An Aircraft Service Staff Rostering using a Hybrid GRASP Algorithm

The aircraft ground service company is responsible for carrying out the regular tasks to aircraft maintenace between their arrival at and departure from the airport. This paper presents the application of a hybrid approach based upon greedy randomized adaptive search procedure (GRASP) for rostering technical staff such that they are assigned predefined shift patterns. The rostering of staff is posed as an optimization problem with an aim of minimizing the violations of hard and soft constraints. The proposed algorithm iteratively constructs a set of solutions by GRASP. Furthermore, with multi-agent techniques, we efficiently identify an optimal roster with minimal constraint violations and fair to employees. Experimental results are included to demonstrate the effectiveness of the proposed algorithm

Bi-objective Evolutionary Heuristics for Bus Drivers

The Bus Driver Rostering Problem refers to the assignment of drivers to the daily schedules of the company's buses, during a planning period of a given duration. The drivers' schedules must comply with legal and institutional rules, namely the Labour Law, labour agreements and the company's specific regulations. This paper presents a bi-objective model for the problem and two evolutionary heuristics differing as to the strategies adopted to approach the Pareto frontier. The first one, the utopian strategy, extends elitism to include an unfeasible solution in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics' empirical performance is studied with computational tests on a set of instances generated from vehicle and crew schedules. This research shows that both methodologies are adequate to tackle the instances of the Bus Driver Rostering Problem. In fact, in short computing times, they provide the planning department, with several feasible solutions, rosters that are very difficult to obtain manually and, in addition, identify among them the efficient solutions of the bi-objective model

Bus driver rostering by hybrid methods based on column generation

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2018Rostering problems arise in a diversity of areas where, according to the business and labor rules, distinct variants of the problem are obtained with different constraints and objectives considered. The diversity of existing rostering problems, allied with their complexity, justifies the activity of the research community addressing them. The current research on rostering problems is mainly devoted to achieving near-optimal solutions since, most of the times, the time needed to obtain optimal solutions is very high. In this thesis, a Bus Driver Rostering Problem is addressed, to which an integer programming model is adapted from the literature, and a new decomposition model with three distinct subproblems representations is proposed. The main objective of this research is to develop and evaluate a new approach to obtain solutions to the problem in study. The new approach follows the concept of search based on column generation, which consists in using the column generation method to solve problems represented by decomposition models and, after, applying metaheuristics to search for the best combination of subproblem solutions that, when combined, result in a feasible integer solution to the complete problem. Besides the new decomposition models proposed for the Bus Driver Rostering Problem, this thesis proposes the extension of the concept of search by column generation to allow using population-based metaheuristics and presents the implementation of the first metaheuristic using populations, based on the extension, which is an evolutionary algorithm. There are two additional contributions of this thesis. The first is an heuristic allowing to obtain solutions for the subproblems in an individual or aggregated way and the second is a repair operator which can be used by the metaheuristics to repair infeasible solutions and, eventually, generate missing subproblem solutions needed. The thesis includes the description and results from an extensive set of computational tests. Multiple configurations of the column generation with three decomposition models are tested to assess the best configuration to use in the generation of the search space for the metaheuristic. Additional tests compare distinct single-solution metaheuristics and our basic evolutionary algorithm in the search for integer solutions in the search space obtained by the column generation. A final set of tests compares the results of our final algorithm (with the best column generation configuration and the evolutionary algorithm using the repair operator) and the solutions obtained by solving the problem represented by the integer programming model with a commercial solver.Programa de Apoio à Formação Avançada de Docentes do Ensino Superior Politécnico (PROTEC), SFRH/PROTEC/67405/201

A Memetic Algorithm for a Bi-objective Bus Driver Rostering Problem

The Bus Driver Rostering Problem (DRP) consists of assigning bus drivers to daily duties during a planning period. The problem considers hard constraints imposed by institutional and legal requirements. Solutions should as much as possible satisfy soft constraints that qualify rosters according to either the company's or the drivers' interests. A bi-objective version of the DRP is considered and two models are presented. Due to the high computational complexity of DRP, this paper proposes the Strength Pareto Utopic Memetic Algorithm (SPUMA) a new heuristic algorithm specially devised to tackle the problem. SPUMA genetic component combines utopic elitism with a strength Pareto fitness evaluation and includes an improvement procedure. Computational results show that SPUMA outperforms an adaptation of one of the state-of-the-art most competitive multi-objective evolutionary algorithms, SPEA2

Solving Public Transit Scheduling Problems

Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicle scheduling, crew scheduling and rostering problems. In this paper, a new integer mathematical formulation to describe the integrated vehicle-crew-rostering problem is presented. The method proposed to solve this multi-objective problem is a sequential algorithm considered within a preemptive goal programming framework that starts from the solution of an integrated vehicle and crew scheduling problem and ends with the solution of a driver rostering problem. Feasible solutions for the vehicle and crew scheduling problem are obtained by combining a column generation scheme with a branch-and-bound method. These solutions are the input of the rostering problem, which is tackled through a mixed binary linear programming approach. An application to real data of a Portuguese bus company is reported and shows the importance of integrating the three scheduling problems

A two-phase method for the Shift Design and Personnel Task Scheduling Problem with Equity objective

International audienceIn this paper, we study the Shift Design and Personnel Task Scheduling Problem with Equity objective (SDPTSP-E), initially introduced in [11]. This problem consists in designing the shifts of workers and assigning a set of tasks to quali ed workers, so as to maximise the equity between workers. We propose a natural two-phase approach consisting in rst designing shifts and then assigning tasks to workers, and we iterate between these two phases to improve solutions. We compare our experimental results with existing literature and show that our approach outperforms previous known results