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Sports timetabling is a complex and challenging problem. The latest open benchmark dataset for the sport timetabling problem is from the International Timetabling Competition (ITC) 2021. Due to its complexity, only a few approaches have successfully generated feasible solutions for the problems in this dataset, as reported in scientific literature. To the best of our knowledge, there is only one study in the literature that has successfully generated feasible solutions for all 45 problems in the dataset. In this paper, we propose our novel efficient algorithm based on the Iterated Local Search algorithm to solve the ITC 2021 benchmark dataset. Unlike prior successful approaches that combined metaheuristics with an exact approach, our proposed approach is solely metaheuristic. Our contribution includes the design of strategies for both perturbation and local search phases, coupled with the integration of shuffling strategies. The experimental results show that our proposed algorithm is remarkably successful in generating feasible solutions for all 45 problems present in the ITC 2021 dataset. [ABSTRACT FROM AUTHOR]

Mobility as a Service (MaaS) transforms urban transportation from car ownership to subscription-based models. A key factor for the success of MaaS is accurately predicting users’ Willingness to Pay (WTP) for various subscription packages, enhancing their adoption and satisfaction. This paper employs a “smart predict-then-optimize” framework, where the weekly, annual, and monthly MaaS subscription models are formulated as online, offline, and hybrid online-offline mobility resource allocation problems, respectively. We develop a group method of data handling (GMDH)-driven self-adaptive harmony search (SAHS) algorithm to solve the proposed mobility resource allocation problems effectively. Initially, GMDH-type neural networks predict users’ WTP using their historical travel data, such as travel distance and service time, and socio-demographic characteristics, including inconvenience tolerance and travel delay budget; then these predicted WTP values are fed into the weekly, annual, and monthly mobility resource allocation problems, respectively. Comprehensive numerical experiments based on a simulated dataset demonstrate the robust prediction performance of the GMDH neural network across weekly, monthly, and annual subscription models, as well as the effectiveness of the GMDH-driven SAHS algorithm in managing resource allocation for these models. Our numerical findings highlight that the monthly subscription model strikes an optimal balance, combining the flexibility of the weekly model with the strategic depth of the annual model. This study proposes three distinct MaaS subscription models and a data-driven metaheuristic algorithm to customize MaaS offerings to user needs. [ABSTRACT FROM AUTHOR]

In this paper we present a hybrid metaheuristic approach called PVS (Progress and Verify Strategy) for the two-dimensional strip packing problem (2SPP). PVS relies on two procedures: a local search algorithm that delivers satisfying placements of the items on the horizontal axis, and an exact procedure that searches for the positions of the items on the vertical axis. This last one explores all the possibilities, starting with the most promising ones, and can be stopped at any moment. PVS follows a specific anytime strategy which continuously improves the current solution until it is provably optimal or a given time limit is reached. Experimental results show that the method is competitive on moderate-sized instances compared to the best known approaches. [ABSTRACT FROM AUTHOR]

This article considers the heterogeneous fleet vehicle routing problem, as a variant of a well-known transportation problem: the vehicle routing problem. In order to solve this particular routing problem, a variable neighborhood search with a threshold accepting mechanism is developed and implemented. The performance of the algorithm was compared to other algorithms and tested on datasets from the available literature. Computational results show that our proposed algorithm is competitive and generates new best solutions. [ABSTRACT FROM AUTHOR]

One of the main challenges for university administration is building a timetable for course sessions. This is not just about building a timetable that works, but building one that is as good as possible. In general, course timetabling is the process of assigning given courses to given rooms and timeslots under specific constraints. Harmony search algorithm is a new metaheuristic population-based algorithm, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments together seeking a pleasing harmony. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. In this paper, a harmony search and a modified harmony search algorithm are applied to university course timetabling against standard benchmarks. The results show that the proposed methods are capable of providing viable solutions in comparison to previous works. [ABSTRACT FROM AUTHOR]