HEC Montréal

About: HEC Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Context (language use) & Vehicle routing problem. The organization has 1221 authors who have published 5708 publications receiving 196862 citations. The organization is also known as: Ecole des Hautes Etudes Commerciales de Montreal & HEC Montreal.

Why does private consumption rise after a government spending shock

Abstract: Recent empirical evidence suggests that private consumption is crowded-in by government spending. This outcome violates existing macroeconomic theory, according to which the negative wealth effect brought about by a rise in public expenditure should decrease consumption.

Simultaneous Vehicle and Crew Scheduling in Urban Mass Transit Systems

Abstract: This paper presents an exact approach for solving the simultaneous vehicle and crew scheduling problem in urban mass transit systems. We consider the single depot case with a homogeneous fleet of vehicles. This approach relies on a set partitioning formulation for the driver scheduling problem that incorporates side constraints for the bus itineraries. The proposed solution approach consists of a column generation process (only for the crew schedules) integrated into a branch-and-bound scheme. The side constraints on buses guarantee that an optimal vehicle assignment can be derived afterwards in polynomial time. A computational study shows that this approach out-performs the previous methods found in the literature for a set of randomly generated instances. A heuristic version of the solution approach is also proposed and tested on larger instances.

Benders Decomposition for Large-Scale Uncapacitated Hub Location

Abstract: This paper describes an exact algorithm capable of solving large-scale instances of the well-known uncapacitated hub location problem with multiple assignments. The algorithm applies Benders decomposition to a strong path-based formulation of the problem. The standard decomposition algorithm is enhanced through the inclusion of several features such as the use of a multicut reformulation, the generation of strong optimality cuts, the integration of reduction tests, and the execution of a heuristic procedure. Extensive computational experiments were performed to evaluate the efficiency and robustness of the algorithm. Computational results obtained on classical benchmark instances (with up to 200 nodes) and on a new and more difficult set of instances (with up to 500 nodes) confirm the efficiency of the algorithm.

Team Goal Commitment and Team Effectiveness: The Role of Task Interdependence and Supportive Behaviors

Abstract: The objectives of this study were to test the relationships between team goal commitment and 3 criteria of team effectiveness (i.e., team performance, quality of group experience, and team viability) as well as to examine the moderating effect of task interdependence and the mediating role of supportive behaviors. Data were gathered from a sample of 74 teams working in 13 Canadian organizations. Results indicated that team goal commitment is positively related to all 3 criteria of team effectiveness. In addition, task interdependence moderates the relationship between team goal commitment and team performance. Furthermore, supportive behaviors mediate the relationships that team goal commitment has with team performance and the quality of group experience. Implications of these findings and future research needs are discussed.

Single-line rail rapid transit timetabling under dynamic passenger demand

Abstract: Railway planning is a complex activity which is usually decomposed into several stages, traditionally network design, line design, timetabling, rolling stock, and staffing. In this paper, we study the design and optimization of train timetables for a rail rapid transit (RRT) line adapted to a dynamic demand environment, which focuses on creating convenient timetables for passengers. The objective is to minimize the average passenger waiting time at the stations, thus focusing on passenger welfare. We first propose two mathematical programming formulations which generalize the non-periodic train timetabling problem on a single line under a dynamic demand pattern. We then analyze the properties of the problem before introducing a fast adaptive large neighborhood search (ALNS) metaheuristic in order to solve large instances of the problem within short computation times. The algorithm yields timetables that may not be regular or periodic, but are adjusted to a dynamic demand behavior. Through extensive computational experiments on artificial and real-world based instances, we demonstrate the computational superiority of our ALNS compared with a truncated branch-and-cut algorithm. The average reduction in passenger waiting times is 26%, while the computational time of our metaheuristic is less than 1% of that required by the alternative CPLEX-based algorithm. Out of 120 open instances, we obtain 84 new best known solutions and we reach the optimum on 10 out of 14 instances with known optimal solutions.