HEC Montréal

About: HEC Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Vehicle routing problem & Corporate governance. 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.

The Challenges of Hospital Supply Chain Management, from Central Stores to Nursing Units

Abstract: The practice of patient care is supported by a range of healthcare supply chain management activities, also referred to by many as healthcare logistics. Improving the efficiency of these activities can provide opportunities for healthcare institutions and health systems to increase the quality of care and reduce costs. Hospitals represent a key link in the supply chain and face their own particular challenges due the complexity of their internal supply chain. The distribution of medical supplies to nursing units represents a vital component of the internal hospital supply chain. Indeed, all doctors, nurses, and clinical support staff deal with and depend on supplies in one way or another and are thus affected by their accessibility and availability on a daily basis. The methods most often used to distribute supplies to nursing units range from requisition-based systems that depend on clinical staff involvement to methods where the hospital’s central stores manage inventory and replenishment. In the latter category, common storage and distribution method options include par level systems and automated cabinets, among others, as well as the two-bin/kanban replenishment method, which has been identified as a best practice. For its part, RFID technology has further enhanced this leading practice and introduced the possibility of proactively managing supplies by triggering replenishment rounds based on a range of user-defined criteria. Beyond its expanded benefits, this innovation opens the door to a large number of research avenues in the areas of capacitated vehicle routing problems, inventory optimization, and simulation.

A Branch-and-Price Algorithm for the Multidepot Vehicle Routing Problem with Interdepot Routes

Abstract: This paper proposes a column generation algorithm for the multidepot vehicle routing problem with interdepot routes. This problem is an extension of the multidepot vehicle routing problem in which the vehicles are allowed to stop at intermediate depots along their routes to replenish. The problem can be modeled as a set covering problem in which the variables are rotations corresponding to feasible combinations of routes. We consider two pricing subproblems to generate rotations. The first one generates rotations directly by solving an elementary shortest path problem with resource constraints on a modified version of the original customer-depot network. The second one exploits the relationship between the sets of routes and rotations but results in a model with many columns. We discuss some issues related to solving this second pricing subproblem by column generation and we introduce an alternate approach to alleviate these difficulties. We show through computational experiments that the second pricing m...

Straussian Grounded Theory Method: An Illustration

Abstract: This paper demonstrates the applicationis an appreciation of Straussian Grounded Theory Grounded Theory method in conducting research in complex settings where parameters are poorly defined. It provides a detailed illustration on how this method can be used to build an internationalization theory. To be specific, this paper exposes readers to the behind-the-scene work to develop a theory on the internationalization of small and medium-sized enterprises based in transition economies. It describes each step from sampling to coding and then to theory formation, explaining the rationale each step of the way. The readers can therefore see how a theory took shape and develop from raw data to refined theoretical propositions/hypotheses.

Forecasting (Aggregate) Demand for U.S. Commercial Air Travel

Abstract: We analyze whether it is better to forecast air travel demand using aggregate data at (say) a national level or whether one should aggregate forecasts derived for individual airports using airport-specific data. We compare the U.S. Federal Aviation Administration’s (FAA) practice of predicting the total number of passengers using macro economic variables with an equivalently specified AIM (aggregating individual markets) approach. The AIM approach outperforms the aggregate forecasting approach in terms of its out-of-sample air travel demand predictions for different forecast horizons. Variants of AIM, where we restrict the coefficient estimates of some explanatory variables to be the same across individual airports, generally dominate both the aggregate and the AIM approaches. The superior out-of-sample performance of these so-called quasi-AIM approaches depend on the trade-off between heterogeneity and estimation uncertainty. We argue that the quasi-AIM approaches efficiently exploit the heterogeneity across individual airports without suffering from as much estimation uncertainty as the AIM approach.