Informs 2015

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INFORMS Philadelphia – 2015
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WC02
02-Room 302, Marriott
Scheduling III
Contributed Session
Chair: Pravin Tambe, Dr., RCOEM, 49, Parate Nagar, Near H.B.Estate,
Sonegaon, Nagpur, MS, 440025, India,
1 - Hybrid Flow Shop Batch Scheduling Problem with a
Bi-Criteria Objective
Rasaratnam Logendran, Professor, Oregon State University,
School of Mech, Indust, and Mfgr. Engr, 204 Rogers Hall,
Corvallis, OR, 97331-6001, United States of America,
Omid Shahvari
We address a batch scheduling problem in hybrid flow shops, wherein one or
more stages have unrelated-parallel machines. The objective is to minimize the
weighted sum of total weighted completion time and total weighted tardiness. Job
release times and machine availability times are assumed to be dynamic. The
performance of search algorithms, based on tabu search, is evaluated by
developing a mixed-integer linear programming model in order to find the best
algorithm, if any, for this problem.
2 - Flowshop Batch Processing Problem with Different
Batches on Machines
Nasser Salmasi, Sharif University of Technology, Department of
Industrial Engineering,, Sharif University of Technology, Tehran,
Te, 11365, Iran, Hossein Nick Zinat Matin,
Mohsen Varmazyar
We approach the multi-stage flowshop batch processing problem with
minimization of makespan. Each batch on each machine has both the maximum
number of jobs in each batch and the batch capacity limitations at the same time.
The size of batches on machines can be different. The jobs can be assigned to
different batches on different stages. We propose a general mathematical model
and a metaheuristic algorithm based on particle swarm optimization (PSO) to
solve the problem.
3 - Adjustable Robust Optimization for Handling Uncertainty in
Process Scheduling
Nikolaos Lappas, Graduate Student, Carnegie Mellon University,
DH3122, 5000 Forbes Avenue, Pittsburgh, PA, 15212, United
States of America, , Chrysanthos Gounaris
We develop an Adjustable Robust Optimization (ARO) framework to address
uncertainty in Process Scheduling. Unlike RO, which results in a here-and-now
solution, ARO results in a policy that is a function of parameter realizations. We
derive the ARO counterpart, propose suitable decision-dependent uncertainty
sets, and describe a cutting-plane-based solution approach. ARO results in
considerably less conservative solutions, and unlike RO, can address also
instances with zero-wait tasks.
4 - Robust Scheduling of Parallel Machines under Uncertainty
Selçuk Güren, Abdullah Gul University, Dept. of Industrial
Engineering, Kayseri, Turkey, ,
Seyma Bekli
We consider stochastic scheduling of a set of jobs subject to processing time
variability on identical parallel machines subject to random breakdowns. We
develop a scenario-based integer-programming model that can handle small-size
problems without machine breakdowns. We then propose a heuristic algorithm
that can also handle large problems with machine breakdowns. Our
computational experiments indicate that the performance of the proposed
algorithms is promising.
5 - A Simulated Annealing Approach for Scheduling Jobs on Identical
Parallel Machines
Pravin Tambe, Dr., RCOEM, 49, Parate Nagar, Near H.B.Estate,
Sonegaon, Nagpur, MS, 440025, India, ,
Makarand Kulkarni
This paper presents a scheduling problem on identical parallel machines. A hybrid
approach using simulated annealing combined with backward-forward heuristic is
used for scheduling. The objective is to minimize the total penalty cost of jobs
allocation on all the machines. Both earliness and tardiness penalties are
considered. The computational experiment results for different problem sizes have
been presented.
WC03
03-Room 303, Marriott
Inventory Management for Supply Chains I
Contributed Session
Chair: Alireza Sheikhzadeh, University of Arkansas, 4207 Bell
Engineering Center, Fayetteville, Ar, 72701, United States of America,
1 - Multi-Period Dynamic Inventory Classification: Models
and Applications
Dorothy Liu Yang, University of Missouri - St. Louis, 240 JCPN,
One University Blvd, Saint Louis, MO, 63121,
United States of America, Haitao Li
This talk presents a multi-period inventory classification model that aims to help
company optimize profitability when facing nonstationary, stochastic demand.
The benefits of multi-period, dynamic inventory policies over single-period, static
inventory policies are examined. We offer managerial insights about how various
purchasing and inventory parameters/settings impact the optimal dynamic policy
and performance measures.
2 - Service-level Estimation in Inventory System Simulations with
Model Uncertainty
Canan Gunes Corlu, Assistant Professor, Boston University,
808 Commonwealth Avenue, Boston, MA, 02215,
United States of America, Alp Akcay
We consider the simulation of a stochastic inventory system in the presence of
input model uncertainty — the input distributions are unknown and there is only
a limited amount of historical input data available for input distribution
estimation. We develop an algorithm that estimates the mean service level of the
inventory system accounting for the model uncertainty without making any
assumptions on the form of the input model(s) that can represent the data on
hand.
3 - Service Level and Contract Design with Shelf Space
Dependent Demand
Peeyush Mehta, Indian Institute of Management Calcutta, NF3/4,
IIM Caclcutta, Kolkata, 700104, India,
R K Amit
We consider supply chain coordination problem when demand is influenced by
the amount of inventory displayed on the shelves. First, we determine an optimal
service level for shelf space dependent demand. Next, we design individually
rational contracts that coordinate the supply chain when the retailer faces
inventory level dependent demand.
4 - Incorporating Order Crossover Information in Service-oriented
Base Stock Policy Decisions
Alan Pritchard, University of Maryland, Robert H. Smith Scholl of
Business, Van Munching Hall, College Park, MD, 20742, United
States of America, , Dean Chatfield
Order crossover, when orders arrive in a different sequence than the sequence in
which they were placed, is a prevalent issue for modern supply chains. We utilize
a hybrid discrete/continuous simulation model to investigate multiple methods of
including order crossover information in the safety stock decision for an (R,S)
inventory system with a service level target. Issues related to effective lead times,
the normal approximation, and the appropriate protection period are
investigated.
5 - Segmentation Methods for Large-scale Multi-echelon Repairable
Parts Provisioning Systems
Alireza Sheikhzadeh, University of Arkansas,
4207 Bell Engineering Center, Fayetteville, Ar, 72701,
United States of America, , Manuel Rossetti
Large-scale manufactured systems are an extremely crucial part of the
infrastructure and worldwide competitiveness. Innovative approaches will
significantly reduce the size and complexity of problems through the use of
segmentation methods. In this research, grouping SKUs based on a no-backorder
stocking policy (NBS) considerably reduced the size of problem with the lowest
penalty cost. This procedure represents a unique segmentation model that has not
been considered in the literature before.
WC03