2015 Informs Annual Meeting

WD02

INFORMS Philadelphia – 2015

3 - Optimizing Military Medevac Dispatching: A Multi-objective Markov Decision Process Model

5 - Scheduling with Convex Resource Allocation, Learning Effects, Job Deterioration, and Setup Times Hossein Soroush, Professor, Kuwait University, Dept. of Stat. & Opns. Res., POB 5969, Safat, 13060, Kuwait, h.soroush@ku.edu.kw We study single machine scheduling problems with convex resource dependent processing times, job learning and deterioration, and setup times. Polynomial time algorithms are proposed to find the optimal sequences and the optimal resource allocations that either minimize a composite function of some criteria subject to the constraint that the total resource cost does not exceed a given amount, or minimize the total resource cost such that the composite function does not surpass a specific limit. WD03 03-Room 303, Marriott Inventory Management for Supply Chains II Contributed Session Chair: Yang Bo, PhD, University of Texas at Dallas, 800 West Campbel Rd., Richardson, TX, United States of America, yxb120630@utdallas.edu 1 - A Three-Level Supply Chain with Up and Down-Stream Trade Credit Periods Linked to Ordered Quantity Roshanak Akram, university of Tennessee, Knoxville, 727 Front Avenue, Apartment 9, Knoxville, TN, 37902, United States of America, roshanak@utk.edu, Rupy Sawhney An EPQ model from manufacturer prospective in a 3-echelon supply chain is developed to minimize total cost considering that a trade credit is offered to the manufacturer, if his order quantity is more than a predetermined value. Customers are permitted to pay off manufacturer in a same or contrary credit period. Defective items can be produced and all would become perfect after rework. Optimal solutions for production lot size and backlogged shortage are obtained based on convex optimization. 2 - A Note on Integrality in Deterministic and Stochastic Inventory Models Yang Bo, PhD, University of Texas at Dallas, 800 West Campbell Rd., Richardson, TX, United States of America, yxb120630@utdallas.edu The “integrality” question for dynamic optimization models of inventory control asks if there exists an integral optimal policy, given integral initial inventory levels, capacities and demand realizations. For single-product, stochastic-demand problems in multi-echelon assembly systems, the answer is yes (Chen et al. 2013). For single-product, multi-echelon distribution and assembly-distribution systems, integrality holds under deterministic demands, but fails to hold under stochastic demands. 3 - A Joint Replenishment Problem with Dissimlar Items

Benjamin Grannan, Virginia Military Institute, Lexington, VA, United States of America, grannanbc@vmi.edu, Nathaniel Bastian, Lawrence Fulton, Mort Webster, Paul Griffin We present an infinite horizon Multi-Objective Markov Decision Process model to optimize sequential resource allocation decision-making in the military medical evacuation of wartime casualties, which consists of identifying how many/which air assets to dispatch in response to a casualty event and which mobile hospital to transport the patients to. These sequential decisions are complicated due to uncertain casualty demand, distinguishable mobile hospitals, and multiple conflicting objectives. 4 - Stochastic Multi-objective Auto-optimization for Resource Allocation Decision-making Nathaniel Bastian, Pennsylvania State Univeristy, 362 Leonhard Building, University Park, PA, 16823, United States of America, nathaniel.bastian@fulbrightmail.org, Lawrence Fulton, Benjamin Grannan, Tahir Ekin, Hyojung Kang, Paul Griffin The military health system is a large, centrally-funded and controlled health system that is challenged to provide healthcare delivery and health services at certain quality and workload levels with a fixed amount of input resources. We present a stochastic multi-objective auto-optimization model to help health system decision-makers automatically re-allocate input resources across hospitals for difference levels of resource uncertainty as to optimize overall system performance. Chair: Hossein Soroush, Professor, Kuwait University, Dept. of Stat. & Opns. Res., POB 5969, Safat, 13060, Kuwait, h.soroush@ku.edu.kw 1 - Heuristics for Scheduling Parallel Machines to Minimize Weighted Squared Tardiness Jeffrey Schaller, Professor, Eastern Connecticut State University, 83 Windham St., Department of Business Administration, Willimantic, CT, 06226, United States of America, schallerj@easternct.edu, Jorge Valente This paper considers a problem in which there is a set of jobs to be sequenced on identical parallel machines. Each job has a weight and the objective is to sequence the jobs to minimize total weighted squared tardiness. Several heuristics are presented and are tested on randomly generated problems of various numbers of jobs, numbers of machines, due date tightness and due date ranges. The results show that some heuristics finds good solutions in a minimal amount of processing time. 2 - Investigation of Algorithms for No-Wait Flowshops Ali Allahverdi, Professor, Kuwait University, P.O. Box 5969, Safat, Kuwait, ali.allahverdi@ku.edu.kw The m-machine no-wait flowshop scheduling problem of minimizing total completion time is addressed where makespan is constrained to be less than a certain value and where setup times are considered as separate from processing times. Several new algorithms are proposed and shown to be efficient. 3 - Synchronized Scheduling in Hybrid Flowshop with Dynamic Customer Order Arrivals We consider a synchronized scheduling of dynamic arriving customer orders in a hybrid flowshop. The objective is to achieve synchronized production that all products of a same order are simultaneously completed so that they are shipped in a batch. We decompose the dynamic problem into a series of periodic sub- problems. A mixed integer programming model is formulated for sub-problem which is solved by genetic algorithm. Numerical studies obtain several significant findings. 4 - Simulation Based Approximate Dynamic Programming for Stochastic Resource-constrained Project Scheduling Yasin Gocgun, Assistant Professor, Istanbul Kemerburgaz University, Mahmutbey Mah., Dilmenler Cad.No: 26, Istanbul, Turkey, yasin.gocgun@kemerburgaz.edu.tr, Archis Ghate, Mahshid Parizi We study dynamic stochastic resource-constrained project scheduling problems. We formulate these problems as Markov decision processes (MDPs). Since the state and actions spaces of the underlying MDP grow exponentially in parameters such as the number of project types and queue capacity, we pursue an approximate dynamic programming (ADP) approach. We compare the performance of the ADP technique against the myopic method. Jian Chen, The University of Hong Kong, LG 108, Composite Building, Hong Kong, Hong Kong - PRC, justinchenjian@gmail.com, George Q. Huang WD02 02-Room 302, Marriott Scheduling IV Contributed Session

Linda Li, Student, University of Alabama, 1105 17th Street, 5102A, Tuscaloosa, AL, 35401, United States of America, lczy1985@163.com, Charles Schmidt

We study a joint replenishment problem with dissimilar items and demand of Poisson process. A class of continuous review policies is considered. The class is defined by three characteristics. Finding the optimal policy out of the class is demonstrated to be a combinatorial optimization problem. The findings reveal that the joint inventory position (a vector) is actually a subordinated continuous time Markov Chain. A new methodology is developed to solve this type of model.

WD04 04-Room 304, Marriott Business Applications II Contributed Session

Chair: James Ang, Research Advisor, The Logistics Institute Asia Pacific, National University of Singapore, #04-01 21 Heng Mui Keng Terrace, Singapore, 119613, Singapore, bizangsk@nus.edu.sg 1 - A Framework for Customer Analytics on Massive Data Sets Ming Xie, IBM, Diamond Bld, ZGC Software Park, Beijing, China, xieming@cn.ibm.com, Changrui Ren, Tianzhi Zhao, Miao He, Jian Xu, Zhen Huang, Yuhui Fu With the rapid growth of the amount of data accumulated, companies are facing opportunities and challenges for efficiently analyzing these massive data to generate insights and help business. Here we present a framework that leverages big data foundation to provide efficient customer analytical models designed to support the key customer service scenarios in banks. This framework has been verified in real cases.

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