A Stochastic Programming Approach for Locating and Dispatching Two Types of Ambulances

Emergency Medical Service systems aim to respond to emergency calls in a timely manner and provide prehospital care to patients. This paper addresses the problem of locating multiple types of emergency vehicles to stations while taking into account that vehicles are dispatched to prioritized patients with different health needs. We propose a two-stage stochastic-programming model that determines how to locate two types of ambulances in the first stage and dispatch them to prioritized emergency patients in the second stage after call-arrival scenarios are disclosed. We demonstrate how the base model can be adapted to include nontransport vehicles. A model formulation generalizes the base model to consider probabilistic travel times and general utilities for dispatching ambulances to prioritized patients. We evaluate the benefit of the model using two case studies, a value of the stochastic solution approach, and a simulation analysis. The case study is extended to study how to locate vehicles in the model extension with nontransport vehicles. Stochastic-programming models are computationally challenging for large-scale problem instances, and, therefore, we propose a solution technique based on Benders cuts.

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Multi-choice and stochastic programming for transportation problem involved in supply of foods and medicines to hospitals with consideration of logistic distribution

RAIRO - Operations Research ◽

10.1051/ro/2019050 ◽

2020 ◽

Vol 54 (4) ◽

pp. 1119-1132

Author(s):

Deshabrata Roy Mahapatra ◽

Shibaji Panda ◽

Shib Sankar Sana

Keyword(s):

Stochastic Programming ◽

Objective Function ◽

Transportation Problem ◽

Programming Model ◽

Logistic Distribution ◽

Programming Approach ◽

Binary Variables ◽

Cost Coefficient ◽

Aspiration Levels ◽

Stochastic Constraints

The objective of the proposed article is to minimize the transportation costs of foods and medicines from different source points to different hospitals by applying stochastic mathematical programming model to a transportation problem in a multi-choice environment containing the parameters in all constraints which follow the Logistic distribution and cost coefficients of objective function are also multiplicative terms of binary variables. Using the stochastic programming approach, the stochastic constraints are converted into an equivalent deterministic one. A transformation technique is introduced to manipulate cost coefficients of objective function involving multi-choice or goals for binary variables with auxiliary constraints. The auxiliary constraints depends upon the consecutive terms of multi-choice type cost coefficient of aspiration levels. A numerical example is presented to illustrate the whole idea.

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A multi-objective model for an integrated oil and natural gas supply chain under uncertainty

RAIRO - Operations Research ◽

10.1051/ro/2021158 ◽

2021 ◽

Author(s):

Ahmed M. Ghaithan ◽

Ahmed Attia ◽

Salih O. Duffuaa

Keyword(s):

Supply Chain ◽

Natural Gas ◽

Stochastic Programming ◽

Oil And Gas ◽

Petroleum Products ◽

Programming Approach ◽

Proposed Model ◽

Stochastic Solution ◽

Oil And Natural Gas ◽

Gas Supply

The oil and gas networks are overlapped because of the inclusion of associated gas in crude oil. This necessitates the integration and planning of oil and gas supply chain together. In recent years, hydrocarbon market has experienced high fluctuation in demands and prices which leads to considerable economic disruptions. Therefore, planning of oil and gas supply chain, considering market uncertainty is a significant area of research. In this regard, this study develops a multi-objective stochastic optimization model for tactical planning of downstream segment of oil and natural gas supply chain under uncertainty of price and demand of petroleum products. The proposed model was formulated based on a two-stage stochastic programming approach with a finite number of realizations. The proposed model helps to assess various trade-offs among the selected goals and guides decision maker(s) to effectively manage oil and natural gas supply chain. The applicability and the utility of the proposed model has been demonstrated using the case of Saudi Arabia oil and gas supply chain. The model is solved using the improved augmented ε-constraint algorithm. The impact of uncertainty of price and demand of petroleum products on the obtained results was investigated. The Value of Stochastic Solution (VSS) for total cost, total revenue, and service level reached a maximum of 12.6 %, 0.4 %, and 6.2% of wait-and see solutions, respectively. Therefore, the Value of the Stochastic Solution proved the importance of using stochastic programming approach over deterministic approach. In addition, the obtained results indicate that uncertainty in demand has higher impact on the oil and gas supply chain performance than the price.

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Selecting Large Portfolios of Social Projects in Public Organizations

Mathematical Problems in Engineering ◽

10.1155/2014/654293 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Igor Litvinchev ◽

Fernando Lopez-Irarragorri ◽

Nancy Maribel Arratia-Martínez ◽

José Antonio Marmolejo

Keyword(s):

Mixed Integer Linear Programming ◽

Large Scale ◽

Programming Model ◽

Public Organizations ◽

Mixed Integer ◽

Scale Problem ◽

Integer Linear Programming Model ◽

Problem Instances ◽

Funding Resource ◽

Selection Of

We address the portfolio selection of social projects in public organizations considering interdependencies (synergies) affecting project funds requirements and tasks. A mixed integer linear programming model is proposed incorporating the most relevant aspects of the problem found in the literature. The model supports both complete (all or nothing) and partial (a certain amount from a given interval of funding) resource allocation policies. Numerical results for large-scale problem instances are presented.

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A Stochastic Programming Approach for Resilient Hub Location in Power Projection Network considering Random Hub Failures

Mathematical Problems in Engineering ◽

10.1155/2017/6517453 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Xu-Tao Zhang ◽

Hai-Ling Bi ◽

Yun Wang

Keyword(s):

Stochastic Programming ◽

Programming Model ◽

Programming Approach ◽

Hub Location ◽

Hub Location Problem ◽

Power Projection ◽

Critical Facilities ◽

Resilient Networks ◽

The Impact ◽

Projection Network

Hubs are critical facilities in the power projection network. Due to the uncertainty factors such as terrorism threats, severe weather, and natural disasters, hub facilities may be disrupted randomly, which could lead to excessive cost or loss in practice. One of the most effective ways to withstand and reduce the impact of disruptions is designing more resilient networks. In this paper, a stochastic programming model is employed for the hub location problem in the presence of random hub failures. A heuristic algorithm based on Monte Carlo method and tabu search is put forward to solve the model. The proposed approach is more general if there are numbers of hubs that would fail even with different failure probability. Compared with the benchmark model, the model which takes the factor of stochastic failure of hubs into account can give a more resilient power projection network.

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A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

10.1101/2021.02.24.21252397 ◽

2021 ◽

Author(s):

Xuecheng Yin ◽

İ. Esra Büyüktahtakın

Keyword(s):

Resource Allocation ◽

Stochastic Programming ◽

Ebola Virus ◽

Programming Model ◽

Virus Disease ◽

Programming Approach ◽

Multiple Time ◽

Allocation Policy ◽

Multi Stage ◽

Constraints Model

AbstractExisting compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multistage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

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Statistical Machine Learning Model for Uncertainty Planning of Distributed Renewable Energy Sources in Distribution Networks

Frontiers in Energy Research ◽

10.3389/fenrg.2021.809254 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xueqian Fu ◽

Xianping Wu ◽

Nian Liu

Keyword(s):

Stochastic Programming ◽

Large Scale ◽

Programming Model ◽

Renewable Energy Sources ◽

Distribution Networks ◽

Point Estimation ◽

Operating Conditions ◽

Order Moment ◽

Operating Characteristics ◽

New Energy

New energy power systems with high-permeability photovoltaic and wind power are high-dimensional dynamic large-scale systems with nonlinear, uncertain and complex operating characteristics. The uncertainty of new energies creates challenges in detailed analyses of operating conditions and the efficient planning of distribution networks. Probabilistic power flows (PPFs) are effective tools for uncertainty analyses of distribution networks, and they can be applied in stochastic programming, risk assessment and other fields. We propose different forms of PPFs, which are origin moments rather than means and variances, based on point estimation. We design a stochastic programming model suitable for new energy planning in practice, and the PPF results can be used to improve energy stochastic programming methods by considering the principle of maximum entropy (POME) and quadratic fourth-order moment (QFM) estimation. The origin moments of PPFs are transformed into central moments as inputs of QFM based on probability theory. QFM can efficiently estimate the constraint probability levels of stochastic optimal planning models, and the proposed method is verified based on an IEEE 33-node distribution network.

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Routing Optimisation of Urban Medical Waste Recycling Network considering Differentiated Collection Strategy and Time Windows

Scientific Programming ◽

10.1155/2021/5523910 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Jiajing Gao ◽

Haolin Li ◽

Jingwen Wu ◽

Junyan Lyu ◽

Zheyi Tan ◽

...

Keyword(s):

Large Scale ◽

Programming Model ◽

Time Windows ◽

Computation Time ◽

Waste Recycling ◽

Medical Waste ◽

Third Party ◽

Mixed Integer ◽

Routing Problem ◽

Solution Approach

The increasing gap between medical waste production and disposal stresses the urgency of further development of urban medical waste recycling. This paper investigates an integrated optimisation problem in urban medical waste recycling network. It combines the vehicle routing problem of medical facilities with different requirements and the collection problem of clinics’ medical waste to the affiliated hospital. To solve this problem, a compact mixed-integer linear programming model is proposed, which takes account of the differentiated collection strategy and time windows. Since the medical waste recycling operates according to a two-day pattern, the periodic collection plan is also embedded in the model. Moreover, we develop a particle swarm optimisation (PSO) solution approach for problem-solving. Numerical experiments are also conducted to access the solution efficiency of the proposed algorithm, which can obtain a good solution in solving large-scale problem instances within a reasonable computation time. Based on the results, some managerial implications can be recommended for the third-party recycling company.

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Multistage Stochastic Programming for VPP Trading in Continuous Intraday Electricity Markets

10.36227/techrxiv.14999496 ◽

2021 ◽

Author(s):

Priyanka Shinde ◽

Iasonas Kouveliotis-Lysikatos ◽

Mikael Amelin

Keyword(s):

Stochastic Programming ◽

Electricity Markets ◽

Electricity Market ◽

Power Plants ◽

Large Scale ◽

Renewable Energy Sources ◽

Programming Approach ◽

Multistage Stochastic Programming ◽

Hydro Power ◽

Dual Dynamic Programming

<div>The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intraday markets provide the possibility to the market participants to modify their market positions based on their updated forecasts. In this paper, we propose a multistage stochastic programming approach to model the trading of a Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, in the Continuous Intraday (CID) electricity market. The order clearing in the CID market is enabled by the two presented models, namely the Immediate Order Clearing (IOC) and the Partial Order Clearing (POC). We tackle the proposed problem with a modified version of Stochastic Dual Dynamic Programming (SDDP) algorithm. The functionality of our model is demonstrated by performing illustrative and large scale case studies and comparing the performance with a benchmark model.</div>

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Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures

Computers ◽

10.3390/computers11010009 ◽

2022 ◽

Vol 11 (1) ◽

pp. 9

Author(s):

David Gilberto Gracia-Velásquez ◽

Andrés Steven Morales-Rodríguez ◽

Oscar Danilo Montoya

Keyword(s):

Nonlinear Programming ◽

Objective Function ◽

Large Scale ◽

Single Phase ◽

Programming Model ◽

Search Algorithm ◽

Solution Space ◽

Parametric Estimation ◽

Solution Technique ◽

Matlab Programming

The problem of the electrical characterization of single-phase transformers is addressed in this research through the application of the crow search algorithm (CSA). A nonlinear programming model to determine the series and parallel impedances of the transformer is formulated using the mean square error (MSE) between the voltages and currents measured and calculated as the objective function. The CSA is selected as a solution technique since it is efficient in dealing with complex nonlinear programming models using penalty factors to explore and exploit the solution space with minimum computational effort. Numerical results in three single-phase transformers with nominal sizes of 20 kVA, 45 kVA, 112.5 kVA, and 167 kVA demonstrate the efficiency of the proposed approach to define the transformer parameters when compared with the large-scale nonlinear solver fmincon in the MATLAB programming environment. Regarding the final objective function value, the CSA reaches objective functions lower than 2.75×10−11 for all the simulation cases, which confirms their effectiveness in minimizing the MSE between real (measured) and expected (calculated) voltage and current variables in the transformer.

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