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stochastic network models for logistics planning in disaster relief

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第1期255卷 187-206页

作者： Alem, Douglas Clark, Alistair Moreno, Alfredo Univ Fed Sao Carlos Dept Prod Engn Sorocaba Brazil Univ West England Dept Engn Design & Math Bristol BS16 1QY Avon England

Emergency logistics in disasters is fraught with planning and operational challenges, such as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable information about the location and needs of victims, possible random supplies and donations, precarious transport links, scarcity of resources, and so on. This paper develops a new two-stage stochastic network flow model to help decide how to rapidly supply humanitarian aid to victims of a disaster within this context The model takes into account practical characteristics that have been neglected by the literature so far, such as budget allocation, fleet sizing of multiple types of vehicles, procurement, and varying lead times over a dynamic multiperiod horizon. Attempting to improve demand fulfillment policy, we present some extensions of the model via state-of-art risk measures, such as semideviation and conditional value-at-risk. A simple two-phase heuristic to solve the problem within a reasonable amount of computing time is also suggested. Numerical tests based on the floods and landslides in Rio de Janeiro state, Brazil, show that the model can help plan and organise relief to provide good service levels in most scenarios, and how this depends on the type of disaster and resources. Moreover, we demonstrate that our heuristic performs well for real and random instances. (C) 2016 Elsevier B.V. All rights reserved.

关键词： OR in disaster relief Humanitarian logistics Emergency logistics planning two-stage stochastic programming Risk-aversion

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Optimization of biofuel production from corn stover under supply uncertainty in Ontario

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BIOFUEL RESEARCH JOURNAL-BRJ 2017年第4期4卷 721-729页

作者： Ranisau, Jonathan Ogbe, Emmanuel Trainor, Aaron Barbouti, Mohammed Elsholkami, Mohamed Elkamel, Ali Fowler, Michael Univ Waterloo Dept Chem Engn Waterloo ON Canada Khalifa Univ Petr Inst Dept Chem Engn Abu Dhabi U Arab Emirates

In this paper, a biofuel production supply chain optimization framework is developed that can supply the fuel demand for 10% of Ontario. Different biomass conversion technologies are considered, such as pyrolysis and gasification and subsequent hydro processing and the Fischer-Tropsch process. A supply chain network approach is used for the modeling, which enables the optimization of both the biorefinery locations and the associated transportation networks. Gasification of corn stover is examined to convert waste biomass into valuable fuel. Biomass-derived fuel has several advantages over traditional fuels including substantial greenhouse gas reduction, generating higher quality synthetic fuels, providing a use for biomass waste, and potential for use without much change to existing infrastructure. The objective of this work is to show the feasibility of the use of corn stover as a biomass feedstock to a hydrocarbon biofuel supply chain in Ontario using a mixed-integer linear programming model while accounting for the uncertainty in the availability of corn stover. In the case study, the exact number of biorefineries is left as a policy decision and the optimization is carried out over a range of the possible numbers of facilities. The results obtained from the case study suggests implementing gasification technology followed by Fischer-Tropsch at two different sites in Ontario. The optimal solution satisfied 10% of the yearly fuel demand of Ontario with two production plants (14.8 billion L of fuel) and requires an investment of $42.9 billion, with a payback period of about 3 years. (C) 2017 BRTeam. All rights reserved.

关键词： Biofuel Supply chain optimization Biomass to biofuels two-stage stochastic programming Uncertainty

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Assessing wind uncertainty impact on short term operation scheduling of coordinated energy storage systems and thermal units

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RENEWABLE ENERGY 2016年 95卷 74-84页

作者： Hemmati, Reza Saboori, Hedayat Saboori, Saeid Kermanshah Univ Technol Dept Elect Engn Kermanshah Iran Sahand Univ Technol Fac Elect Engn Tabriz Iran

Renewable resources, especially wind power, are widely integrated into the power systems nowadays. Managing uncertainty of the large scale wind power is often known as one of the most challenging issues in the power system operation scheduling. Additionally, energy storage systems (ESSs) have been widely investigated in the power systems owing to their valuable applications, especially renewable energy smoothing and time shift. In this paper, a stochastic unit commitment (UC) model is proposed to assess the impact of the wind uncertainty impact on ESSs and thermal units schedule in UC problem. Wind uncertainty is modeled based on the two measures. First, the wind penetration level is changed with respect to the basic level. Second, the wind forecasting error is modeled through a normal probability distribution function with different variances. The ESSs are modeled based on several technical characteristics and optimally scheduled considering different levels of the wind penetration and forecasting accuracies. The proposed formulation is a stochastic mixed integer linear programming (SMILP) and solved using GAMS software. Simulation results demonstrate that the wind uncertainty have a considerable impact on operation cost and ESSs schedule while proposed optimum storage scheduling through the stochastic programming will reduce the daily operational cost considerably. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Energy storage system Short term operation scheduling two-stage stochastic programming Wind power uncertainty

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Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part II: Dealing with uncertainty

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ENERGY 2016年 96卷 461-467页

作者： Tan, Siah Hong Barton, Paul I. MIT Proc Syst Engn Lab 77 Massachusetts Ave Cambridge MA 02139 USA

Using the Bakken shale play as a case study, the previous part of this two-part series demonstrated how small-scale mobile plants could be used to monetize associated or stranded gas effectively. Here, we address the issue of uncertainty in future supply, demand and price conditions. To this end, we modified our multi-period optimization framework to a stochastic programming framework to account for various scenarios with different parameter realizations in the future. The maximum ENPV (expected net present value) obtained was $2.01 billion, higher than the NPV obtained in the previous part. In addition, the value of the stochastic solution was 0.11% of the optimal ENPV, indicating that the flexible nature of mobile plants affords them a great advantage when dealing with uncertainty. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Natural gas monetization Mobile modular plants Mixed-integer linear programming two-stage stochastic programming Parametric uncertainty Bakken shale play

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stochastic Unit Commitment in Isolated Systems With Renewable Penetration Under CVaR Assessment

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IEEE TRANSACTIONS ON SMART GRID 2016年第3期7卷 1356-1367页

作者： Asensio, Miguel Contreras, Javier Univ Castilla La Mancha ETS Ingenieros Ind E-13071 Ciudad Real Spain

Isolated regions and islands are facing imported fossil-fuel dependency, higher electricity prices, and vulnerability to climate change. At the same time, they are increasing their renewable penetration and, therefore, risk for electric utilities. Integrating stochastic energy resources in noninterconnected systems may take advantage of an intelligent and optimized risk-averse unit commitment (UC) model. This paper presents a two-stage stochastic UC model with high renewable penetration including reserve requirements for the efficient management of uncertainty. In order to account for the uncertainty around the true outcomes of load, wind, and photovoltaic (PV) generation, a minimum conditional value at risk term has been included in the model formulation. A stochastic measure of the value of the stochastic solution is used to evaluate the benefits of using stochastic programming. The model considers the need for reserves dependent on the forecasting horizon and the amount of renewable generation. Active power demand, and wind and PV generations are considered as probability distribution functions. The model is applied to the Lanzarote-Fuerteventura system in the Canary Islands, Spain, and Crete, Greece.

关键词： Conditional value at risk (CVaR) mean-risk value of the stochastic solution (MRVSS) mixed-integer linear programming risk aversion two-stage stochastic programming unit commitment (UC)

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A hybrid metaheuristic for the vehicle routing problem with stochastic demand and duration constraints

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JOURNAL OF HEURISTICS 2016年第4期22卷 539-566页

作者： Mendoza, Jorge E. Rousseau, Louis-Martin Villegas, Juan G. Univ Catholique Ouest LUNAM Univ LARIS EA 7315 3 Pl Andre Leroy F-49008 Angers France Univ Tours CNRS LI EA 6300 OC ERL CNRS 6305 64 Ave Jean Portalis F-37300 Tours France Ecole Polytech CIRRELT Succ Ctr VilleCP 6079 Montreal PQ H3C 3A7 Canada Univ Antioquia Fac Ingn Dept Ingn Ind Calle 70 52-21 Medellin 050010 Colombia

The vehicle routing problem with stochastic demands (VRPSD) consists in designing optimal routes to serve a set of customers with random demands following known probability distributions. Because of demand uncertainty, a vehicle may arrive at a customer without enough capacity to satisfy its demand and may need to apply a recourse to recover the route's feasibility. Although travel times are assumed to be deterministic, because of eventual recourses the total duration of a route is a random variable. We present two strategies to deal with route-duration constraints in the VRPSD. In the first, the duration constraints are handled as chance constraints, meaning that for each route, the probability of exceeding the maximum duration must be lower than a given threshold. In the second, violations to the duration constraint are penalized in the objective function. To solve the resulting problem, we propose a greedy randomized adaptive search procedure (GRASP) enhanced with heuristic concentration (HC). The GRASP component uses a set of randomized route-first, cluster-second heuristics to generate starting solutions and a variable-neighborhood descent procedure for the local search phase. The HC component assembles the final solution from the set of all routes found in the local optima reached by the GRASP. For each strategy, we discuss extensive computational experiments that analyze the impact of route-duration constraints on the VRPSD. In addition, we report state-of-the-art solutions for a established set of benchmarks for the classical VRPSD.

关键词： Distance-constrained vehicle routing problem Vehicle routing problem with stochastic demands two-stage stochastic programming GRASP Heuristic concentration

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two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第20期54卷 6192-6215页

作者： Englberger, Julian Herrmann, Frank Manitz, Michael OTH Regensburg Innovat & Competence Ctr Prod Logist & Factory Pl Regensburg Germany Univ Duisburg Essen Mercator Sch Management Duisburg Germany

This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.

关键词： Master production scheduling demand uncertainty two-stage stochastic programming scenario-based stochastic programming with recourse hierarchical production planning

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Large-scale optimization with the primal-dual column generation method

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MATHEMATICAL programming COMPUTATION 2016年第1期8卷 47-82页

作者： Gondzio, Jacek Gonzalez-Brevis, Pablo Munari, Pedro Univ Edinburgh Sch Math James Clerk Maxwell BldgKings BldgMayfield Rd Edinburgh EH9 3JZ Midlothian Scotland Univ Desarrollo Sch Engn Ave Sanhueza 1750 Concepcion 4040418 Chile Univ Fed Sao Carlos Dept Prod Engn RodoviaWashington Luis Km 235 BR-13565905 Sao Carlos SP Brazil

The primal-dual column generation method (PDCGM) is a general-purpose column generation technique that relies on the primal-dual interior point method to solve the restricted master problems. The use of this interior point method variant allows to obtain suboptimal and well-centered dual solutions which naturally stabilizes the column generation process. As recently presented in the literature, reductions in the number of calls to the oracle and in the CPU times are typically observed when compared to the standard column generation, which relies on extreme optimal dual solutions. However, these results are based on relatively small problems obtained from linear relaxations of combinatorial applications. In this paper, we investigate the behaviour of the PDCGM in a broader context, namely when solving large-scale convex optimization problems. We have selected applications that arise in important real-life contexts such as data analysis (multiple kernel learning problem), decision-making under uncertainty (two-stage stochastic programming problems) and telecommunication and transportation networks (multicommodity network flow problem). In the numerical experiments, we use publicly available benchmark instances to compare the performance of the PDCGM against recent results for different methods presented in the literature, which were the best available results to date. The analysis of these results suggests that the PDCGM offers an attractive alternative over specialized methods since it remains competitive in terms of number of iterations and CPU times even for large-scale optimization problems.

关键词： Column generation Cutting plane method Interior point methods Convex optimization Multiple kernel learning problem two-stage stochastic programming Multicommodity network flow problem

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Reliable transportation network design considering uncertain demand variability

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INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 2016年第8期10卷 752-763页

作者： Wang, David Z. W. Xie, Dong-Fan Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Beijing Jiaotong Univ Sch Traff & Transportat Inst Syst Sci Beijing Peoples R China

Travel demand is indeed stochastic with day-to-day travel demand variability, because of the underlying characteristic of variability of travel activities. The stochastic travel demand can be described by different probability distribution patterns and has to be considered in transport network design, as it affects travelers' routing choice behavior against stochastic travel time and eventually the traffic flow pattern on the network. However, on the other hand, because of the inaccurate or inconsistent travel demand estimation procedures, the prediction of the travel demand distribution pattern and parameters is susceptible to a certain level of uncertainty. Therefore, both the travel demand day-to-day variability and the uncertainty in terms of estimating travel demand distribution pattern and parameters have to be incorporated in the transportation network design. This study develops an approach to model network design problem with uncertain demand variability. Specifically, it is assumed that the total travel demand comprises two parts, commuters and infrequent travelers, and only the demand of infrequent travelers is stochastic. Then, the travel time reliability is introduced to depict the path choice behavior. A finite-scenario two-stage stochastic programming approach is applied to model the network design problem with uncertain demand variability. Numerical studies are performed to illustrate the model validity and efficiency of solution algorithm.

关键词： Network design travel time reliability two-stage stochastic programming uncertain demand variability

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Islanding in distribution systems considering wind power and storage

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SUSTAINABLE ENERGY GRIDS & NEtwoRKS 2016年 5卷 156-166页

作者： Meneses de Quevedo, Pilar Allahdadian, Javad Contreras, Javier Chicco, Gianfranco Univ Castilla La Mancha ETS Ingenieros Ind E-13071 Ciudad Real Spain Politecn Torino Energy Dept DENERG I-10129 Turin Italy

In modern power systems the penetration of renewable energies has been growing dramatically. The combination of renewable energy and energy storage is seen as an opportunity to better exploit the intermittent and uncertain local generation in distribution systems, especially in the case of islanding. The main goal of this paper is to keep the load and generation units on-line under islanding conditions with respect to the total power imbalance of the isolated area and minimizing the power losses and nodal voltage deviations. A two-stage stochastic linear programming model is introduced to solve the optimization problem and find the best combination of generation, demand and electrical energy storage under islanding conditions. The proposed model has been tested on a 69-bus distribution system and the results obtained in the islanded areas are presented considering two case studies (with and without electrical energy storage), under different levels of generation and demand. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Islanding Distributed generation Distribution system Storage devices two-stage stochastic programming

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