2015 Informs Annual Meeting

TD26

INFORMS Philadelphia – 2015

TD26 26-Room 403, Marriott Production and Scheduling I Contributed Session Chair: Srimathy Mohan, Associate Professor, Arizona State University, Department of Supply Chain Management, Tempe, AZ, United States of America, srimathy@asu.edu 1 - Weekly Production Planning on the Basis of Average Value-at-Risk by Shapley Value Nobuyuki Ueno, Hiroshima University of Economics, 5-37-1 Gion Asaminami-ku, Hiroshima, Japan, ueno@pu-hiroshima.ac.jp, Hiroshi Morita, Koji Okuhara Under demand uncertainty,they used stock-out ratio for estimating the risk. In this presentation, we propose a formulation for weekly production planning problem that reflects the AVaR (Average value-at-risk) for weighing tail risk and a solution by Shapley value. The characteristics of the solution procedure is proved. It has the features that it does not require strict probability distribution of stock- out and it enables an extension to the case where demand for each period is correlated. 2 - A Generalized Dantzig-Wolfe Decomposition Algorithm for Mixed Integer Programming Problems We propose a generalized Dantzig-Wolfe decomposition algorithm for mixed integer programming. By generating copy variables, we can reformulate the original problem to have a diagonal structure which is amendable to the Dantzig- Wolfe decomposition. We apply the proposed algorithm to multi-level capacitated lot sizing problem and production routing problem. Rigorous computational results show that our algorithm provides a tighter bound of the optimal solution than all the existing methods. 3 - The Impact of Postponement Practices on the Lot-sizing Decisions of a Wine Bottling Plant Sergio Maturana, Professor, Pontificia Universidad Catolica de Chile, Vicuna Mackenna 4860, Santiago, Chile, smaturan@ing.puc.cl, Mauricio Varas Export-focused wineries face a difficult problem when planning their bottling lines due to the number of different products they have to bottle and label. A way of reducing misallocation due to demand variability is by postponing the labeling process. We propose two MIP planning models that support tactical lot-sizing decisions. We tested both models in a rolling horizon framework, under different conditions of capacity tightness, horizon length, and demand uncertainty and we report the results. 4 - Scheduling of Maximizing Total Job Value with Machine Availability Constraint Eun-Seok Kim, Middlesex University, The Burroughs, London, NW4 4BT, United Kingdom, e.kim@mdx.ac.uk, Joonyup Eun We study a single machine scheduling problem of maximizing total job value with machine availability constraint. The value of each job is given as a non-increasing step function of its completion time. We develop a branch-and-bound algorithm and a heuristic algorithm for the problem. Finally, we perform computational experiments showing that the developed algorithms provide efficient and effective solutions. Xue Lu, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, United Kingdom, X.Lu7@lse.ac.uk, Zeger Degraeve

2 - Evaluating Lignocellulosic Biomass Supply Chains Considering a Multi-objective of Optimizing Cost Burton English, Professor, The University of Tennessee, 2621 Morgan Hall, Knoxville, TN, 37922, United States of America, benglish@utk.edu, James Larson, Edward Yu, Jia Zhong A switchgrass supply chain that considers the optimization of cost, GHG emissions and soil erosion for a cellulosic biofuel plant is developed. Using an augmented epsilon constraint multi-objective optimization model and a compromise solution method, along with high-resolution spatial data the optimal placement of feedstock supply chains can be estimated. Spatial characteristics, including land coverage and infrastructure availability, are crucial to both the cost and the environmental results. TD28 28-Room 405, Marriott Dynamic Matching Markets Cluster: Auctions Invited Session Chair: John Dickerson, CMU, 9219 Gates-Hillman Center, Pittsburgh, PA, 15213, United States of America, dickerson@cs.cmu.edu 1 - Global Kidney Exchange afshin.nikzad@gmail.com, Mohammad Akbarpour, Alvin Roth In some countries, many patients die after a few weeks of diagnosis mainly because the costs of kidney transplantation and dialysis are beyond the reach of most citizens. We analyze the two proposals in which patients with financial restrictions who have willing donors participate in kidney exchange without paying for surgery. Our proposals can save thousands of patients, while substantially decreasing the average dialysis costs; in particular, we prove that they are “self-financing” 2 - Matching with Stochastic Arrival Neil Thakral, Harvard, 1805 Cambridge Street, Cambridge, MA, United States of America, nthakral@fas.harvard.edu We study matching in a dynamic setting, with applications to public-housing allocation. Objects of different types that arrive stochastically over time must be allocated to agents in a queue. When objects share priorities over agents, we propose an efficient, envy-free, and strategy-proof mechanism. The mechanism continues to satisfy these properties if and only if the priority relations are acyclic. Estimated welfare gains over existing housing-allocation procedures exceed $5000 per applicant. 3 - Dynamic Kidney Exchange with Heterogeneous Types Maximilien Burq, Student, MIT, 77 Massachusetts Avenue, Cambridge, MA, 02139, United States of America, mburq@mit.edu, Itai Ashlagi, Vahideh Manshadi, Patrick Jaillet Kidney exchange programs face growing number of highly sensitized patients. We develop an online model that models such heterogeneity, and we prove that having some easy-to-match patients in the pool greatly reduces waiting times both in the presence of bilateral matching and chain matching. We provide simulations showing that some prioritizing leads to improved overall efficiency. 4 - Competing Dynamic Matching Markets Sanmay Das, WUSTL, One Brookings Dr, CB 1045, St. Louis, MO, 63130, United States of America, sanmay@wustl.edu, John Dickerson, Zhuoshu Li, Tuomas Sandholm We extend a framework of dynamic matching due to Akbarpour et al. to characterize outcomes in cases where two rival matching markets compete. One market matches quickly while the other builds thickness by matching slowly. We present analytical and simulation results, both in general and for kidney exchange, demonstrating that rival markets increase overall loss compared to a single market that builds thickness. Afshin Nikzad, Stanford University, 37 Angell Court, Apt 116, Stanford, CA, 94305, United States of America,

TD27 27-Room 404, Marriott Applications of Multi-objective Optimization Sponsor: Multiple Criteria Decision Making Sponsored Session

Chair: Matthias Ehrgott, Professor, Department: Management Science, Lancaster University, The Management School, Lancaster, 00, LA1 4YX, United Kingdom, m.ehrgott@lancaster.ac.uk 1 - A Hybrid Decision Making Approach for Multi-Objective Infrastructure Planning Hana Chmielewski, North Carolina State University, Campus Box 7908, Raleigh, NC, 27695, United States of America, htchmiel@ncsu.edu, Ranji Ranjithan A hybrid approach using evolutionary computation and dynamic programming is used to optimize investments and operational decisions in a water supply case study system. Solutions are categorized by network centralization metrics, and analyzed with respect to multiple planning objectives.

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