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Robust Water Supply Chain Network Design under Uncertainty in Capacity

WATER RESOURCES MANAGEMENT 2020年第13期34卷 4093-4112页

作者： Mozafari, Marzieh Zabihi, Alireza Islamic Azad Univ Dept Ind Engn Elect Branch Tehran Iran

This paper focuses on the capacity uncertainty in water supply chains that occurs when facilities face disruption. A combination of scenario-based two-stage stochastic programming with the min-max robust optimization approach is proposed to optimize the water supply chain network design problem. In the first stage, the decisions are made on locations and capacities of reservoirs and water-treatment plants while recourse decisions including amount of water extraction, amount of water refinement, and consequently amount of water held in reservoirs are made at the second stage. The proposed robust two-stage stochastic programming model can help decision makers consider the impacts of uncertainties and analyze trade-offs between system cost and stability. The literature reveals that most exact methods are not able to tackle the computational complexity of mixed integer non-linear two-stage stochastic problems at large scale. Another contribution of this study is to propose two metaheuristics - a particle swarm optimization (PSO) and a bat algorithm (BA) - to solve the proposed model in large-scale networks efficiently in a reasonable time. The developed model is applied to several hypothetical cases of water resources management systems to evaluate the effectiveness of the model formulation and solution algorithms. Sensitivity analyses are also carried out to analyze the behavior of the model and the robustness approach under parameters variations.

关键词： Water supply chain network design Capacity uncertainty two-stage scenario-based stochastic programming Robust optimization Evolutionary algorithms

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Integrated production-distribution planning in two-echelon systems: a resilience view

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2017年第4期55卷 1040-1064页

作者： Khalili, Seyed Mohammad Jolai, Fariborz Torabi, Seyed Ali Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage's objective function is considered by utilising the conditional value at risk (CVaR) measure. The validation and applicability of the proposed model are examined through several numerical experiments.

关键词： production and distribution planning supply chain resilience two-stage scenario-based stochastic programming possibilistic programming conditional value at risk

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A novel robust optimization approach for an integrated municipal water distribution system design under uncertainty: A case study of Mashhad

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COMPUTERS & CHEMICAL ENGINEERING 2018年 110卷 13-34页

作者： Ghelichi, Zabih Tajik, Javad Pishvaee, Mir Saman Iran Univ Sci & Technol Sch Ind Engn Tehran *** Iran

This paper proposes a novel robust optimization (RO) approach along with a two-stage scenario-based stochastic programming to optimize a municipal water distribution system (WDS) under demand and rainfall uncertainties. Firstly, we have proposed a new multi-period mixed-integer linear programming (MILP) formulation of a municipal WDS. The goal is to find solutions that are both cost-effective and completely fulfill potable and non-potable demand in an integrated system. Furthermore, a novel RO approach is developed which attempts to adjust protection level in a column what we call "adjustable column-wise robust optimization". The interesting point of the proposed RO approach is its linear structure and being computationally tractable. The efficiency of the proposed models are evaluated through a real case study of Mashhad. The acquired results reveal the proposed WDS model have dramatically reduced the total costs. Simultaneously, the RO approach has risen robustness besides realization demonstrates its better performance than deterministic one. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： Robust optimization Water distribution system two-stage scenario-based stochastic programming

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A two-stage stochastic Framework for an Electricity Retailer Considering Demand Response and Uncertainties Using a Hybrid Clustering Technique

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2019年第1期43卷 541-558页

作者： Gilvaei, Mostafa Nasouri Baghramian, Alfred Univ Guilan Dept Elect Engn Rasht Iran

Due to the highly volatile prices of pool market, a main source of an electricity retailer to meet its clients' demand, retailers generally sign forward contracts in order to protect themselves from being exposed to the risk imposed by the uncertain pool prices. These contracts, however, decrease the retailer's expected profit owing to their higher average prices compared with the pool market. In this paper, focusing on price-based demand response programs, a two-stage scenario-based stochastic framework is presented for the medium-term decision-making problem of an electricity retailer. This study would demonstrate that demand response programs can be an effective tool to hedge against the risk and an appropriate alternative yielding less involvement in costly forward agreements. The proposed model decides the optimal level of participation in the pool as well as forward market and determines the electricity rates offered to the clients. The objective is maximizing the expected value of the retailer's profit, whereas the exposure risk is confined to a pre-specified level. Moreover, the scenarios required for the stochastic programming problem are generated using a hybrid clustering technique based on K-means and particle swarm optimization algorithms. The proposed model is mathematically described as a mixed-integer linear problem which is solvable through commercial software packages. The efficiency of the provided approach is evaluated via a realistic case study according to the available data from Spain electricity market.

关键词： Demand response Electricity retailer K-means Particle swarm optimization Risk management two-stage scenario-based stochastic programming

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