Mark S Daskin | University of Michigan (original) (raw)

Papers by Mark S Daskin

The first capacitated location-inventory model we introduce in this dissertation assigns each ret... more The first capacitated location-inventory model we introduce in this dissertation assigns each retailer to a single distribution center. We formulate this model as a nonlinear integer program in which the objective function is neither concave nor convex. Feasible solutions for this ...

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.

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.

INFOR: Information Systems and Operational Research, 2017

ABSTRACT Hard capacity constraints have been used for decades in facility location modelling and ... more ABSTRACT Hard capacity constraints have been used for decades in facility location modelling and planning. However, such constraints are unrealistic as a variety of operational tools can be used to extend capacity in the short term. To address this, the Inventory-Modulated Capacitated Location Problem (IMCLP) uses inventory as a method of mitigating the hard capacity constraints, but enforces single sourcing. In this paper, we examine a cyclic, day-specific allocation approach to assigning demand sites to processing facilities in the IMCLP. This enables the model to develop a day-of-the-week allocation policy that considers day-to-day variations in the daily processing capacity levels of a set of candidate processing facilities and/or systematic day-to-day demand variations. We demonstrate that allowing demands at a particular site to be allocated to multiple processing facilities in such a manner can be a cost-effective operational tool.

International Journal of Production Research, 2017

In this paper, we present an extension of the classic p-median facility location model. The new f... more In this paper, we present an extension of the classic p-median facility location model. The new formulation allows the user to trace the trade-off between the demand-weighted average distance (the traditional p-median objective) and the range in assigned demand. We extend the model to incorporate additional constraints that significantly reduce the computation time associated with the model. We also outline a genetic algorithm-based approach for solving the problem. The paper shows that significant reductions in the range in assigned demand are possible with relatively minor degradations in the average distance metric. The paper also shows that the genetic algorithm does very well at identifying the approximate trade-off curve. The model and algorithms were tested on real-life data-sets ranging in size from 33 nodes to 880 nodes.

Patient Education and Counseling, 2018

This study examined whether kidney transplant recipients' post-transplant goals and expectations ... more This study examined whether kidney transplant recipients' post-transplant goals and expectations align with those perceived by their healthcare providers. Methods: Posttransplant goals and expectations across four domains were assessed via a descriptive survey of healthcare providers (N=72) and kidney transplant recipients (N=476) at the University of Michigan from March 23-October 1, 2015. Demographic and transplant-related data were collected via a retrospective review of medical records, and survey responses were compared using Chi-square tests, Wilcoxon two-sample tests, and logistic regression. Results: Patients expressed higher quality of life (mean Neuro-QOL T-score 60.2 vs. 52.7), were less likely to report that they were currently experiencing complications (11% vs. 24%), and anticipated their transplants to last longer (median 25 vs. 15 years) and to live longer (median 80 vs. 71 years) than providers expected for their typical patient. However, provider perceptions of patients' future ability to feel well, perform daily activities and work were significantly higher than those expressed by patients (all p<0.05). Conclusion: Kidney transplant patient and provider expectations differ in significant ways. Practice Implications: Identified areas of discordance may provide opportunities for patients and providers to better evaluate treatment option tradeoffs in post-transplant clinical interactions.

Page 1. SERVICE SCIENCE Mark S. Daskin Page 2. SERVICE SCIENCE Page 3. Page 4. SERVICE SCIENCE Ma... more Page 1. SERVICE SCIENCE Mark S. Daskin Page 2. SERVICE SCIENCE Page 3. Page 4. SERVICE SCIENCE Mark S. Daskin Department of Industrial and Operations Engineering University of Michigan Ann Arbor, MI A JOHN WILEY & SONS, INC., PUBLICATION Page 5. ...

This report is in microfiche form. Two models of supertanker lightering operations are developed.... more This report is in microfiche form. Two models of supertanker lightering operations are developed. The first is a set of linked queueing models while the second employs a five-dimensional static space to model the process using the theory of Markov processes. Both models estimate delays to supertankers and to lightering vessels as functions of super-tanker arrival rate, the number of lightering vessels employed, the lightering vessel load and discharge times and transit times, and the number of berths used for lightering. The models are compared, and the input assumptions and output predictions are tested against observed data. The use of the models as planning tools is illustrated.

We study a strategic facility location problem under uncertainty. The uncertainty associated with... more We study a strategic facility location problem under uncertainty. The uncertainty associated with future events is modeled by defining alternative future scenarios with probabilities. We present a new model which minimizes the expected regret with respect to an endogenously selected subset of worst-case scenarios whose collective probability of occurrence is exactly 1-α. We demonstrate the effectiveness of this new approach by comparing it to the “α-reliable p-median Minimax regret” model and by presenting computation results for large-scale problems. We also present a heuristic, which involves solving a series of α-reliable Mean-excess regret sub-problems, for the α-reliable p-median Minimax regret model.

Transportation Science, 1982

The major components of delay to rail cars in passing through yards are waiting for classificatio... more The major components of delay to rail cars in passing through yards are waiting for classification and connection to an appropriate outbound train. This paper proposes queuing models for each of these components which provide expressions for both the mean and variance of delay times. The models are then used in an example application to draw inferences regarding the effectiveness of alternative strategies for dispatching trains between yards.

Volume 33, Number 5, October 2006, 2020

For the third time in the five years since Sept. 11, 2001, I find myself flying on Sept. 11. In 2... more For the third time in the five years since Sept. 11, 2001, I find myself flying on Sept. 11. In 2003, I was en route to Singapore. Last year, I flew to Baltimore for a memorial service for Chuck ReVelle, a close colleague of many of us. Today, I am flying home from Boston after spending the last three days with my mother who underwent emergency surgery last week. Perhaps all this flying on Sept. 11 is chance. Certainly, I did not plan any of these flights for this particular day. On the other hand, maybe these flights are my quiet way of saying to those who would do us harm that they have not won.

We present the Stochastic R-Interdiction Median Problem with Fortification (S-RIMF). This model o... more We present the Stochastic R-Interdiction Median Problem with Fortification (S-RIMF). This model optimally allocates defensive resources among facilities to minimize the worst-case impact of an intentional disruption. Since the extent of terrorist attacks and malicious actions is uncertain, the problem deals with a random number of possible losses. A max-covering type formulation for the S-RIMF is developed. Since the problem size grows very rapidly with the problem inputs, we propose pre-processing techniques based on the computation of valid lower and upper bounds to expedite the solution of instances of realistic size. We also present heuristic approaches based on heuristic concentration-type rules. The heuristics are able to find an optimal solution for almost all problem instances considered. Extensive computational testing shows that both the optimal algorithm and the heuristics are very successful at solving the problem. A comparison of the results obtained by the two methods ...

Michael Lim• Achal Bassamboo• Sunil Chopra• Mark S. Daskin Department of Business Administration,... more Michael Lim• Achal Bassamboo• Sunil Chopra• Mark S. Daskin Department of Business Administration, University of Illinois, Urbana-Champaign, IL 61820, USA • mlim@illinois.edu Department of Managerial Economics and Decision Sciences, Northwestern University, Evanston, IL 60208, USA • a-bassamboo@kellogg.northwestern.edu; s-chopra@kellogg.northwestern.edu Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA • msdaskin@umich.edu

IISE Transactions, 2019

Supply chain disruptions have caused hundreds of shortages of medically-necessary drugs since 201... more Supply chain disruptions have caused hundreds of shortages of medically-necessary drugs since 2011. Once a disruption occurs, the industry is limited in its ability to adapt, and improving strategic resiliency decisions is important to preventing future shortages. Yet, many shortages have been of low-margin, generic injectable drugs, and it is an open question whether resiliency is optimal. It is also unknown what policies would be effective at inducing companies to be resilient.

Journal of Graduate Medical Education, 2016

Background As resident “index” procedures change in volume due to advances in technology or reli... more Background As resident “index” procedures change in volume due to advances in technology or reliance on simulation, it may be difficult to ensure trainees meet case requirements. Training programs are in need of metrics to determine how many residents their institutional volume can support. Objective As a case study of how such metrics can be applied, we evaluated a case distribution simulation model to examine program-level mediastinoscopy and endobronchial ultrasound (EBUS) volumes needed to train thoracic surgery residents. Methods A computer model was created to simulate case distribution based on annual case volume, number of trainees, and rotation length. Single institutional case volume data (2011–2013) were applied, and 10 000 simulation years were run to predict the likelihood (95% confidence interval) of all residents (4 trainees) achieving board requirements for operative volume during a 2-year program. Results The mean annual mediastinoscopy volume was 43. In a simul...

Location Science, 1997

Facility location problems are inherently strategic in nature. One approach to dealing with the u... more Facility location problems are inherently strategic in nature. One approach to dealing with the uncertainty associated with future events is to define alternative future scenarios. Planners then attempt to optimize: (1) the expected performance over all future scenarios, (2) the expected regret, or (3) the worst-case regret. Both the expected performance and the expected regret approaches assume that the planner can associate probabilities with the scenarios, while optimizing the worst-case regret obviates the need for these probabilities. Worst-case regret planning can, however, be driven by a scenario with a very small likelihood of occurrence. We present a new model that optimizes the worst-case performance over a set of scenarios that is endogenously selected from a broader exogenously specified set. The selection is based on the scenario probabilities. The new model is formulated and computational results on a moderately sized problem are presented. Model extensions arc discussed.