scholarly journals Location of cross-docking centers and vehicle routing scheduling under uncertainty: A fuzzy possibilistic–stochastic programming model

2014 ◽  
Vol 38 (7-8) ◽  
pp. 2249-2264 ◽  
Author(s):  
S. Meysam Mousavi ◽  
Behnam Vahdani ◽  
R. Tavakkoli-Moghaddam ◽  
H. Hashemi
Keyword(s):  
Stochastic Programming ◽  
Vehicle Routing ◽  
Programming Model ◽  
Cross Docking ◽  
Stochastic Programming Model ◽  
Scheduling Under Uncertainty

Vehicle routing problem for humanitarian relief distribution under hybrid uncertainty

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sara Nodoust ◽  
Mir Saman Pishvaee ◽  
Seyed Mohammad Seyedhosseini
Keyword(s):  
Stochastic Programming ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
Programming Approach ◽  
Routing Problem ◽  
Content Type ◽  
Demand Rate ◽  
Stochastic Programming Model ◽  
Hybrid Uncertainty

PurposeGiven the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem in order to distribute first aid relief items in the post disaster phase, where routes are subject to disruption.Design/methodology/approachTo cope with such kind of uncertainty, the demand rate of relief items is considered as a random fuzzy variable and a robust scenario-based possibilistic-stochastic programming model is elaborated. The results are presented and reported on a real case study of earthquake, along with sensitivity analysis through some important parameters.FindingsThe results show that the demand satisfaction level in the proposed model is significantly higher than the traditional scenario-based stochastic programming model.Originality/valueIn reality, in the occurrence of a disaster, demand rate has a mixture nature of objective and subjective and should be represented through possibility and probability theories simultaneously. But so far, in studies related to this domain, demand parameter is not considered in hybrid uncertainty. The worth of considering hybrid uncertainty in this study is clarified by supplementing the contribution with presenting a robust possibilistic programming approach and disruption assumption on roads.

scholarly journals Analysis of a Stochastic Programming Model for Optimal Hydropower System Operation under a Deregulated Electricity Market by Considering Forecasting Uncertainty

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 885 ◽  
Author(s):  
Bin Xu ◽  
Ping-An Zhong ◽  
Baoyi Du ◽  
Juan Chen ◽  
Weifeng Liu ◽  
...  
Keyword(s):  
Stochastic Programming ◽  
Electricity Market ◽  
Energy Production ◽  
Programming Model ◽  
Optimal Operation ◽  
Spot Price ◽  
Deregulated Electricity Market ◽  
Hydropower System ◽  
Stochastic Programming Model ◽  
Hydropower Operation

In a deregulated electricity market, optimal hydropower operation should be achieved through informed decisions to facilitate the delivery of energy production in forward markets and energy purchase level from other power producers within real-time markets. This study develops a stochastic programming model that considers the influence of uncertain streamflow on hydropower energy production and the effect of variable spot energy prices on the cost of energy purchase (energy shortfall). The proposed model is able to handle uncertainties expressed by both a probability distribution and discretized scenarios. Conflicting decisions are resolved by maximizing the expected value of net revenue, which jointly considers benefit and cost terms under uncertainty. Methodologies are verified using a case study of the Three Gorges cascade hydropower system. The results demonstrate that optimal operation policies are derived based upon systematic evaluations on the benefit and cost terms that are affected by multiple uncertainties. Moreover, near-optimal operation policy under the case of inaccurate spot price forecasts is also analyzed. The results also show that a proper policy for guiding hydropower operation seeks the best compromise between energy production and energy purchase levels, which explores their nonlinear tradeoffs over different time periods.

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