possible actions include a parking operation that decouples the semitrailer as well as a swapping operation that switches the swap body with another one, which was previously parked at an intermediate location. Successful ideas that combine mathematical programming and heuristics have been recently presented for several vehicle routing problems. In this line, the contribution of this paper is the development of a column generation-based approach, in which a variable neighborhood search heuristic populates the route pool. A detailed numerical analysis is carried out for 138 instances with up to 1000 customers and at most 100 intermediate locations. Comparisons with existing metaheuristics show that our solution strategy is suitable for solving this problem. It has obtained 42 new best known solutions with a maximal improvement of 18.54% on published benchmark instances. [ABSTRACT FROM AUTHOR]

The multi-depot vehicle routing problem with inter-depot routes is studied in this paper, where vehicles may reset their capacity at any depot during the working day. Due to the complexity of this problem, exact approaches are limited to small-size applications. In order to overcome this limitation, we propose a matheuristic which integrates a mixed integer linear programming formulation with a set of relax-and-fix strategies. This solution approach is shown to be very efficient, and for the first time, large-size benchmarking instances are solved. [ABSTRACT FROM AUTHOR]

An optimisation model is proposed for scheduling the decommissioning of an offshore wind farm in order to minimise the total cost which is comprised of jack-up vessel, barge (transfer) vessel, inventory, processing and on-land transportation costs. This paper also presents a comprehensive review of the strategic issues relating to the decommissioning process and of scheduling models that have been applied to offshore wind farms. A mathematical model using integer linear programming is developed to determine the optimal schedule considering several constraints such as the availability of vessels and planning delays. As the decommissioning problem is challenging to solve, a matheuristic approach based on the hybridisation of a heuristic approach and an exact method is also proposed to find near optimal solutions for a test set of problems. A set of computational experiments has been carried out to assess the proposed approach. [ABSTRACT FROM AUTHOR]