2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Minimizing The Total Number Of Late Multi-task Jobs On
Identical Machines
Hairong Zhao, Purdue University Calumet, 2200 169 Street,
Hammond, IN, 46323, United States,
Lingxiang Li, Haibing Li
We consider scheduling multi-task jobs on identical machines in parallel. Each job
consists of one or more tasks that can be processed by any machine. The tasks of a
job can be processed concurrently. Preemptions are not allowed. Each job has a
release date and a due date. The completion time of a job is the time when all of
its tasks have been completed. We focus on the problem of minimizing the
number of late jobs. We show that while some special cases are solvable, the
general problem is NP-hard and admits no constant approximation algorithm
unless =NP. We then present a framework of a general algorithm for the problem
and derive from it six heuristics whose performance is evaluated by experimental
results.
4 - An Improvement In NSGA II For Resource Constrained Project
Scheduling Problem
Fikri Kucuksayacigil, Iowa State University, 610 Squaw Creek
Drive, Unit 18, Ames, IA, 50010, United States,
Resource constrained project scheduling problem has been extensively studied.
For multi-objective form of this problem, since finding an optimum solution is
nearly impossible, several metaheuristic methods have been proposed and
implemented. Non-dominated sorting genetic algorithm (NSGA II) has been one
of the most effective algorithms in this respect. In this study, we develop a hybrid
simulated annealing / NSGA II algorithm to find more diverge and better quality
results. The results show that our algorithm visits more solutions in the solution
space.
5 - Production Scheduling Of Jobs With Fixed Processing Property
On Parallel Machines
Sangoh Shim, Hanbat National University, Dept of Business
Administration, Deokmyung-Dong, Daejeon, 305-719, Korea,
Republic of,
One of the important things for smart factory is an intelligent production
scheduling, how to schedule jobs effectively and efficiently. This problem is for
scheduling jobs on parallel machines with the fixed processing property in which
a group of specific jobs can be processed on the predetermined machine. Usually,
even though parallel machines can process various types of jobs, fixed processing
are preferred not to deteriorate products’ quality. Also, in this problem, when
changing process of different groups of jobs, operations for changing type of
groups, called as setup, are necessary. To minimize makespan of jobs, several
heuristic algorithms are devised.
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203B-MCC
Simulation and Optimization III
Contributed Session
Chair: Prasanna Kumar Ragavan, Virginia Tech, Durham Hall,
Blacksburg, VA, 24061, United States,
1 - Managing Escalations: Equipment Failure And Response
Capacity Allocation
Marc Christiaan Jansen, PhD Candidate, Cambridge Judge
Business School, Downing College, Regent Street, Cambridge,
CB2 1DQ, United Kingdom,
Nektarios Oraiopoulos, Daniel Ralph
Failure of medical equipment represents a cause of downtime for hospitals and
may lead to life-threatening circumstances for patients. At the onset of such
failure, the scale of the disruption is typically unknown. This paper examines how
contracting decisions between a maintenance service provider and multiple
clients can enable efficient allocation of response capacity under imperfect and
asymmetric information on the true nature of the disruption.
2 - Illusion Of Control In Resource Allocation Decision Making
Howard Charles Ralph, Visiting Assistant Professor, Western
Carolina University, 201 Edgemont Avenue, Liberty, SC,
29657-1110, United States,
Resource allocation decisions drive the managerial function of control and are
basic to business school curricula. Decision problems, deterministic or
probabilistic, seek to equip future managers with mathematical tools for
optimized solutions, and flexibility to operate under uncertainty. But, “illusion of
control” or cognitive biases giving the decision-maker unwarranted confidence,
interferes with learning. An exercise has inexperienced decision-makers prioritize
a set of realistic allocation problems and explores recorded rationales for features
of illusion of control biases.
3 - Flexible High Density Puzzle Storage System
Ehsan Shirazi, West Virginia University, 1204, Van Voorhis Road,
Unit B, Morgantown, WV, 26505, United States,
A puzzle-based storage system has been introduced to replenish and retrieve
items from the top and bottom of a highly dense storage system. Each cell of the
puzzle storage is considered as a grid. Each grid is able to store an item and or to
move items in the south direction. We describe a high density storage system that
can retrieve and replenish items from all sides. A puzzle storage with this
characteristic is a lot more flexible than what has been introduced before. We will
illustrate how this puzzle storage scheme affects replenish and retrieve time based
on different network policies, distributions of replenishing and retrieving items,
and number of free spaces on the puzzle network.
4 - The Use Of Simulation For Evaluating Forecast Models
Sanjeewa Naranpanawe, Sr Analytical Consultant, SAS Institute,
100 SAS Campus Drive, Cary, NC, 27513, United States,
The normal process of evaluating forecast models are by fitting the model using
historical data, evaluating using holdout samples to select the model. However,
this single point evaluation of forecast accuracy may not be good at predicting
how the model is going to perform in the future. This presentation examines how
simulation can be use to evaluate different forecast models.
5 - Adaptive-spline For Integer-order Simulation Optimization
Prasanna Kumar Ragavan, Virginia Tech, Durham Hall,
Blacksburg, VA, 24061, United States,
Raghu Pasupathy, Michael Taaffe
We present Adaptive-SPLINE to solve simulation optimization (SO) problems
where the decision variables are integer-ordered, and the objective function can
only be estimated through “noisy” observations from a simulation. Adaptive-
SPLINE iterates between a line search and an enumeration procedure, and
adaptively determines sampling effort by trading-off stochastic error with
structural error. We will discuss consistency and finite-time performance.
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204-MCC
Joint Session HAS/MSOM-HC: Analytics in
Drug Development
General Session
Chair: Elisa Frances Long, UCLA Anderson School of Management,
Los Angeles, CA, United States,
1 - Continuity In Gatekeepers: Quantifying The Impact Of
Care Fragmentation
Vishal Ahuja, SMU Cox School of Business,
Bradley R Staats,
Care coordination is increasingly being recognized as an criticla aspect of overall
patient care. We attempt to establish a quantitative measure of care coordination
and study its impact on patient health outcomes. Further, we investigate the
mechanism by which coordination affects these outcomes. We use data on
patients with diabetes, a chronic condition.
2 - Flexible FDA Approval Thresholds: A Dynamic Programming
Approach
Taylor Corcoran, University of California-Los Angeles,
, Elisa Frances Long,
Fernanda Bravo
Current FDA approval standards require drug companies to demonstrate the
efficacy of their product by presenting statistically significant results from clinical
trials. Traditionally, this significance level is set to 0.05 or 0.01, but this choice
ignores the complexity of the drug approval process. In particular, the current
approval threshold does not incorporate the severity and prevalence of the
disease being treated, the level of research and development taking place, and the
quantity of existing drugs available for the disease. We develop a continuous time
dynamic programming model to study how the optimal significance level should
depend on characteristics of the drug pipeline.
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