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.

Analysis of an exact algorithm for the vessel speed optimization problem

Abstract: Increased fuel costs together with environmental concerns have led shipping companies to consider the optimization of vessel speeds. Given a fixed sequence of port calls, each with a time window, and fuel cost as a convex function of vessel speed, we show that optimal speeds can be found in quadratic time. © 2013 Wiley Periodicals, Inc. NETWORKS, 2013

Distributive justice, procedural justice, and psychological distress: the moderating effect of coworker support and work autonomy

Abstract: Recent research has demonstrated that the perception of injustice at work may increase psychological health-related problems. The purpose of this study is to examine the moderating effect of coworker support and work autonomy on the relationships between both distributive and procedural justice and psychological distress. Results, on the basis of responses to questionnaires given to 248 prison employees, show that coworker support moderates the relationships between both forms of justice and psychological distress. Specifically, these relationships are weakened when employees benefit from a high level of coworker support. Furthermore, work autonomy moderates the relationship between procedural justice and psychological distress but not the relationship between distributive justice and psychological distress. Thus, procedural injustice is less likely to increase psychological distress when the level of work autonomy is high.

Ethics, Diversity Management, and Financial Reporting Quality

Abstract: This article proposes and empirically tests a theoretical framework incorporating Reidenbach and Robin’s (J Bus Ethics 10(4):273–284, 1991) conceptual model of corporate moral development. The framework is used to examine the relation between governance and business ethics, as proxied by diversity management (DM), and financial reporting quality, as proxied by the magnitude of earnings management (EM). The level of DM and governance quality are measured in accordance with the ratings of Jantzi Research (JR), a leading provider of social and governance research for institutional investors. This DM score is part of an index developed by JR that investment managers use to integrate DM criteria into their investment decisions. As expected, a negative relation between corporate DM development and financial reporting quality is found while controlling for other factors known in the literatures on governance and accounting choices to affect earnings quality. Despite some caveats presented in conclusion, this study contributes to the ethics, governance, and financial reporting literatures by studying the dynamics between governance and ethics in the prevention of EM.

Probabilistic Logic Neural Networks for Reasoning

Abstract: Knowledge graph reasoning, which aims at predicting missing facts through reasoning with observed facts, is critical for many applications. Such a problem has been widely explored by traditional logic rule-based approaches and recent knowledge graph embedding methods. A principled logic rule-based approach is the Markov Logic Network (MLN), which is able to leverage domain knowledge with first-order logic and meanwhile handle uncertainty. However, the inference in MLNs is usually very difficult due to the complicated graph structures. Different from MLNs, knowledge graph embedding methods (e.g. TransE, DistMult) learn effective entity and relation embeddings for reasoning, which are much more effective and efficient. However, they are unable to leverage domain knowledge. In this paper, we propose the probabilistic Logic Neural Network (pLogicNet), which combines the advantages of both methods. A pLogicNet defines the joint distribution of all possible triplets by using a Markov logic network with first-order logic, which can be efficiently optimized with the variational EM algorithm. Specifically, in the E-step, a knowledge graph embedding model is used for inferring the missing triplets, while in the M-step, the weights of the logic rules are updated according to both the observed and predicted triplets. Experiments on multiple knowledge graphs prove the effectiveness of pLogicNet over many competitive baselines.