2015 Informs Annual Meeting

TB76

INFORMS Philadelphia – 2015

TB76 76-Room 204C, CC Advances in Simulation-based Optimization II Sponsor: Simulation Sponsored Session Chair: Enlu Zhou, Assistant Professor, Georgia Institute of technology, 755 Ferst Drive, NW, Atlanta, United States of America, enlu.zhou@isye.gatech.edu 1 - A Set Approach to Simulation Optimization with Probabilistic Branch and Bound Hao Huang, PhD Candidate, University of Washington, Industrial and Systems Engineering, Seattle, WA, 98195-2650, United States of America, haoh7493@uw.edu, Zelda Zabinsky Probabilistic Branch and Bound (PBnB) is a partition-based random search simulation optimization algorithm for stochastic problems. PBnB determines a set of solutions through an estimated bound on the performance. For single objective problem, PBnB approximates a desirable level set with quantile estimation. In a multiple objective circumstance, PBnB considers a bound of the closeness to the efficient frontier and approximates the Pareto optimal set of solutions. 2 - A Model-based Approach to Multi-objective Optimization Joshua Hale, Graduate Student, Georgia Institute of Technology, We develop a model-based algorithm for the optimization of multiple objective functions that can only be assessed through black-box evaluation. The algorithm iteratively generates candidate solutions from a mixture distribution over the solution space and updates the mixture distribution based on the sampled solutions’ domination count such that the future search is biased towards the set of Pareto optimal solutions. We demonstrate the performance of the proposed algorithm on benchmark problems. 3 - Simulation Optimization: Review and Exploration Chun-hung Chen, George Mason University, 4400 University Drive, MS 4A6, SEOR Dept, GMU, Fairfax, VA, 22030, United States of America, cchen9@gmu.edu, Edward Huang, Jie Xu, Loo Hay Lee Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. We will review some recent developments. We will also discuss how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems. 4 - MO-MO2TOS for Multi Objective Multi Fidelity Simulation Optimization Loo Hay Lee, National University of Singapore, Department of Industrial & Systems, Engineering, Singapore, iseleelh@nus.edu.sg, Giulia Pedrielli, Chun-hung Chen, Ek Peng Chew, Haobin Li In simulation–optimization, low fidelity models can be particularly useful. However, we need to account for their inaccuracy while searching for the optimum. In 2015, Xu et al. proposed MO2TOS, which exploits multiple fidelities to improve the simulation optimization procedure. We extend the approach proposing MO–MO2TOS for the multi-objective case, using the concepts of non–dominated sorting and crowding distance. Several interesting insights specific to the multi-objective case are drawn. 755 Ferst Drive, NW, Atlanta, GA, Atlanta, GA, 30332, United States of America, jhale32@gatech.edu, Enlu Zhou

2 - Flexibility Analysis on a Supply Chain Contract using a Parametric Linear Programming Model Eric Longomo, PhD student, University of Portsmouth, Lion Gate

Building, Lion Terrace, Hampshire, Portsmouth, PO1 3HF, United Kingdom, eric.longomo@port.ac.uk, Xiang Song, Djamila Ouelhadj, Chengbin Chu

This study considers a multi-period Quantity Flexibility contract between a car manufacturer (buyer) and an external parts supplying company. The buyer -in concert with the supplier- aims to develop a policy –at strategic level, that determines the optimal nominal order quantity and variation rate underpinning the contract. The feasibility and convexity of the proposed LP model are examined. Simulations are carried out to evaluate the theoretical results. 3 - Assigning Non-Fixed Parts of a Delivery Area to Fixed Tours Serviced by Electric Vehicles Sarah Ubber, RWTH Aachen University, Kackertstrafle 7, Aachen, Germany, ubber@dpor.rwth-aachen.de We consider last mile distribution where a delivery area is operated by different tours. Parts of this area are serviced by fixed tours in a fixed sequence every day. Other parts are not assigned to fixed tours. To respond e.g. to variable battery ranges or to fluctuations in demand, it is useful to reassign daily the non-fixed parts to the tours, whereby the assignment must not significantly alter the usual delivery sequence. We have developed a model and a heuristic for solving this problem. 4 - Asset Allocation in the Industrial Gas Bulk Supply Chain Leily Farrokhvar, Virginia Tech, 250 Durham Hall (0118), Blacksburg, VA, 24061, United States of America, leily@vt.edu, Kimberly Ellis We study an asset allocation problem in a vendor managed inventory system of an industrial gas distribution network where customer demands vary over time. The objective is to determine the preferred size of bulk tanks to assign to customer sites to minimize recurring gas distribution costs and initial tank installation costs while accommodating customers’ time varying demand. The problem is modeled as a mixed-integer program and then solved using a periodically restricting heuristic approach. TB78 78-Room 301, CC Planning and Scheduling in Energy Applications Contributed Session Chair: Yanyi He, Senior Scientist, IBM, 1001 E Hillsdale Blvd, Foster City, Ca, 94404, United States of America, heyanyidaodao@gmail.com 1 - Stochastic and Robust Optimization of the Scheduling and Market Involvement for an Energy Producer Ricardo Lima, KAUST, Thuwal, Thuwal, Saudi Arabia, ricardo.lima@kaust.edu.sa, Sabique Langodan, Ibrahim Hoteit, Antonio Conejo, Omar Knio We will present three optimization methods based on stochastic programming, robust optimization, and a hybrid method for the scheduling and market involvement for an electricity producer. This producer operates a system with thermal, hydro, and wind sources. The wind power and the electricity prices are uncertain. The methods are implemented using parallel optimization runs. The computational performance, scheduling results, and the impact of risk management are presented and discussed. 2 - A Two-Echelon Wind Farm Layout Planning Model Huan Long, City University of Hong Kong, Room 601, Nam Shan Estat,, Hong Kong, China, hlong5-c@my.cityu.edu.hk, Zijun Zhang In this paper,a two-echelon layout planning model is proposed to determine the optimal wind farm layout to maximize its expected power output.In the first echelon,a grid composed of identical cells is utilized to model the wind farm while the cell center is the potential slot.In the second echelon, the model for determining the optimal coordinate in a grid cell is formulated.The comparative analysis between the two-echelon planning model and the traditional grid/coordinate models is conducted. 3 - Demand Side Participation for a Major Consumer in a Co-optimized Electricity and Reserve Markets Mahbubeh Habibian, Miss, University of Auckland, 6A-Short St, Auckland Central, Auckland, 1010, New Zealand, mhab735@aucklanduni.ac.nz, Golbon Zakeri, Anthony Downward The paper probes demand side participation for a large consumer through demand response and offering in interruptible load reserve. Our model is a bi- level optimization problem that embeds the dispatch model, where electricity and reserve are co-optimized, as the lower level and the profit maximization problem for the consumer (over 2 sets of supply functions) as the upper level. The objective function is transformed into piecewise linear form via utilizing a new interpretation of offer stacks.

TB77 77-Room 300, CC Logistics I Contributed Session

Chair: Leily Farrokhvar, Virginia Tech, 250 Durham Hall (0118), Blacksburg, VA, 24061, United States of America, leily@vt.edu 1 - Analysis of a New Dual-Command Operation in Puzzle-Based Storage Systems with Block Movement Hu Yu, PhD Student, University of Science and Technology of China, Number 96, JinZhai Road, HeFei, China, yuhu0421@mail.ustc.edu.cn, Yugang Yu Dual-command operation jointly performing storage and retrieval requests has been widely discussed in classical warehouse systems, but has been rarely studied in puzzle-based storage systems with block movement. We analytically derive the travel time of completing dual requests that randomly locate in the system. Comparison results with traditional dual-command operation in different scenarios show that significant reduction in the expected travel time is obtained in puzzle-based systems.

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