A Matheuristic for Integrated Timetabling and Vehicle Scheduling

Planning a public transportation system is a complex process, which is usually broken down in several phases, performed in sequence. Most often, the trips required to cover a service with the desired frequency (headway) are decided early on, while the vehicles needed to cover these trips are determined at a later stage. This potentially leads to requiring a larger number of vehicles (and, therefore, drivers) that would be possible if the two decisions were performed simultaneously. We propose a multicommodity-flow type model for integrated timetabling and vehicle scheduling. Since the model is large-scale and cannot be solved by off-the-shelf tools with the efficiency required by planners, we propose a diving-type matheuristic approach for the problem. We report on the efficiency and effectiveness of two variants of the proposed approach, differing on how the continuous relaxation of the problem is solved, to tackle real-world instances of bus transport planning problem originating from customers of M.A.I.O.R., a leading company providing services and advanced decision-support systems to public transport authorities and operators. The results show that the approach can be used to aid even experienced planners in either obtaining better solutions, or obtaining them faster and with less effort, or both

A matheuristic for customized multi-level multi-criteria university timetabling

Course timetables are the organizational foundation of a university’s educational program. While students and lecturers perceive timetable quality individually according to their preferences, there are also collective criteria derived normatively such as balanced workloads or idle time avoidance. A recent challenge and opportunity in curriculum-based timetabling consists of customizing timetables with respect to individual student preferences and with respect to integrating online courses as part of modern course programs or in reaction to flexibility requirements as posed in pandemic situations. Curricula consisting of (large) lectures and (small) tutorials further open the possibility for optimizing not only the lecture and tutorial plan for all students but also the assignments of individual students to tutorial slots. In this paper, we develop a multi-level planning process for university timetabling: On the tactical level, a lecture and tutorial plan is determined for a set of study programs; on the operational level, individual timetables are generated for each student interlacing the lecture plan through a selection of tutorials from the tutorial plan favoring individual preferences. We utilize this mathematical-programming-based planning process as part of a matheuristic which implements a genetic algorithm in order to improve lecture plans, tutorial plans, and individual timetables so as to find an overall university program with well-balanced timetable performance criteria. Since the evaluation of the fitness function amounts to invoking the entire planning process, we additionally provide a proxy in the form of an artificial neural network metamodel. Computational results exhibit the procedure’s capability of generating high quality schedules

Balancing the Game: Comparative Analysis of Single Heuristics and Adaptive Heuristic Approaches for Sports Scheduling Problem

Sport timetabling problems are Combinatorial Optimization problems which involve the creation of schedules that determine when and where teams compete against each other. One specific type of sports scheduling, the double round-robin (2RR) tournament, mandates that each team faces every other team twice, once at their home venue and once at the opponent’s. Despite the relatively small number of teams involved, the sheer volume of potential scheduling combinations has spurred researchers to employ various techniques to find efficient solutions for sports scheduling problems. In this thesis, we present a comparative analysis of single and adaptive heuristics designed to efficiently solve sports scheduling problems. Specifically, our focus is on constructing time-constrained double round-robin tournaments involving 16 to 20 teams, while adhering to hard constraints and minimizing penalties for soft constraints violations. The computational results demonstrate that our adaptive heuristic approach not only successfully finds feasible solutions for the majority of instances but also outperforms the single heuristics examined in this study.Master's Thesis in InformaticsINF399MAMN-INFMAMN-PRO

Matheuristics: using mathematics for heuristic design

Matheuristics are heuristic algorithms based on mathematical tools such as the ones provided by mathematical programming, that are structurally general enough to be applied to different problems with little adaptations to their abstract structure. The result can be metaheuristic hybrids having components derived from the mathematical model of the problems of interest, but the mathematical techniques themselves can define general heuristic solution frameworks. In this paper, we focus our attention on mathematical programming and its contributions to developing effective heuristics. We briefly describe the mathematical tools available and then some matheuristic approaches, reporting some representative examples from the literature. We also take the opportunity to provide some ideas for possible future development

Applications of Mathematical Programming in Personnel Scheduling

In the few decades of its existence, mathematical programming has evolved into an important branch of operations research and management science. This thesis consists of four papers in which we apply mathematical programming to real-life personnel scheduling and project management problems. We develop exact mathematical programming formulations. Furthermore, we propose effective heuristic strategies to decompose the original problems into subproblems that can be solved effciently with tailored mathematical programming formulations. We opt for solution methods that are based on mathematical programming, because their advantages in practice are a) the exibility to easily accommodate changes in the problem setting, b) the possibility to evaluate the quality of the solutions obtained, and c) the possibility to use general-purpose solvers, which are often the only software available in practice

Inbound and outbound flow integration for cross-docking operations

We consider the optimization of the cross-docking operations at three INtermodal LOgistics Platforms (INLOPs) of a large European car manufacturer (ECM). The planning horizon is a week and the time bucket is a day. An inbound flow of products is gradually received over the week by truck from inland suppliers, and has to be loaded into containers which are then shipped to offshore production plants. The full content of a container must be available at the INLOP to enable its loading operations to start, hence temporary storage is needed. The objective is to minimize an inventory penalty, computed as the largest daily volume of temporary product storage observed over the planning horizon. The current practice at ECM is to first optimize the content of the inbound trucks and of the outbound containers independently, and then determine the loading day of each container to be shipped based on these fixed contents. We propose to integrate, within the same optimization framework, the decisions on both truck and container contents, which involve complex loading constraints related to the dimensions and weights of the products, with those on the scheduling of container loading. We model the resulting problem as a mixed integer linear program, and we develop a decomposition scheme for it, as well as a fix-and-optimize matheuristic. We perform extensive computational experiments on real instances provided by ECM. Results show that a combination of these two matheuristics is able to generate solutions that reduce the average inventory penalty by 40%.</p

Educational timetabling: Problems, benchmarks, and state-of-the-art results

We propose a survey of the research contributions on the field of Educational Timetabling with a specific focus on “standard” formulations and the corresponding benchmark instances. We identify six of such formulations and we discuss their features, pointing out their relevance and usability. Other available formulations and datasets are also reviewed and briefly discussed. Subsequently, we report the main state-of-the-art results on the selected benchmarks, in terms of solution quality (upper and lower bounds), search techniques, running times, and other side settings