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

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

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

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

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...