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.

Clinical Effects of Home Telemonitoring in the Context of Diabetes, Asthma, Heart Failure and Hypertension: A Systematic Review

Abstract: Background: Home telemonitoring figures among the various solutions that could help attenuate some of the problems associated with aging populations, rates of chronic illness, and shortages of health professionals. Objective: The primary aim of this study was to further our understanding of the clinical effects associated with home telemonitoring programs in the context of chronic diseases. Results: In all, 62 empirical studies were analyzed. The results from studies involving patients with diabetes indicated a trend toward patients with home telemonitoring achieving better glycemic control. In most trials in which patients with asthma were enrolled, results showed significant improvements in patients' peak expiratory flows, significant reductions in the symptoms associated with this illness, and improvements in perceived quality of life. Virtually all studies involving patients with hypertension demonstrated the ability of home telemonitoring to reduce systolic and/or diastolic blood pressure. Lastly, due to the equivocal nature of current findings of home telemonitoring involving patients with heart failure, larger trials are still needed to confirm the clinical effects of this technology for these patients. Conclusions: Although home telemonitoring appears to be a promising approach to patient management, designers of future studies should consider ways to make this technology more effective as well as controlling possible mediating variables.

Effectiveness of mHealth interventions for patients with diabetes: An overview of systematic reviews

Abstract: Background Diabetes is a common chronic disease that places an unprecedented strain on health care systems worldwide. Mobile health technologies such as smartphones, mobile applications, and wearable devices, known as mHealth, offer significant and innovative opportunities for improving patient to provider communication and self-management of diabetes. Objective The purpose of this overview is to critically appraise and consolidate evidence from multiple systematic reviews on the effectiveness of mHealth interventions for patients with diabetes to inform policy makers, practitioners, and researchers. Methods A comprehensive search on multiple databases was performed to identify relevant systematic reviews published between January 1996 and December 2015. Two authors independently selected reviews, extracted data, and assessed the methodological quality of included reviews using AMSTAR. Results Fifteen systematic reviews published between 2008 and 2014 were eligible for inclusion. The quality of the reviews varied considerably and most of them had important methodological limitations. Focusing on systematic reviews that offered the most direct evidence, this overview demonstrates that on average, mHealth interventions improve glycemic control (HbA1c) compared to standard care or other non-mHealth approaches by as much as 0.8% for patients with type 2 diabetes and 0.3% for patients with type 1 diabetes, at least in the short-term (≤12 months). However, limitations in the overall quality of evidence suggest that further research will likely have an important impact in these estimates of effect. Conclusions Findings are consistent with clinically relevant improvements, particularly with respect to patients with type 2 diabetes. Similar to home telemonitoring, mHealth interventions represent a promising approach for self-management of diabetes.

The impact of a simulation game on operations management education

Abstract: This study presents a new simulation game and analyzes its impact on operations management education. The proposed simulation was empirically tested by comparing the number of mistakes during the first and second halves of the game. Data were gathered from 100 teams of four or five undergraduate students in business administration, taking their first course in operations management. To assess learning, instead of relying solely on an overall performance measurement, as is usually done in the skill-based learning literature, we analyzed the evolution of different types of mistakes that were made by students in successive rounds of play. Our results show that although simple decision-making skills can be acquired with traditional teaching methods, simulation games are more effective when students have to develop decision-making abilities for managing complex and dynamic situations.

Models and branch-and-cut algorithms for pickup and delivery problems with time windows

Abstract: In the pickup and delivery problem with time windows (PDPTW), capacitated vehicles must be routed to satisfy a set of transportation requests between given origins and destinations. In addition to capacity and time window constraints, vehicle routes must also satisfy pairing and precedence constraints on pickups and deliveries. This paper introduces two new formulations for the PDPTW and the closely related dial-a-ride problem (DARP) in which a limit is imposed on the elapsed time between the pickup and the delivery of a request. Several families of valid inequalities are introduced to strengthen these two formulations. These inequalities are used within branch-and-cut algorithms which have been tested on several instance sets for both the PDPTW and the DARP. Instances with up to eight vehicles and 96 requests (194 nodes) have been solved to optimality. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 49(4), 258–272 2007

Learning Heuristics for the TSP by Policy Gradient

Abstract: The aim of the study is to provide interesting insights on how efficient machine learning algorithms could be adapted to solve combinatorial optimization problems in conjunction with existing heuristic procedures. More specifically, we extend the neural combinatorial optimization framework to solve the traveling salesman problem (TSP). In this framework, the city coordinates are used as inputs and the neural network is trained using reinforcement learning to predict a distribution over city permutations. Our proposed framework differs from the one in [1] since we do not make use of the Long Short-Term Memory (LSTM) architecture and we opted to design our own critic to compute a baseline for the tour length which results in more efficient learning. More importantly, we further enhance the solution approach with the well-known 2-opt heuristic. The results show that the performance of the proposed framework alone is generally as good as high performance heuristics (OR-Tools). When the framework is equipped with a simple 2-opt procedure, it could outperform such heuristics and achieve close to optimal results on 2D Euclidean graphs. This demonstrates that our approach based on machine learning techniques could learn good heuristics which, once being enhanced with a simple local search, yield promising results.