2016 INFORMS Annual Meeting Program

MC30

INFORMS Nashville – 2016

2 - Closing A Supplier’s Energy Efficiency Gap: The Role Of Assessment Assistance And Procurement Commitment Quang Dang Nguyen, University of Minnesota, Minneapolis, MN, 55455, United States, nguy1762@umn.edu Karen Donohue, Mili Mehrotra This paper analyzes the Energy Efficiency (EE) investment decisions of a capital- constrained manufacturer that competes with an alternate supplier for the business of a large industrial buyer. Through a series of game theoretic models, we analyze the impact of EE assessment assistance and procurement commitment on the supplier’s EE investment. 3 - Mind The Gap: Coordinating Energy Efficiency And Demand Response Eric Webb, Kelley School of Business, Indiana University, Bloomington, IN, 47405, United States, ermwebb@indiana.edu Owen Wu, Kyle D Cattani Traditionally, energy demand-side management techniques, such as energy efficiency (EE) and demand response (DR), are evaluated in isolation. We examine the interactions between long-term EE upgrades and daily DR participation at an industrial firm. We find that EE and DR act as substitutes in terms of reduction of peak electricity demand, and the long-studied energy efficiency gap between firm-optimal and societal-optimal levels of EE is smaller when DR is considered. We suggest three approaches to reducing the energy efficiency gap, including an original suggestion that relies upon the interactions between EE and DR.

well as demand non-stationarity and substitution under stockouts. The optimal allocation of bikes across stations to maximize ridership is determined using a dynamic program. Our study provides insights on the relationship between the allocation of bikes and ridership, and the value of incorporating non-stationarity, real-time inventory information, and station substitution. MC31 202C-MCC Operational Issues in Agriculture Sponsored: Manufacturing & Service Oper Mgmt, iFORM Sponsored Session Chair: Onur Boyabatli, Singapore Management University, 50 Stamford Road 04-01, Singapore, 178899, Singapore, oboyabatli@smu.edu.sg 1 - Designing Contracts And Sourcing Channels To Create Shared Value Joann de Zegher, Stanford University, jfdezegher@stanford.edu, Hau Leung Lee, Dan Andrei Iancu We study contract and channel design to create mutual benefit in decentralized agricultural value chains, where suppliers bear costs of new technologies while benefits accrue primarily to buyers. We provide insights to companies seeking to incorporate responsible sourcing strategies while also creating economic value - a concept called creating shared value. We identify that the technology’s cost elasticity drives whether switching sourcing channel, changing contract structure, or adopting an integrated change is necessary. Using a dataset of farms in Argentina we estimate that the our mechanism could increase average supply chain profit by 6.9% while realizing environmental benefits. 2 - Third-wave Coffee: Sourcing And Pricing A Specialty Product Under Uncertainty Shahryar Gheibi, Siena College, Loudonville, NY, United States, sgheibi@siena.edu, Burak Kazaz, Scott Webster Motivated by an emerging phenomenon in the coffee industry—third-wave coffee—we study an agricultural supply chain where a firm sells a finished product which requires processing an agricultural product as input. In order to target the quality-sensitive segment of consumers, the firm (processor) offers specialty coffee by engaging in Direct Trade which in turn leads to exposure to supply risk. Our study provides insights into the main driving forces that influence the sourcing and pricing decisions of the processor in a specialty-coffee supply chain. 3 - New Results For Bounds In Newsvendor Problems Saurabh Bansal, Penn State University, sub32@psu.edu We discuss new results for the bounds on the newsvendor problem in the agribusiness context and quantify the value of decisions based on these bounds over some commonly used approaches. MC32 203A-MCC Scheduling VI Contributed Session Chair: Matthew J Liberatore, Villanova University, 800 Lancaster Avenue, Villanova, PA, 19085, United States, matthew.liberatore@villanova.edu 1 - Job Shop Scheduling With Convex Costs Reinhard Burgy, GERAD and Polytechnique Montreal, GERAD – HEC Montréal, 3000, ch. de la Côte-Sainte-Catherine, Montreal, QC, H3T 2A7, Canada, reinhard.burgy@gerad.ca We address an extension of the classical job shop scheduling problem with a generic convex cost objective. This objective makes it possible to model, for example, convex tardiness costs and convex (intermediate) holding costs. It is, to the best of our knowledge, the first time such a generic nonlinear and nonregular objective is considered in job shop scheduling. We give a disjunctive graph formulation and develop a local search heuristic. Numerical results support the validity of our approach. 2 - Heuristics For Lot Streaming In Flow Shop Scheduling Anurag Agarwal, Professor, University of South Florida, Information Systems and Decision Sciences, Coll of Business, Sarasota, FL, 34243, United States, agarwala@usf.edu, Ramakrishna Govindu We develop heuristic solutions to generate efficient schedules for a lot streaming scheduling problem within the flowshop environment. We formulate this problem as a multiobjective problem that attempts to strike a balance between makespan and cost of handling the sublots. We consider transfer times, sequence dependent lot setup times, as well as sublot setup times.

MC30 202B-MCC

New Business Models In Transportation Sponsored: Manufacturing & Service Oper Mgmt Sponsored Session Chair: Karan Girotra, INSEAD, Fontainebleau, France, karan.girotra@insead.edu

1 - Service Region Design For Urban Electric Vehicle Systems Long He, National University of Singapore, longhe@nus.edu.sg, Ho-Yin Mak, Ying Rong, Zuo-Jun Max Shen We consider the service region design problem for electric vehicle sharing systems. We then develop a model that incorporates both customer adoption behavior and fleet operations under spatially-imbalanced and time-varying travel patterns. To address the uncertainty in adoption patterns, we employ a distributionally-robust optimization framework. Applying this approach to the case of Car2Go’s service, with real operations data, we address a number of planning questions. 2 - Dynamic Type Matching Ming Hu, University of Toronto, Toronto, ON, Canada, ming.hu@rotman.utoronto.ca, Yun Zhou We study a dynamic multi-period assignment/transportation problem, in which an intermediary dynamically matches demand and supply of heterogeneous types and the unmatched will incur waiting or holding costs, and be carried over to the next period with abandonments. This problem also applies to many emerging settings in the sharing economy. The Monge sequence discovered by Gaspard Monge in 1781 was introduced to solve a deterministic, balanced transportation problem in a greedy fashion. We propose modified Monge conditions that are sufficient and robustly necessary for structural priority properties for the dynamic, stochastic and unbalanced transportation problem. 3 - Algorithmic Support For Bike-sharing System Operations At Motivate David B Shmoys, Cornell University, david.shmoys@cornell.edu Daniel Freund, Shane Henderson, Nanjing Jian Bike-sharing systems (BSSs) have become increasinglly prevalent as part of the urban landscape, and are common even in smaller towns. For larger cities, these systems give rise to a number of interesting logistical problems to support their operations. A group at Cornell has been embedded within the support structure for Motivate, which operates BSSs in several major US cities. We will give an update on a number of the models and algorithmic advances that have been implemented to support operations at Motivate, and in particular, for Citibike in NYC. 4 - Maximizing Ridership In Bike Sharing Systems Using Empirical Data And Stochastic Models Vinayak Deshpande, University of North Carolina, Chapel Hill, NC, 27599, United States, Vinayak_Deshpande@kenan-flagler.unc.edu Pradeep Kumar Pendem We analyze the optimal allocation of bikes in a network of stations to improve ridership under non-stationarity demand and station substitution. We utilize large datasets on trips, real time inventory information at stations, and distances between stations. Our demand model captures both bike pickups and dropffs, as

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