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Articles 1 - 15 of 15

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Determination Of Bus Station Locations Under Emission And Social Cost Constraints, Atousa Zarindast, Elif Elçin Günay, Kijung Park, Gül E. Okudan Kremer Jan 2018

Determination Of Bus Station Locations Under Emission And Social Cost Constraints, Atousa Zarindast, Elif Elçin Günay, Kijung Park, Gül E. Okudan Kremer

Industrial and Manufacturing Systems Engineering Conference Proceedings and Posters

This study proposes a two-stage stochastic programming model to determine an optimal set of bus stations that minimizes operational, environmental, and social costs under uncertain weather conditions and customer perceptions on sustainability. The first stage of the proposed model focuses on the derivation of a set of bus stations under uncertain demand and weather conditions. Then, the second stage determines an optimal vehicle capacity (i.e., bus size) to minimize the impact of vehicle shortages. In the proposed model, different customer perceptions on sustainability are conceptualized through a range of dissatisfaction levels. Weather conditions are considered as causing higher dissatisfaction ...


Robust Optimization Vs. Stochastic Programming Incorporating Risk Measures For Unit Commitment With Uncertain Variable Renewable Generation, Narges Kazemzadeh, Sarah M. Ryan, Mahdi Hamzeei Dec 2017

Robust Optimization Vs. Stochastic Programming Incorporating Risk Measures For Unit Commitment With Uncertain Variable Renewable Generation, Narges Kazemzadeh, Sarah M. Ryan, Mahdi Hamzeei

Industrial and Manufacturing Systems Engineering Publications

Unit commitment seeks the most cost effective generator commitment schedule for an electric power system to meet net load, defined as the difference between the load and the output of renewable generation, while satisfying the operational constraints on transmission system and generation resources. Stochastic programming and robust optimization are the most widely studied approaches for unit commitment under net load uncertainty. We incorporate risk considerations in these approaches and investigate their comparative performance for a multi-bus power system in terms of economic efficiency as well as the risk associated with the commitment decisions. We explicitly account for risk, via Conditional ...


Shop Floor Lot-Sizing And Scheduling With A Two-Stage Stochastic Programming Model Considering Uncertain Demand And Workforce Efficiency, Yihua Li, Guiping Hu Jul 2017

Shop Floor Lot-Sizing And Scheduling With A Two-Stage Stochastic Programming Model Considering Uncertain Demand And Workforce Efficiency, Yihua Li, Guiping Hu

Industrial and Manufacturing Systems Engineering Publications

Efficient and flexible production planning is necessary for the manufacturing industry to stay competitive in today’s global market. Shop floor lot-sizing and scheduling is one of the most challenging and rewarding subjects for the management. In this study, a two-stage stochastic programming model is proposed to solve a single-machine, multi-product shop floor lot-sizing and scheduling problem. Two sources of uncertainties are considered simultaneously: product demand from the market, and workforce efficiency, which is the major contribution of this study. The workforce efficiency affects the system productivity, and we propose different distributions to model its uncertainty with insufficient information.The ...


Hybrid Robust And Stochastic Optimization For Closed-Loop Supply Chain Network Design Using Accelerated Benders Decomposition, Esmaeil Keyvanshokooh, Sarah M. Ryan, Elnaz Kabir Feb 2016

Hybrid Robust And Stochastic Optimization For Closed-Loop Supply Chain Network Design Using Accelerated Benders Decomposition, Esmaeil Keyvanshokooh, Sarah M. Ryan, Elnaz Kabir

Industrial and Manufacturing Systems Engineering Publications

Environmental, social and economic concerns motivate the operation of closed-loop supply chain networks (CLSCN) in many industries. We propose a novel profit maximization model for CLSCN design as a mixed-integer linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the firm's policies. Our major contribution is to develop a novel hybrid robust-stochastic programming (HRSP) approach to simultaneously model two different types of uncertainties by including stochastic scenarios for transportation costs and polyhedral uncertainty sets for demands and returns. Transportation cost scenarios are generated using a Latin Hypercube Sampling method ...


Integration Of Progressive Hedging And Dual Decomposition In Stochastic Integer Programs, Ge Guo, Gabriel Hackebeil, Sarah M. Ryan, Jean-Paul Watson, David L. Woodruff May 2015

Integration Of Progressive Hedging And Dual Decomposition In Stochastic Integer Programs, Ge Guo, Gabriel Hackebeil, Sarah M. Ryan, Jean-Paul Watson, David L. Woodruff

Industrial and Manufacturing Systems Engineering Publications

We present a method for integrating the Progressive Hedging (PH) algorithm and the Dual Decomposition (DD) algorithm of Carøe and Schultz for stochastic mixed-integer programs. Based on the correspondence between lower bounds obtained with PH and DD, a method to transform weights from PH to Lagrange multipliers in DD is found. Fast progress in early iterations of PH speeds up convergence of DD to an exact solution. We report computational results on server location and unit commitment instances.


Evaluation Of The Impacts Of Governmental Policies On The Biofuels Supply Chain Design Under Uncertainty, Narges Kazemzadeh, Guiping Hu Jan 2015

Evaluation Of The Impacts Of Governmental Policies On The Biofuels Supply Chain Design Under Uncertainty, Narges Kazemzadeh, Guiping Hu

Industrial and Manufacturing Systems Engineering Publications

There is a growing interest in the use of biomass as a source of energy around the world. In the United States, the Renewable Fuel Standard (RFS) sets policies and mandates to support the production and consumption of biofuels. However, the uncertainties associated with the governmental and local policies and regulations on both production and consumption have imposed significant impacts on the biofuel supply chain network. This study aims to determine the optimal design of the biofuel supply chain to maximize annual profit under the impacts of governmental policies. In this study, two-stage stochastic programming models are developed in which ...


Supply Chain Design Under Uncertainty For Advanced Biofuel Production Based On Bio-Oil Gasification, Qi Li, Guiping Hu Sep 2014

Supply Chain Design Under Uncertainty For Advanced Biofuel Production Based On Bio-Oil Gasification, Qi Li, Guiping Hu

Industrial and Manufacturing Systems Engineering Publications

An advanced biofuels supply chain is proposed to reduce biomass transportation costs and take advantage of the economics of scale for a gasification facility. In this supply chain, biomass is converted to bio-oil at widely distributed small-scale fast pyrolysis plants, and after bio-oil gasification, the syngas is upgraded to transportation fuels at a centralized biorefinery. A two-stage stochastic programming is formulated to maximize biofuel producers' annual profit considering uncertainties in the supply chain for this pathway. The first stage makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants as well as the centralized ...


Solution Sensitivity-Based Scenario Reduction For Stochastic Unit Commitment, Yonghan Feng, Sarah M. Ryan Sep 2014

Solution Sensitivity-Based Scenario Reduction For Stochastic Unit Commitment, Yonghan Feng, Sarah M. Ryan

Industrial and Manufacturing Systems Engineering Publications

A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if many scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled ...


Robust Two-Stage Stochastic Linear Programs With Moment Constraints, Sarah Yini Gao, Lingchen Kong, Jie Sun Jun 2014

Robust Two-Stage Stochastic Linear Programs With Moment Constraints, Sarah Yini Gao, Lingchen Kong, Jie Sun

Research Collection Lee Kong Chian School Of Business

We consider the two-stage stochastic linear programming model, in which the recourse function is a worst case expected value over a set of probabilistic distributions. These distributions share the same first- and second-order moments. By using duality of semi-infinite programming and assuming knowledge on extreme points of the dual polyhedron of the constraints, we show that a deterministic equivalence of the two-stage problem is a second-order cone optimization problem. Numerical examples are presented to show non-conservativeness and computational advantage of this approach.


Robust Design Of A Closed-Loop Supply Chain Network For Uncertain Carbon Regulations And Random Product Flows, Nan Gao, Sarah M. Ryan Jun 2014

Robust Design Of A Closed-Loop Supply Chain Network For Uncertain Carbon Regulations And Random Product Flows, Nan Gao, Sarah M. Ryan

Industrial and Manufacturing Systems Engineering Publications

This paper addresses a multi-period capacitated closed-loop supply chain (CLSC) network design problem subject to uncertainties in the demands and returns as well as the potential carbon emission regulations. Two promising regulatory policy settings are considered: namely, (a) a carbon cap and trade system, or (b) a tax on the amount of carbon emissions. A traditional CLSC network design model using stochastic programming is extended to integrate robust optimization to account for regulations of the carbon emissions caused by transportation. We propose a hybrid model to account for both regulatory policies and derive tractable robust counterparts under box and ellipsoidal ...


Temporal Vs. Stochastic Granularity In Thermal Generation Capacity Planning With Wind Power, Shan Jin, Audun Botterud, Sarah M. Ryan Jan 2014

Temporal Vs. Stochastic Granularity In Thermal Generation Capacity Planning With Wind Power, Shan Jin, Audun Botterud, Sarah M. Ryan

Industrial and Manufacturing Systems Engineering Publications

We propose a stochastic generation expansion model, where we represent the long-term uncertainty in the availability and variability in the weekly wind pattern with multiple scenarios. Scenario reduction is conducted to select a representative set of scenarios for the long-term wind power uncertainty. We assume that the short-term wind forecast error induces an additional amount of operating reserves as a predefined fraction of the wind power forecast level. Unit commitment (UC) decisions and constraints for thermal units are incorporated into the expansion model to better capture the impact of wind variability on the operation of the system. To reduce computational ...


A Multi-Objective Facility Location Model For Closed-Loop Supply Chain Network Under Uncertain Demand And Return, Saman Hassanzadeh Amin, Guoqing Zhang Mar 2013

A Multi-Objective Facility Location Model For Closed-Loop Supply Chain Network Under Uncertain Demand And Return, Saman Hassanzadeh Amin, Guoqing Zhang

Mechanical, Automotive & Materials Engineering Publications

A closed-loop supply chain (CLSC) network consists of both forward and reverse supply chains. In this paper, a CLSC network is investigated which includes multiple plants, collection centres, demand markets, and products. To this aim, a mixed-integer linear programming model is proposed that minimizes the total cost. Besides, two test problems are examined. The model is extended to consider environmental factors by weighed sums and ε-constraint methods. In addition, we investigate the impact of demand and return uncertainties on the network configuration by stochastic programming (scenario-based). Computational results show that the model can handle demand and return uncertainties, simultaneously.


Scenario Construction And Reduction Applied To Stochastic Power Generation Expansion Planning, Yonghan Feng, Sarah M. Ryan Jan 2013

Scenario Construction And Reduction Applied To Stochastic Power Generation Expansion Planning, Yonghan Feng, Sarah M. Ryan

Industrial and Manufacturing Systems Engineering Publications

A challenging aspect of applying stochastic programming in a dynamic setting is to construct a set of discrete scenarios that well represents multivariate stochastic processes for uncertain parameters. Often this is done by generating a scenario tree using a statistical procedure and then reducing its size while maintaining its statistical properties. In this paper, we test a new scenario reduction heuristic in the context of long-term power generation expansion planning. We generate two different sets of scenarios for future electricity demands and fuel prices by statistical extrapolation of long-term historical trends. The cardinality of the first set is controlled by ...


Modeling And Solving A Large-Scale Generation Expansion Planning Problem Under Uncertainty, Shan Jin, Sarah M. Ryan, Jean-Paul Watson, David L. Woodruff Nov 2011

Modeling And Solving A Large-Scale Generation Expansion Planning Problem Under Uncertainty, Shan Jin, Sarah M. Ryan, Jean-Paul Watson, David L. Woodruff

Industrial and Manufacturing Systems Engineering Publications

We formulate a generation expansion planning problem to determine the type and quantity of power plants to be constructed over each year of an extended planning horizon, considering uncertainty regarding future demand and fuel prices. Our model is expressed as a two-stage stochastic mixed-integer program, which we use to compute solutions independently minimizing the expected cost and the Conditional Value-at-Risk; i.e., the risk of significantly larger-than-expected operational costs. We introduce stochastic process models to capture demand and fuel price uncertainty, which are in turn used to generate trees that accurately represent the uncertainty space. Using a realistic problem instance ...


Effects Of Uncertain Fuel Costs On Fossil Fuel And Electric Energy Flows In The Us, Yan Wang, Sarah M. Ryan May 2010

Effects Of Uncertain Fuel Costs On Fossil Fuel And Electric Energy Flows In The Us, Yan Wang, Sarah M. Ryan

Industrial and Manufacturing Systems Engineering Publications

To explore the effects of uncertain fuel costs on the bulk energy flows in the US, we introduce stochastic fuel costs in a generalized network flow model of the integrated electric energy system, including coal, natural gas, and electricity generation. The fuel costs are modeled as discretely distributed random variables. A rolling two-stage recourse stochastic programming approach is employed to simulate the decision process involving uncertain costs with forecast updates. All the data are derived from publicly available information for the years 2002, when natural gas prices rose much higher than forecast, and 2006, when gas prices were lower than ...