Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

WA52

3 - Coalition Analysis of Basic Hierarchical Graph Model in Solving Climate Change Disputes Shawei He, Assistant Professor, Nanjing University of Aeronautics and Astronautics, Nanjing, China Coalition in hierarchical conflicts is studied in basic hierarchical graph model (BHGM). A BHGM consists of two local graph models (LGMs), containing a common decision maker (CDM) in both LGMs and two local decision makers (LDMs), each of which plays in one LGM. Coalition between CDM and an LDM, and between two LDMs, are discussed. Theorems indicate that transition from an equilibrium to another only takes place jointly by CDM and an LDM. An example of disputes over achieving emission goals between the national and provincial governments in China are investigated, suggesting that agreements on achieving stricter emission goals by a province can be reached if being subsidized by the national government. 4 - Cost of Information Sharing Under Group Purchasing Wenli Peng, Université Catholique de Louvain, Voie du Roman Pays 34 bte L1.03.01, Louvain La Neuve, 1348, Belgium, Gilles Merckx, Philippe Chevalier, Aadhaar Chaturvedi This paper investigates how information sharing dimension affects OEMs’ motivations towards group purchasing, specifically in industries characterized by market demand and technology level uncertainties. Under Cournot competition, we find that group purchasing is preferred by OEMs when product technology strongly affects market demand, and that preference for group purchasing would depend on product substitutability, market demand variability and supplier rebate when influence of the product technology is low. We further find that group purchasing can benefit both the OEMs and the consumers. n WA51 North Bldg 231B Production & Scheduling Contributed Session Chair: Elham Taghizadeh, Wayne State University, 23794 Saravilla Dr, Detroit, MI, 48035, United States 1 - A Mixed Integer Linear Programming Formulation for Permutation Flowshop Lot Streaming Problems Ramakrishna Govindu, University of South Florida, 8350 N. Tamiami Tr, Sarasota, FL, 34243, United States, Anurag Agarwal MxN flowshop problems are known to be NP-hard. Lot streaming problems, being more complex than flowshop problems, are typically solved using heuristics and meta-heuristics. It is a well-known fact that problem formulations play a crucial role determining in solution times for integer and mixed integer programming problems. Given the computational power and advanced solvers available these days, is there any value in solving lot streaming as an MILP problem? We propose an MILP formulation, conduct experiments, and present the results. 2 - The Cutting Stock Problem with Diameter Conversion in Construction Industry Deniz Altinpulluk, Baskent University, Ankara, Turkey, Haldun Sural One-dimensional cutting stock problem has been widely used for reinforcement rebar steel in the construction industry. Diameter size of a rebar is determined by structural designer to provide tensile strength to the structure and it can be changed if cross-section area per concrete area stays constant. We study the problem that decides diameter size and required number of rebar providing tensile strength and cutting patterns to minimize usage of raw material. It differs from classical cutting stock as it is not known how many pieces should be cut until diameter sizes are chosen. We propose a Reflect Arc-Flow Formulation to solve the problem and provide our computational results. 3 - Integrated Production Planning and Scheduling in the Multistage Production System with Dynamic Forecast Updates Hakeem-Ur Rehman, Shanghai Jiao Tong Univesity, Shanghai, 54000, China, Guohua Wan, Zhan Yang We study the multilevel multistage lotsizing and scheduling problem with demand information updating. We employ the Martingale Model for Forecast Evolution to model the evolving demand over the time with demand information updating and build a shortfall-based chance constraints MIP model using hybrid period approach. To solve the problem, three heuristic algorithms based on the relax-and-fix method within rolling horizon framework are developed. The results of the computational experiments show that Heuristic 1 performs better than Heuristic 2 and 3 for all the problem instances.

4 - Integrated Optimization of Production Scheduling and Preventive Maintenance Planning In the Flexible Job Shop. Baoru Zhou, Tsinghua University, Beijing, 100084, China, Changchun Liu, Li Zheng This study addresses the problem of integrating production scheduling and preventive maintenance planning in the flexible job shop. A model aimed to minimize the total tardiness is formulated. To solve the model, we propose a heuristic and an adapted Genetic Algorithm. Finally, extensive numerical experiments are conducted to test the performance of the proposed methods. 5 - A Biobjective Production Planning and Scheduling Model Dealing with Reworking the Perishable Items in a Parallel Machine System Elham Taghizadeh, Wayne State University, Detroit, MI, 48035, United States, Farshid Evazabadian, Setareh Torabzadeh, Abdollah Mohammadi Production systems could struggle with defective products due to human error, machine break-down, and imperfect production system. Reworking is one strategy to deal with this issue. Rework planning involves the production planning and scheduling of the defected items along the master production planning. This paper presents a multi-objective mathematical model to determine how the work and rework must be scheduled in a parallel machine system for perishable products. The model objective functions are the Min of cost and makespan. A linear model is developed and numerical experiments are based on a case study from the literature for biopharmaceutical industry. n WA52 North Bldg 231C Practice – Integer Programming & Applications I Contributed Session Chair: Reed Harder, Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, United States 1 - Least Cost Energy Optimization: Pakistan Power Sector Analysis Raza Ali Rafique, Assistant Professor, Lahore University of Management Sciences, Sector ‘U’, DHA, Lahore Cantt., Lahore, 54792, Pakistan In our study, the optimal energy-mix for power generation is sought. We propose a mixed integer linear programming (MILP) model for minimizing the power generation infrastructure cost considering indigenous energy resources of Pakistan. The proposed optimization tool can assist policy makers for strategic planning and development of future energy-mix for power generation in Pakistan. 2 - A Weighting Local Search Algorithm for the Constrained Binary Quadratic Program Ryutaro Matsumoto, Osaka University, Osaka, Japan, Shunji Umetani, Hiroshi Morita We develop a 2-flip neighborhood local search algorithm for the constrained binary quadratic program (CBQP) that incorporates an incremental evaluation of solutions and an adaptive control of penalty weights. The proposed method detects the special type of constraints and specifies a suitable neighborhood search. Computational results for a variety of combinatorial optimization problems show that the proposed method achieves comparable results to the specially tailored algorithms for them. 3 - A Software Tool for Interfacing with Linear Programming Solvers Zachary Steever, University at Buffalo, Buffalo, NY, United States, Chase Murray Note: We are finalizing a provisional patent for this tool, so we can’t disclose details yet. We will update the abstract to be more descriptive well in advance of the conference. We present a new tool to help researchers more efficiently interact with application programming interfaces (APIs) that are often provided by integer programming solvers. This tool streamlines a researcher’s typical workflow by automating some steps of the code-writing process. The talk will feature a demonstration of the new software. A roadmap of future enhancements to the tool will be presented, followed by a discussion on how the OR community can influence the incorporation of new functionality into the software. 4 - A Novel Lattice-based Douglas Rachford Splitting to Solve Convex Optimization Problems over Integers Shuvomoy Das Gupta, Thales Canada, Research & Technology, 105 Moatfield Drive, Toronto, ON, M3B 0A4, Canada Convex optimization problems over integer decision variables have a wide range of applications in transportation, management science, computer science and engineering. In this study, we combine results from lattice theory and monotone operator methods to construct a novel lattice-based Douglas-Rachford splitting algorithm to solve convex optimization problems over integers. The algorithm has several desirable properties, namely: its convex steps are parallelizable, and its nonconvex projection step is reducible into projection onto a lattice in a potentially much smaller dimension. We also establish sufficient conditions for the convergence of the algorithm to an optimal solution.

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