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.

A large neighbourhood search heuristic for the aircraft and passenger recovery problem

Abstract: This paper introduces a large neighbourhood search heuristic for an airline recovery problem combining fleet assignment, aircraft routing and passenger assignment. Given an initial schedule, a list of disruptions, and a recovery period, the problem consists in constructing aircraft routes and passenger itineraries for the recovery period that allow the resumption of regular operations and minimize operating costs and impacts on passengers. The heuristic alternates between construction, repair and improvement phases, which iteratively destroy and repair parts of the solution. The aim of the first two phases is to produce an initial solution that satisfies a set of operational and functional constraints. The third phase then attempts to identify an improved solution by considering large schedule changes while retaining feasibility. The whole process is iterated by including some randomness in the construction phase so as to diversify the search. This work was initiated in the context of the 2009 ROADEF Challenge, a competition organized jointly by the French Operational Research and Decision Analysis Society and the Spanish firm Amadeus S.A.S., in which our team won the first prize.

Boards of Directors, CEO Ownership, and the Use of Non-Financial Performance Measures in the CEO Bonus Plan

Abstract: Manuscript Type: Empirical Research Question/Issue: This study examines the associations between the board of director's choice to integrate non-financial performance measures into the CEO bonus plan and two other governance mechanisms – board independence and CEO ownership – in a sample of publicly traded Canadian firms. Research Findings/Results: The results provide evidence that the use of non-financial performance measures in the CEO bonus plan varies predictably. Growth opportunities are positively associated with the firm's choice to integrate non-financial information into the CEO bonus plan. The results are also sensitive to our proxy for board independence and CEO ownership in firms with high growth opportunities. Theoretical Implications: Agency theory states that any costless performance measure providing incremental information about the agent's effort will improve the efficiency of the contract with the agent. In contrast with most of the literature in this area, which investigates pay-performance sensitivity and governance structure, we examine an important component of pay-for-performance plans used to align and compensate executive actions that might not be reflected in traditional financial performance measures. Practical Implications: This study documents that boards choose performance measures that best reflect the CEO's contribution to firm value, taking into account the firm's monitoring environment. This study therefore has policy implications regarding the need for enhanced disclosure of CEO compensation to improve investor understanding of the alignment between executive pay and firm performance.

Optimal auditing with scoring: theory and application to insurance fraud

Abstract: This article makes a bridge between the theory of optimal auditing and the scoring methodology in an asymmetric information setting. Our application is meant for asurance claims fraud, but it can be applied to many other activities that use the scoring approach. We show that the optimal auditing strategy takes the form of a "Red Flags Strategy" which consists in referring claims to a Special Investigative unit (SIU) when certain fraud indicators are observed. Fraud indicators are classified based on the degree to which they reveal an increasing probability of fraud. This strategy remains optimal even when the investigation policy is budget constrained. Moreover, the auditing policy acts as a deterrence device and we explain why it requires the commitment of the insurer and how it should affect the incentives of SIU staffs. the models is calibrated with data from a large European insurance company. We compute a critical suspicion index for fraud, providing a threshold above which all claims must be audited and we estimate the potential gain that could be derived from the optimal auditing policy. We show that it is possible to improve these results by separating different groups of insureds with different moral costs of fraud. Finally, our results indicate how the deterrence effect of the audit scheme can be taken into account and how it affects the optimal auditing strategy.

Utilizing graph machine learning within drug discovery and development.

Abstract: Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.