Informs 2015

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INFORMS Philadelphia – 2015
310
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,
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, 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,
755 Ferst Drive, NW, Atlanta, GA, Atlanta, GA, 30332,
United States of America, Enlu Zhou
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, , 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,
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.
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,
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,
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.
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, , 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,
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,
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,
1 - Stochastic and Robust Optimization of the Scheduling and
Market Involvement for an Energy Producer
Ricardo Lima, KAUST, Thuwal, Thuwal, Saudi Arabia,
, 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, ,
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 the first
echelon,a grid composed of identical cells is utilized to model the wind farm while
the cell center is the potential 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,
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.
TB76