69热视频

Welcoming a new MMA advisory member

The Master鈥檚 of Management in Analytics program is pleased to announce that Richard Hines, Senior Director, Data and Digital Enablement at Air Canada, has joined the program鈥檚 Advisory Board.

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Shoppers hit the stores with sights set on bargains

Black Friday and Cyber Monday have become synonymous with frenzied shoppers looking to get the best deals on their favorite products.

In light of their growing appeal, Professor Saibal Ray charts the development of these major shopping events, particularly within the Canadian market.

Insights into the changing face of retail

On one of the biggest shopping days of the year, Professor Saibal Ray, Academic Director of the Bensadoun School of Retail Management joins Global News Morning to explore the unique opportunities that the school will bring for students and researchers interested in the evolving retail sector.

Open for Business: 69热视频鈥檚 Bensadoun School of Retail Management dedicated to the future of retail

At a time of significant transformation in the retail industry worldwide, the newly opened Bensadoun School of Retail Management (BSRM) at 69热视频 will act as a hub in the heart of Montreal for students, researchers and practitioners to work collaboratively towards addressing the host o

The Decision Rule Approach to Optimization under Uncertainty: Methodology and Applications

Authors:听Angelos Georghiou, Daniel Kuhn, Wolfram Wiesemann

Publication:听Computational Management Science, Forthcoming

Abstract:听

Robust Dual Dynamic Programming

Authors:听Angelos Georghiou, Angelos Tsoukalas, Wolfram Wiesemann

Publication:听Operations Research, Forthcoming

Abstract:听

Multi-stage robust optimization problems, where the decision maker can dynamically react to consecutively observed realizations of the uncertain problem parameters, pose formidable theoretical and computational challenges. As a result, the existing solution approaches for this problem class typically determine suboptimal solutions under restrictive assumptions. In this paper, we propose a robust dual dynamic programming (RDDP) scheme for multi-stage robust optimization problems. The RDDP scheme takes advantage of the decomposable nature of these problems by bounding the costs arising in the future stages through lower and upper cost to-go functions. For problems with uncertain technology matrices and/or constraint right-hand sides, our RDDP scheme determines an optimal solution in finite time. If also the objective function and/or the recourse matrices are uncertain, our method converges asymptotically (but deterministically) to an optimal solution. Our RDDP scheme does not require a relatively complete recourse, and it offers deterministic upper and lower bounds throughout the execution of the algorithm. We demonstrate the promising performance of our algorithm in a stylized inventory management problem.

MMA assembles accomplished advisory board

This fall, the Masters of Management in Analytics (MMA) kicked off its first Advisory Board meeting to discuss the future directions of the MMA curriculum, which will benefit its inaugural cohort.

The MMA Advisory Board comprises senior industry leaders from a wide variety of educational and skills backgrounds who have excelled in their given fields.

Optimizing Foreclosed Housing Acquisitions in Societal Response to Foreclosures

Authors:听Senay Solak, Armagan Bayram, Mehmet Gumus,听Yueran Zhuo

Publication: Operations Research, Forthcoming

Abstract:

A dramatic increase in U.S. mortgage foreclosures during and after the great economic recession of 2007-2009 had devastating impacts on the society and the economy. In response to such negative impacts, non-profit community development corporations (CDCs) throughout the U.S. utilize various resources, such as grants and lines of credit, in acquiring and redeveloping foreclosed housing units to support neighborhood stabilization and revitalization. Given that the cost of all such acquisitions far exceeds the resources accessible by these non-profit organizations, we identify socially optimal policies for CDCs in dynamically selecting foreclosed properties to target for potential acquisition as they become available over time. We evaluate our analytical results in a numerical study involving a CDC serving a major city in the U.S, and specify social return based thresholds defining selection decisions at different funding levels. We also find that for most foreclosed properties CDCs should not offer more than the asking price, and should typically consider overbidding only when the total available budget is low. Overall, comparisons of optimal policies with historical acquisition data suggest a potential improvement of around 20% in expected total impacts of the acquisitions on nearby property values. Considering a CDC with annual fund availability of $4 million for investment, this corresponds to an estimated additional value of around $280,000 for the society.

Angelos Georghiou appointed Associate Editor of Energy Systems

Angelos Georghiou,听Assistant Professor in Operations Management, was recently听appointed Associate Editor of Energy Systems - Optimization, Modeling, Simulation, and Economic Aspects.

The journal Energy Systems presents mathematical programming, control, and economic approaches towards energy systems related topics, and is especially relevant in light of the major worldwide challenges confronting humanity in this century.

In celebration of two 69热视频 greats

Fifty years ago, 69热视频 had the good fortune of welcoming Professors Henry Mintzberg and Morty Yalovsky on board.

On August 30, members of the 69热视频 community, along with family and friends, celebrated their contributions by establishing two teaching awards in their honour.

A Smart-City Scope of Operations Management

Authors:听Wei Qi and Zuo-Jun Max Shen

Publication: Production and Operations Management, Forthcoming

Abstract:

We are entering an era of great expectations towards our cities. The vision of 鈥渟mart city鈥� has been pursued worldwide to transform urban habitats into superior efficiency, quality and sustainability. This phenomenon prompts us to ponder what role the scholars in operations management (OM) can assume. In this essay, we express our initial thoughts on expanding OM to the smart-city scope. We review smart-city initiatives of governments, industry, national laboratories and academia. We argue that the smart-city movement will transition from the tech-oriented stage to the decision-oriented stage. Hence, a smart city can be perceived as a system scope within which planning and operational decisions are orchestrated at the urban scale, reflective of multidimensional needs, and adaptive to massive data and innovation. The benefits of studying smart-city OM are manifold and significant: contributing to deeper understanding of smart cities by providing advanced analytical frameworks, pushing OM knowledge boundaries (such as data-driven decision making), and empowering the OM community to deliver much broader impacts than before. We discuss several research opportunities to embody these thoughts, in the interconnected contexts of smart buildings, smart grid, smart mobility and new retail. These opportunities arise from the increasing integration of systems and business models at the urban scale.

Masters of Management in Analytics: Meet the inaugural class

The Desautels Faculty of Management welcomed its inaugural cohort of Masters of Management in Analytics (MMA) students on August 3.

The thirty-five students (14 female, 21 male) originate from 12 nations across five continents and have an average entering CGPA of 3.40, bringing diverse cultural perspectives and strong academic credentials.

Designing Risk鈥怉djusted Therapy for Patients with Hypertension

Authors:听Manaf Zargoush, Mehmet Gumus, Vedat Verter,听Stella S. Daskalopoulou

Publication: Production and Operations Management, Forthcoming

Abstract:

Limited guidance is available for providing patient鈥恠pecific care to hypertensive patients, although this chronic condition is the leading risk factor for cardiovascular diseases. To address this issue, we develop an analytical model that takes into account the most relevant risk factors including age, sex, blood pressure, diabetes status, smoking habits, and blood cholesterol. Using the Markov Decision Process framework, we develop a model to maximize expected quality鈥恆djusted life years, as well as characterize the optimal sequence and combination of antihypertensive medications. Assuming the physician uses the standard medication dose for each drug, and the patient fully adheres to the prescribed treatment regimen, we prove that optimal treatment policies exhibit a threshold structure. Our findings indicate that our recommended thresholds vary by age and other patient characteristics, for example (1) the optimal thresholds for all medication prescription are nonincreasing in age, and (2) the medications need to be prescribed at lower thresholds for males who smoke than for males who have diabetes. The improvements in quality鈥恆djusted life years associated with our model compare favorably with those obtained by following the British Hypertension Society's guideline, and the gains increase with the severity of risk factors. For instance, in both genders (although at different rates), diabetic patients gain more than non鈥恉iabetic patients. Our sensitivity analysis results indicate that the optimal thresholds decrease if the medications have lower side鈥恊ffects and vice versa.

Quality at the Source or at the End? Managing Supplier Quality Under Information Asymmetry

Authors:听Mohammad E. Nikoofal, Mehmet Gumus

Publication: Manufacturing & Service Operations Management, Vol. 20, No. 3, Summer 2018

Abstract:

Supply Diagnostic Incentives under Endogenous Information Asymmetry

Authors:听Mohammad E. Nikoofal,听Mehmet Gumus

Publication: Production and Operations Management, Forthcoming

Abstract:

This paper develops a dyadic supply chain model with one buyer who contracts the manufacturing of a new product to a supplier. Due to the lack of experience in manufacturing, the extent of supply risk is unknown to both the buyer and supplier before the time of contract. However, after the contract is accepted, the supplier may invest in a diagnostic test to acquire information about his true reliability, and use this information when deciding on a process improvement effort. Using this setting, we identify both operational and strategic benefits and costs of diagnostic test. Operationally, it helps the supplier to take the first-best level of improvement effort, which would increase efficiency of the total supply chain. Strategically, it enables the buyer to reduce the agency costs associated with implementing process improvement on the supplier. Besides these benefits, diagnostic test increases the degree of information asymmetry along the supply chain. This in turn provides the supplier with proprietary information, whose rent would be demanded from the buyer in equilibrium. Benefit-cost analysis reveals two key factors in determining the value of diagnostic test: (i) degree of endogenous information asymmetry between supply chain firms, and (ii) the relative cost of diagnostic test with respect to process improvement cost. Our results indicate that when both are high, the mere presence of diagnostic test can result in less reliable supply chain. This implies that when incentives are not properly aligned, information asymmetry amplified due to diagnostic test neutralizes all its benefits.