Mark S Daskin

University of Michigan, Industrial and Operations Engineering, Faculty Member

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I am a faculty member in the Industrial and Operations Engineering Department of the University of Michigan. Prior to joining Michigan in January 2010, I was a faculty member at Northwestern University, originally in Civil Engineering and then in Industrial Engineering and Management Sciences.My research focuses on the application of operations research techniques to supply chain problems, facility location decisions, and healthcare operations.I have served as the chair of the IOE department at Michigan and the IEMS department at Northwestern. I am also a former editor-in-chief of both IIE Transactions and Transportation Science. Finally, I served as president of INFORMS in 2006.
Phone: 1-734-764-9410
Address: Industrial and Operations Engineering
University of Michigan
1205 Beal Avenue
Ann Arbor, MI 48109
USA

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Location-inventory planning models: capacity issues and solution algorithms

The first capacitated location-inventory model we introduce in this dissertation assigns each ret... more

Planning for Disruption in Supply Chain Networks

Location Analysis and Network Design

Operations, Logistics and Supply Chain Management, 2018

This chapter begins with a basic taxonomy of facility location models. This is followed by the fo... more This chapter begins with a basic taxonomy of facility location models. This is followed by the formulation of five classic facility location models: the set covering model, the maximum covering model, the p-median model, the fixed charge location model and the p-center problem. Advanced: Computational results on a new set-covering problem instance with 880 nodes representing 880 population centers in the contiguous United States are provided and a few counter-intuitive results are outlined. This is followed by a state of the art discussion of multi-objective problems in location analysis and the importance of multiple objectives in designing distribution networks. Models that integrate inventory planning into facility location modeling are then outlined. Finally, the chapter ends with a discussion of reliability in facility network planning.

An integer L-shaped algorithm for the integrated location and network restoration problem in disaster relief

Transportation Research Part B: Methodological, 2021

The high impact of these disasters has attracted the attention of many scholars in operations res... more The high impact of these disasters has attracted the attention of many scholars in operations research. There has been a significant interest in using operations research in humanitarian applications to reduce loss of life and alleviate human suffering caused by disasters. Kara and Savaşer (2017) state that location problems constitute the majority of problems studied in the relief logistics literature published between 2007 and 2017. In particular, the warehouse location and inventory prepositioning problem has been extensively studied in recent years. Being prepared for potential disaster scenarios enables government agencies and humanitarian organizations to respond effectively once the disaster hits. In the literature, the two-stage stochastic programming models are commonly employed to develop preparedness plans before anticipated disasters. These models can be very difficult to solve as the complexity increases by several sources of uncertainty and interdependent decisions. In this study, we propose an integer L-shaped algorithm to solve the integrated location and network restoration model, which is a two-stage stochastic programming model determining the number and locations of the emergency response facilities and restoration resources under uncertainty. Our algorithm accommodates the second-stage binary decision variables which are required to indicate undamaged and restored roads of the network that can be used for relief distribution. Our computational results show that our algorithm outperforms CPLEX for the larger number of disaster scenarios as the solution time of our algorithm increases only linearly as the number of scenarios increases.

Mark S Daskin

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