2015 Informs Annual Meeting

MB77

INFORMS Philadelphia – 2015

2 - Distributionally Robust Stochastic Optimization using Divergences- A Data Driven Strategy Anand Vidyashankar, Associate Professor, George Mason University, Department of Statistics, Volgeneau School of Engineering, Fairfax, VA, 22030, United States of America, avidyash@gmu.edu, Jie Xu We propose a new paradigm for data-driven distributionally robust stochastic optimization (DRSO). This paradigm integrates existing approaches to decision making under uncertainty with robust and efficient statistical procedures. Specifically, it extends the scope of DRSO by centering the ambiguity sets on density estimates neighborhoods in the space of probability densities. The proposed approaches are transparent, theoretically justified, and accessible to researchers and decision makers. 3 - Some Statistical Perspectives on Optimization under Parameter Uncertainty Henry Lam, Assistant Professor, University of Michigan, 1205 Beal Ave., Ann Arbor, MI, 48109, United States of America, khlam@umich.edu, Jeff Hong We consider approaches to improve the quality of solutions for optimizations under parameter uncertainty, in the case of limited data. We analyze two computationally tractable methods: bootstrap aggregation, or bagging, and Bayes estimator in the decision-theoretic framework. Both are simulation-based schemes that aim to improve the distributional behavior of the optimality gap by reducing its frequency of hitting large values. 4 - Simulation Optimization When Facing Input Uncertainty Enlu Zhou, Assistant Professor, Georgia Institute of Technology, 755 Ferst Drive, NW, Atlanta, GA, United States of America, enlu.zhou@isye.gatech.edu, Xie Wei This talk makes an attempt at the question of what is a good formulation for simulation optimization when there is input uncertainty. We propose a risk formulation of simulation optimization that tries to balance the trade-off between optimizing under the estimated input model and hedging against the risk brought by input uncertainty. Chair: Bisheng Du, Assistant Professor, Ningbo University, 818 Fenghua Road, Ningbo, China, dubisheng@nbu.edu.cn 1 - Supplier Development Investments under Competition Tarun Jain, IIM Bangalore, FPM Office, IIM Bangalore, Banerghatta R, Bangalore, India, tarun.jain@iimb.ernet.in, Jishnu Hazra We model a case, where a buyer is evaluating a new supplier. The buyer allocates some capacity to the incumbent supplier and makes supplier development investments in the entrant supplier. Both incumbent and entrant supplier also exerts improvement investments. We find the optimal investments strategy of these players and the optimal capacity allocation strategy of the buyer. 2 - Analysis and Design of Retail Backrooms and its Impact on Supply Chain Lita Das, Student, MIT, 77 Massachusetts Avenue, E40-286, Cambridge, MA, 02139, United States of America, litadas@mit.edu, Edgar Blanco Backroom space management and supply chain operations are closely linked. We propose a model to optimally manage backroom space and demonstrate the impact on retail supply chain planning and design. 3 - An Evaluation of the Progressive Formulation (PF) through the Modeling of the PCSA Daniel Mota, Researcher, MIT, Rua Jose de Magalhaes 373, Vila Clementino, Sao Paulo, SP, 04026, Brazil, danmota@mit.edu, Roberto Perez-franco Progressive Formulation (PF), an ad-hoc approach recently proposed for formulating a supply chain strategy, has shown potential in applied projects but has never been objectively evaluated. This paper attempts a first evaluation of the merits of the PF approach, by modeling PF as a greedy algorithm and comparing its results against an optimal solution for the same data set. Results are compared in terms of the quality of the solution generated and the amount of information needed to achieve it. MB77 77-Room 300, CC Supply Chain Management VI Contributed Session

4 - Capacity Planning over a Finite Time Horizon with Dual Contracts: An Optimization Approach Ramya Ravi, Student, Indian Institute of Technology, Madras, Chennai, 600036, India, ramya.tcemdu@gmail.com, Haritha Thirrumalai, Rajendran Chandrasekharan, Vivekanandan Srinivasan We consider a supply chain with multiple products manufactured using processes outsourced to suppliers by the manufacturer. Capacity planning contracts (i.e.,fixed charge and option, with setup/order costs) are considered. We present an algorithm to determine the capacity investments, considering the total supply chain profit over a finite time horizon. 5 - Optimal Fulfillment Decisions in a Capacitated Online Retailing System with Different Leadtimes Bisheng Du, Assistant Professor, Ningbo University, 818 Fenghua Road, Ningbo, China, dubisheng@nbu.edu.cn Online retailers offer more options, like flexible delivery, pricing, etc. We consider an inventory system where customers may have different lead-times under the retailer’s capacity. The retailer has a primary warehouse and many regional warehouses. The regional warehouses fulfill the customers’ orders by on-hand inventory, or the primary warehouse will fulfill orders with longer time. Our intension is to analyze the performance of the capacitated retailing system under the multistage setting. MB78 78-Room 301, CC Shipping and Transportation for Supply Chains Contributed Session Chair: Sherif Masoud, Operations Research Analyst, RockTenn, 3950 Shackleford Rd., Duluth, United States of America, smasoudphd@gmail.com 1 - Port Logistics and the Voice of the Customer Richard Monroe, Visiting Assoc Prof, Longwood Univ, College of Business and Econ, 201 High Street, Farmville, VA, 23120, United States of America, rickmon53@gmail.com Customers are key stakeholders for logistics services through the various modes which include seaports, trucking, rail and inland ports. Primary customers such as manufacturers and retailers are highly dependent on the smooth flow of freight through the logistics system. Customer expectations for logistics services have received limited attention in previous research. This paper will utilize a combination of approaches to explore the voice of the customer in the port logistics setting. 2 - A New Formulation for the Cyclic Inventory Routing Problem and the Solution Method Zhe Liang, Professor, Tongji University, No. 1239, Siping Road, Shanghai, 200092, China, liangzhe@tongji.edu.cn, W. Art Chaovalitwongse We study a cyclic inventory routing problem (IRP). The traditional exact methods for IRP use an arc-based model, in which a variable represents a possible vehicle flow between a pair of customers. We develop a Dantzig-Wolfe reformulation for the arc-based model. To solve the problem efficiently, we develop a set of valid inequalities and a column generation algorithm. Computational results show that the new model can obtain near optimal solutions to very large test cases in a reasonable time. 3 - Robust Supply Chain System under Yield Uncertainty Samir Alsobhi, PhD Candidate, Wichita State University, 11328 E Pine Meadow Ct, Wichita, KS, 67206, United States of America, samiralsobhi@gmail.com, Krishna Krishnan Products are often damaged in transit.These damages are stochastic in nature.To minimize the impact of damage,the selection of routes should consider not only the expected damage but also the variability of damage.In this research,the first model is of the supply chain network in order to minimize total cost,which consists of product cost and transportation cost while considering multiple routes and products.In the second model,the concept of robust design has been applied to minimize damage. 4 - A Floating Price Contract for the Ocean Freight Industry Ruina Yang, Xi’an Jiaotong University, No. 28 West Xianning Road, Xi’an, China, rnyang@mail.xjtu.edu.cn We propose a floating price policy to address the shipper default issue. Specifically, the shipper has to make a tradeoff between not fulfilling all committed capacity to secure a lower spot price in the low season and purchasing the capacity at a higher floated price in the high season. The results reveal that the non-capacity commitment contract serves both parties’ best interests under a tight-capacity market, while the floating price contract is the most effective in an over-capacity market.

198