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Статті в журналах з теми "Chance-constrained optimisation"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Charles, Vincent, and A. Udhayakumar. "Genetic algorithm for chance constrained reliability stochastic optimisation problems." International Journal of Operational Research 14, no. 4 (2012): 417. http://dx.doi.org/10.1504/ijor.2012.047513.

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Murray, Andrew, Michael Cashmore, Ashwin Arulselvan, and Jeremy Frank. "Joint Chance Constrained Probabilistic Simple Temporal Networks via Column Generation (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (July 17, 2022): 305–7. http://dx.doi.org/10.1609/socs.v15i1.21794.

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Probabilistic Simple Temporal Networks (PSTN) are used to represent scheduling problems under uncertainty. In a temporal network that is Strongly Controllable (SC) there exists a concrete schedule that is robust to any uncertainty. We solve the problem of determining Chance Constrained PSTN SC as a Joint Chance Constrained optimisation problem via column generation, lifting the usual assumptions of independence and Boole's inequality typically leveraged in PSTN literature. Our approach offers on average a 10 times reduction in cost versus previous methods.

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Ibrahim, Sarmad, Aaron Cramer, Xiao Liu, and Yuan Liao. "PV inverter reactive power control for chance-constrained distribution system performance optimisation." IET Generation, Transmission & Distribution 12, no. 5 (March 13, 2018): 1089–98. http://dx.doi.org/10.1049/iet-gtd.2017.0484.

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Geletu, Abebe, Michael Klöppel, Hui Zhang, and Pu Li. "Advances and applications of chance-constrained approaches to systems optimisation under uncertainty." International Journal of Systems Science 44, no. 7 (July 2013): 1209–32. http://dx.doi.org/10.1080/00207721.2012.670310.

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López, Julio, Jose R. S. Mantovani, and Javier Contreras. "Reactive power planning under conditional-value-at-risk assessment using chance-constrained optimisation." IET Generation, Transmission & Distribution 9, no. 3 (February 19, 2015): 231–40. http://dx.doi.org/10.1049/iet-gtd.2014.0224.

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Wang, Xinwei, Alexander E. I. Brownlee, Michal Weiszer, John R. Woodward, Mahdi Mahfouf, and Jun Chen. "A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties." Transportation Research Part C: Emerging Technologies 132 (November 2021): 103382. http://dx.doi.org/10.1016/j.trc.2021.103382.

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Liu, Chunyang, Xiuli Wang, Jingli Guo, Minghuang Huang, and Xiong Wu. "Chance-constrained scheduling model of grid-connected microgrid based on probabilistic and robust optimisation." IET Generation, Transmission & Distribution 12, no. 11 (June 19, 2018): 2499–509. http://dx.doi.org/10.1049/iet-gtd.2017.1039.

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Zhao, Xia, Xiaobin Ye, Lun Yang, Rongrong Zhang, and Wei Yan. "Chance constrained dynamic optimisation method for AGC units dispatch considering uncertainties of the offshore wind farm." Journal of Engineering 2019, no. 16 (March 1, 2019): 2112–19. http://dx.doi.org/10.1049/joe.2018.8558.

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Zhao, Yi, Qingwan Xue, Zhichao Cao, and Xi Zhang. "A Two-Stage Chance Constrained Approach with Application to Stochastic Intermodal Service Network Design Problems." Journal of Advanced Transportation 2018 (December 24, 2018): 1–18. http://dx.doi.org/10.1155/2018/6051029.

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Анотація:

Compared with traditional freight transportation, intermodal freight transportation is more competitive which can combine the advantages of different transportation modes. As a consequence, operational research on intermodal freight transportation has received more attention and developed rapidly, but it is still a young research field. In this paper, a stochastic intermodal service network design problem is introduced in a sea-rail transportation system, which considers stochastic travel time, stochastic transfer time, and stochastic container demand. Given candidate train and ship services, we develop a two-stage chance constrained programming model for this problem with the objective of minimising the expected total cost. The first stage allows for the selection of operated services, while the second stage focuses on the determination of intermodal container routes where capacity and on-time delivery chance constraints are presented. A hybrid heuristic algorithm, incorporating sample average approximation and ant colony optimisation, is employed to solve this model. The proposed model is applied to a realistic intermodal sea-rail network, which demonstrates the performance of the model and algorithm as well as the influence of stochasticity on transportation plans. Hence, the proposed methodology can improve effectively the performance of intermodal service network design scheme under stochastic conditions and provide managerial insights for decision-makers.

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Sarfraz, Ali, Muhammad Shahzad Pansota, Nabeel Abdulhadi M. Fahal, Ahsan Sarfaraz, and Haseeb Javed. "Analytical Solution of Stochastic Real-time Power Dispatch with Large Scale Wind Farms." Pakistan Journal of Engineering and Technology 4, no. 3 (September 30, 2021): 18–26. http://dx.doi.org/10.51846/vol4iss3pp18-26.

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The automated gain control (AGC) units as well as other non AGC equipment may be utilized in real-time power transmitting (RTPD) to coordinate the operations (RTD). In order to guarantee high-probability system security and to save operating costs, it is essential to correctly define the probable Wind Energy Forecast (WPFE) mistakes in RTD. The Cauchy Distribution (CD) is the perfect match for the "leptokurtic" characteristic of WPFE small-scale distributions, following previous research and our onsite testing. In this study, the CD represents WPFE, which is suggested to provide a chance-controlled real-time dispatch (CCRTD) paradigm (Chance-Constrained Randomization). The suggested CCRTD Model may be analytically converted to the "Convex Optimisation Problem," which takes into consideration the dependency of the wind farm outputs because of the stability and attractive mathematical features of the CD. The inclusion of a refined control method that may also be used in combination with AGC systems is an additional aspect of the suggested model. This technique, when combined with the WPFE RTD Stage, allows the CCRTD to respond to the higher ramping power requirements as well as power variations on WPFE-generated transmittal lines. The proposed technique was shown to be trustworthy and efficient in numerical testing. It is nevertheless extremely effective as well as suitable for usage.

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Kapelan, Z., A. V. Babayan, D. A. Savic, G. A. Walters, and S. T. Khu. "Two new approaches for the stochastic least cost design of water distribution systems." Water Supply 4, no. 5-6 (December 1, 2004): 355–63. http://dx.doi.org/10.2166/ws.2004.0126.

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The problem of stochastic (i.e. robust) water distribution system (WDS) design is formulated and solved here as an optimisation problem under uncertainty. The objective is to minimise total design costs subject to a target level of system robustness. System robustness is defined as the probability of simultaneously satisfying minimum pressure head constraints at all nodes in the network. The decision variables are the alternative design options available for each pipe in the WDS. The only source of uncertainty analysed is the future water consumption uncertainty. Uncertain nodal demands are assumed to be independent random variables following some pre-specified probability density function (PDF). Two new methods are developed to solve the aforementioned problem. In the Integration method, the stochastic problem formulation is replaced with a deterministic one. After some simplifications, a fast numerical integration method is used to quantify the uncertainties. The optimisation problem is solved using the standard genetic algorithm (GA). The Sampling method solves the stochastic optimisation problem directly by using the newly developed robust chance constrained GA. In this approach, a small number of Latin Hypercube (LH) samples are used to evaluate each solution's fitness. The fitness values obtained this way are then averaged over the chromosome age. Both robust design methods are applied to a New York Tunnels rehabilitation case study. The optimal solutions are identified for different levels of robustness. The best solutions obtained are also compared to the previously identified optimal deterministic solution. The results obtained lead to the following conclusions: (1) Neglecting demand uncertainty in WDS design may lead to serious under-design of such systems; (2) Both methods shown here are capable of identifying (near) optimal robust least cost designs achieving significant computational savings.

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Fang, Cheng, Peng Yu, and Brian Williams. "Chance-Constrained Probabilistic Simple Temporal Problems." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 1 (June 21, 2014). http://dx.doi.org/10.1609/aaai.v28i1.9048.

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Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maximizing utility subject to these risk bounds. In this paper we present the probabilistic Simple Temporal Network (pSTN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing. We introduce a constrained optimisation algorithm for pSTNs that achieves compactness and efficiency through a problem encoding in terms of a parameterised STNU and its reformulation as a parameterised STN. We demonstrate through a car sharing application that our chance-constrained approach runs in the same time as the previous probabilistic approach, yields solutions with utility improvements of at least 5% over previous arts, while guaranteeing operation within the specified risk bound.

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Yang, Jia, Fengming Tao, and Yanni Zhong. "Dynamic routing for waste collection and transportation with multi-compartment electric vehicle using smart waste bins." Waste Management & Research: The Journal for a Sustainable Circular Economy, February 8, 2022, 0734242X2110697. http://dx.doi.org/10.1177/0734242x211069738.

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The municipal solid waste (MSW) collection and transportation issue has been studied by numerous researchers; however, a few studies consider the chance-constrained programming for co-collection of sorted waste with electric vehicles (EVs). Therefore, this article attempts to study on the chance-constrained collection and transportation problem for sorted waste with multiple separated compartments EVs. Considering the uncertainty of the waste generation rate under the scenario of application of smart waste bins, chance-constrained programming is applied to transform the uncertain model into a certain one. A Chance-Constrained Multi-Compartment Electric Vehicle Routing Problem (CCMCEVRP) is introduced and the corresponding mathematical formulation is established. A diversity-enhanced particle swarm optimisation with neighbourhood search and simulated annealing (DNSPSOSA) is proposed to solve this problem, and effectiveness of the proposed algorithms is verified by extensive numerical experiments on the newly generated instances. In addition, the application of the model is tested by comparing different compartment and different type vehicles. It is found that, compared with fuel vehicles, 32.66% of the average cost could be saved with EVs. Furthermore, the rate of cost-saving of EVs increases with the increase in the number of compartments: the improvement rate of cost-saving of two-compartment EVs and three-compartment EVs is 52.77% and 68.13%, respectively.

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Zhou, Yufeng, Ying Gong, Xiaoqing Hu, and Changshi Liu. "Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief." Grey Systems: Theory and Application, December 6, 2022. http://dx.doi.org/10.1108/gs-08-2022-0090.

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PurposeThe purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.Design/methodology/approachDifferent from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.FindingsA case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.Research limitations/implicationsThe casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.Practical implicationsConsidering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.Originality/value(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

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Paul, Sanjoy Kumar, Priyabrata Chowdhury, Ripon Kumar Chakrabortty, Dmitry Ivanov, and Karam Sallam. "A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item." Annals of Operations Research, April 11, 2022. http://dx.doi.org/10.1007/s10479-022-04650-2.

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AbstractThe COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

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