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Journal articles on the topic 'Scenario-Based Stochastic Programming'

Author: Grafiati

Published: 12 September 2021

Last updated: 2 February 2022

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1

Tarim, S. Armagan, Suresh Manandhar, and Toby Walsh. "Stochastic Constraint Programming: A Scenario-Based Approach." Constraints 11, no. 1 (2006): 53–80. http://dx.doi.org/10.1007/s10601-006-6849-7.

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Golpîra, Hêriş. "A Scenario Based Stochastic Time-Cost-Quality Trade-Off model for Project Scheduling Problem." International Journal of Management Science and Business Administration 2, no. 5 (2014): 7–12. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.25.1001.

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This paper formulates a new time–cost trade-off problem under some uncertainties for a multi-phase project. To do this, a new approach is proposed based on goal programming in compliance with scenario-based stochastic optimization formulation. To the best of our knowledge, this problem has not been extensively treated in the literature. Computational results show the applicability and usefulness of the proposed method.

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Li, He Peng, Chuan Zhi Zang, Peng Zeng, Hai Bin Yu, and Zhong Wen Li. "Scenario-Based Stochastic Programming Strategy for Microgrid Energy Scheduling Considering Uncertainties." Applied Mechanics and Materials 672-674 (October 2014): 1322–28. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1322.

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The inherent random and intermittence of the renewable energy resources pose a huge challenge to the Microgrid (MG) energy management systems (EMS). In order to mitigate the effects of uncertainties, we propose a novel two-stage stochastic programming model for the energy scheduling optimization by considering the uncertainties in solar and wind generation, and the plug-in electric vehicles (EV). The random nature of uncertainty is characterized by a scenarios generation approach based on autoregressive moving average (ARMA) model according to probability density function of each random variable. By use of the strategy of scenarios simulation, the stochastic problem is decomposed into the deterministic equivalent problem. The firefly algorithm (FA) is used to solve the equivalent model. The effectiveness and robust of proposed stochastic energy scheduling optimization strategy for MG is valid by comparison with the simulation results of deterministic method.

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JI, XIAODONG, XIUJUAN ZHAO, and XIULI CHAO. "A NOVEL METHOD FOR MULTISTAGE SCENARIO GENERATION BASED ON CLUSTER ANALYSIS." International Journal of Information Technology & Decision Making 05, no. 03 (2006): 513–30. http://dx.doi.org/10.1142/s0219622006002106.

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Based on cluster analysis, a novel method is introduced in this paper to generate multistage scenarios. A linear programming model is proposed to exclude the arbitrage opportunity by appending a scenario to the generated scenario set. By means of a cited stochastic linear goal programming portfolio model, a case is given to exhibit the virtues of this scenario generation approach.

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Chen, Zhi-Long, Shanling Li, and Devanath Tirupati. "A scenario-based stochastic programming approach for technology and capacity planning." Computers & Operations Research 29, no. 7 (2002): 781–806. http://dx.doi.org/10.1016/s0305-0548(00)00076-9.

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Mitra, Sovan, and Tong Ji. "Optimisation of stochastic programming by hidden Markov modelling based scenario generation." International Journal of Mathematics in Operational Research 2, no. 4 (2010): 436. http://dx.doi.org/10.1504/ijmor.2010.033439.

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Jayaraman, Raja, Cinzia Colapinto, Danilo Liuzzi, and Davide La Torre. "Planning sustainable development through a scenario-based stochastic goal programming model." Operational Research 17, no. 3 (2016): 789–805. http://dx.doi.org/10.1007/s12351-016-0239-8.

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8

Li, Y. P., G. H. Huang, and X. Chen. "Multistage scenario-based interval-stochastic programming for planning water resources allocation." Stochastic Environmental Research and Risk Assessment 23, no. 6 (2008): 781–92. http://dx.doi.org/10.1007/s00477-008-0258-y.

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Mohd Noh, Norshela, Arifah Bahar, and Zaitul Marlizawati Zainuddin. "Scenario Based Two-Stage Stochastic Programming Approach for the Midterm Production Planning of Oil Refinery." MATEMATIKA 34, no. 3 (2018): 45–55. http://dx.doi.org/10.11113/matematika.v34.n3.1138.

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Recently, oil refining industry is facing with lower profit margin due to uncertainty. This causes oil refinery to include stochastic optimization in making a decision to maximize the profit. In the past, deterministic linear programming approach is widely used in oil refinery optimization problems. However, due to volatility and unpredictability of oil prices in the past ten years, deterministic model might not be able to predict the reality of the situation as it does not take into account the uncertainties thus, leads to non-optimal solution. Therefore, this study will develop two-stage stochastic linear programming for the midterm production planning of oil refinery to handle oil price volatility. Geometric Brownian motion (GBM) is used to describe uncertainties in crude oil price, petroleum product prices, and demand for petroleum products. This model generates the future realization of the price and demands with scenario tree based on the statistical specification of GBM using method of moment as input to the stochastic programming. The model developed in this paper was tested for Malaysia oil refinery data. The result of stochastic approach indicates that the model gives better prediction of profit margin.

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10

Papavasiliou, A., S. S. Oren, and R. P. O'Neill. "Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework." IEEE Transactions on Power Systems 26, no. 4 (2011): 2197–206. http://dx.doi.org/10.1109/tpwrs.2011.2121095.

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11

송창훈 and 박석봉. "A Two-Stage Scenario-Based Stochastic Programming Approach To Pre-Positioning Emergency Supplies." Korean Journal of Military Art and Science 68, no. 2 (2012): 97–109. http://dx.doi.org/10.31066/kjmas.2012.68.2.005.

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12

Watkins, D. W., D. C. McKinney, L. S. Lasdon, S. S. Nielsen, and Q. W. Martin. "A scenario-based stochastic programming model for water supplies from the highland lakes." International Transactions in Operational Research 7, no. 3 (2000): 211–30. http://dx.doi.org/10.1111/j.1475-3995.2000.tb00195.x.

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13

Shao, Jian, Bu Han Zhang, Wei Si Deng, Kai Min Zhang, Bing Jie Jin, and Teng Yu Ge. "A Stochastic Programming Method for Unit Commitment of Wind Integrated Power System." Advanced Materials Research 732-733 (August 2013): 1390–95. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.1390.

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This paper presents a stochastic programming method that can assess the impact of wind generation uncertainties on unit comment (UC) problem. To model the uncertainty of wind genration, scenarios of wind speed are generated based on the known probability interval of forecasted wind speed and a scenario reduction technique limits the number of scenarios. The UC problem is modeled as a stochastic programming problem based on chance-constrained programming, and is decomposed into two embedded optimization sub-problems: the unit on/off status schedule problem and the load economic dispatch problem. Discrete particle swarm optimization (DPSO) and the equal incremental principle are used to solve the stochastic UC problem. The numerical results indicate that the proposed stochastic model is more suitable for wind integrated system with uncertainty.

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14

Ji, Xiaodong, Shushang Zhu, Shouyang Wang, and Shuzhong Zhang. "A stochastic linear goal programming approach to multistage portfolio management based on scenario generation via linear programming." IIE Transactions 37, no. 10 (2005): 957–69. http://dx.doi.org/10.1080/07408170591008082.

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15

Bayram, Vedat, and Hande Yaman. "A stochastic programming approach for Shelter location and evacuation planning." RAIRO - Operations Research 52, no. 3 (2018): 779–805. http://dx.doi.org/10.1051/ro/2017046.

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Shelter location and traffic allocation decisions are critical for an efficient evacuation plan. In this study, we propose a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to nearest shelters and to shortest paths within a tolerance degree to minimize the expected total evacuation time. Our model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for a potential earthquake in Istanbul. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values.

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16

Chung, Shu-Hsing, and Yi-Shu Yang. "A scenario-based stochastic programming model for the control or dummy wafers downgrading problem." Applied Stochastic Models in Business and Industry 25, no. 3 (2009): 263–74. http://dx.doi.org/10.1002/asmb.748.

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17

Chopra, Isha, and Dharmaraja Selvamuthu. "Scenario generation in stochastic programming using principal component analysis based on moment-matching approach." OPSEARCH 57, no. 1 (2019): 190–201. http://dx.doi.org/10.1007/s12597-019-00418-8.

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18

Niknam, Taher, Rasoul Azizipanah-Abarghooee, and Mohammad Rasoul Narimani. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation." Applied Energy 99 (November 2012): 455–70. http://dx.doi.org/10.1016/j.apenergy.2012.04.017.

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19

Rebennack, Steffen. "Combining sampling-based and scenario-based nested Benders decomposition methods: application to stochastic dual dynamic programming." Mathematical Programming 156, no. 1-2 (2015): 343–89. http://dx.doi.org/10.1007/s10107-015-0884-3.

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20

Qin, Yichen, Hoi-Lam Ma, Felix T. S. Chan, and Waqar Ahmed Khan. "A scenario-based stochastic programming approach for aircraft expendable and rotable spare parts planning in MRO provider." Industrial Management & Data Systems 120, no. 9 (2020): 1635–57. http://dx.doi.org/10.1108/imds-03-2020-0131.

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PurposeThis paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.Design/methodology/approachA stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.FindingsCompared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.Research limitations/implicationsThe results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.Practical implicationsExtending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.Originality/valueA novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.

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21

Samimi, Abouzar, and Ahad Kazemi. "Scenario-Based Stochastic Programming for Volt/Var Control in Distribution Systems With Renewable Energy Sources." IETE Technical Review 33, no. 6 (2016): 638–50. http://dx.doi.org/10.1080/02564602.2015.1135088.

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22

Maggioni, Francesca, Florian A. Potra, and Marida Bertocchi. "A scenario-based framework for supply planning under uncertainty: stochastic programming versus robust optimization approaches." Computational Management Science 14, no. 1 (2017): 5–44. http://dx.doi.org/10.1007/s10287-016-0272-3.

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23

Woo, Young-Bin, and Ilkyeong Moon. "Scenario-based stochastic programming for an airline-driven flight rescheduling problem under ground delay programs." Transportation Research Part E: Logistics and Transportation Review 150 (June 2021): 102360. http://dx.doi.org/10.1016/j.tre.2021.102360.

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24

Liu, Yajie, and Bo Guo. "A Lexicographic Approach to Postdisaster Relief Logistics Planning Considering Fill Rates and Costs under Uncertainty." Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/939853.

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Predicting the occurrences of earthquakes is difficult, but because they often bring huge catastrophes, it is necessary to launch relief logistics campaigns soon after they occur. This paper proposes a stochastic optimization model for post-disaster relief logistics to guide the strategic planning with respect to the locations of temporary facilities, the mobilization levels of relief supplies, and the deployment of transportation assets with uncertainty on demands. In addition, delivery plans for relief supplies and evacuation plans for critical population have been developed for each scenario. Two objectives are featured in the proposed model: maximizing the expected minimal fill rate of affected areas, where the mismatching distribution among correlated relief demands is penalized, and minimizing the expected total cost. An approximate lexicographic approach is here used to transform the bi-objective stochastic programming model into a sequence of single objective stochastic programming models, and scenario-decomposition-based heuristic algorithms are furthermore developed to solve these transformed models. The feasibility of the proposed bi-objective stochastic model has been demonstrated empirically, and the effectiveness of the developed solution algorithms has also been evaluated and compared to that of commercial mixed-integer optimization software.

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25

Ryan, Kevin, Shabbir Ahmed, Santanu S. Dey, Deepak Rajan, Amelia Musselman, and Jean-Paul Watson. "Optimization-Driven Scenario Grouping." INFORMS Journal on Computing 32, no. 3 (2020): 805–21. http://dx.doi.org/10.1287/ijoc.2019.0924.

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Scenario decomposition algorithms for stochastic programs compute bounds by dualizing all nonanticipativity constraints and solving individual scenario problems independently. We develop an approach that improves on these bounds by reinforcing a carefully chosen subset of nonanticipativity constraints, effectively placing scenarios into groups. Specifically, we formulate an optimization problem for grouping scenarios that aims to improve the bound by optimizing a proxy metric based on information obtained from evaluating a subset of candidate feasible solutions. We show that the proposed grouping problem is NP-hard in general, identify a polynomially solvable case, and present two formulations for solving the problem: a matching formulation for a special case and a mixed-integer programming formulation for the general case. We use the proposed grouping scheme as a preprocessing step for a particular scenario decomposition algorithm and demonstrate its effectiveness in solving standard test instances of two-stage 0–1 stochastic programs. Using this approach, we are able to prove optimality for all previously unsolved instances of a standard test set. Additionally, we implement this scheme as a preprocessing step for PySP, a publicly available and widely used implementation of progressive hedging, and compare this grouping approach with standard grouping approaches on large-scale stochastic unit commitment instances. Finally, the idea is extended to propose a finitely convergent algorithm for two-stage stochastic programs with a finite feasible region.

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26

Xie, Yulei, Linrui Wang, Guohe Huang, Dehong Xia, and Ling Ji. "A Stochastic Inexact Robust Model for Regional Energy System Management and Emission Reduction Potential Analysis—A Case Study of Zibo City, China." Energies 11, no. 8 (2018): 2108. http://dx.doi.org/10.3390/en11082108.

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In this study, in order to improve regional energy system adjustment, a multistage stochastic inexact robust programming (MSIRP) is proposed for electric-power generation planning and structure adjustment management under uncertainty. Scenario-based inexact multistage stochastic programming and stochastic robust optimization were integrated into general programming to reflect uncertainties that were expressed as interval values and probability distributions in the objective function and constraints. An MSIRP-based energy system optimization model is proposed for electric-power structure management of Zibo City in Shandong Province, China. Three power demand scenarios associated with electric-power structure adjustment, imported electricity, and emission reduction were designed to obtain multiple decision schemes for supporting regional sustainable energy system development. The power generation schemes, imported electricity, and emissions of CO2 and air pollutants were analyzed. The results indicated that the model can effectively not only provide a more stable energy supply strategies and electric-power structure adjustment schemes, but also improve the balanced development between conventional and new clear power generation technologies under uncertainty.

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27

Li, Xinran, Haoxuan Kan, Xuedong Hua, and Wei Wang. "Simulation-Based Electric Vehicle Sustainable Routing with Time-Dependent Stochastic Information." Sustainability 12, no. 6 (2020): 2464. http://dx.doi.org/10.3390/su12062464.

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We propose a routing method for electric vehicles that finds a route with minimal expected travel time in time-dependent stochastic networks. The method first estimates whether the vehicle can reach the destination with the current battery level and selects potential reasonable charging stations if needed. Then, the route-search problem is formulated as a shortest path problem with time-dependent stochastic disruptions, using a Markov decision process. The shortest path problem is solved by an approximate dynamic programming algorithm to improve calculation efficiency in complex networks. Several simulation cases and a scenario-based example are given to prove the validity of the method.

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Zheng, Xiao Li, Ji Chun Liu, Jia Yi Li, et al. "The Reserve Capacity Model Based on the Idea of Scenario in Power System." Advanced Materials Research 1008-1009 (August 2014): 173–78. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.173.

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According to the impact of the wind power prediction uncertainty on the power system reserve capacity, the idea of scenario is introduced to the stochastic programming model. The method of scenario is used to simulate the uncertainty model of the wind power generation, load and the conventional units. The scenario-reduction methodology is combined to reduce the large scenario set to a simpler one, then the probability statistics on these scenarios is given in order to obtain the probability density of the system power difference, and the expected energy not supplied (EENS) and expected wind waste risk (EWWR) are presented. The reserve capacity is determined by the two aspects, which are the reliability shown by EENS and EWWR, and the economy of reserve capacity cost. Finally, simulations on a ten-unit system are given to demonstrate the method is effective to reduce the cost of reserve and the abandoned wind power in the context of system reliability.

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Sabet, Ehsan, Baback Yazdani, Ramez Kian, and Kostas Galanakis. "A strategic and global manufacturing capacity management optimisation model: A Scenario-based multi-stage stochastic programming approach." Omega 93 (June 2020): 102026. http://dx.doi.org/10.1016/j.omega.2019.01.004.

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Li, Xiaohong, Dong Yang, and Mengqi Hu. "A scenario-based stochastic programming approach for the product configuration problem under uncertainties and carbon emission regulations." Transportation Research Part E: Logistics and Transportation Review 115 (July 2018): 126–46. http://dx.doi.org/10.1016/j.tre.2018.04.013.

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31

Slama, I., O. Ben-Ammar, F. Masmoudi, and A. Dolgui. "Scenario-based stochastic linear programming model for multi-period disassembly lot-sizing problems under random lead time." IFAC-PapersOnLine 52, no. 13 (2019): 595–600. http://dx.doi.org/10.1016/j.ifacol.2019.11.224.

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32

Ha, Jung-Su, Hyeok-Joo Chae, and Han-Lim Choi. "A Stochastic Game-Based Approach for Multiple Beyond-Visual-Range Air Combat." Unmanned Systems 06, no. 01 (2018): 67–79. http://dx.doi.org/10.1142/s2301385018500048.

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This paper addresses tactical decisions in beyond-visual-range (BVR) air combat between two adversarial teams of multiple (autonomous) aircraft. A BVR combat is formalized as a two-player stochastic game consisting of a sequence of normal-form games that determines on the number of missiles to be allocated to each adversary aircraft; within this normal-form game a continuous sub-game is embedded to determine the missile shooting times. The formulation reduces the size of decision space by taking advantage of the underlying symmetry of the combat scenario, and also facilitates incorporation of the effect of cooperative missile maneuvers and transition into within-visual-range (WVR) combat. The Nash equilibrium strategies and the associate value functions of the game are computed through linear-programming-based dynamic programming procedure. Numerical case studies on combat between airplanes with heterogeneous capabilities and cooperation effects demonstrate the validity of the proposed formulation and the effectiveness of the proposed solution scheme.

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Bakir, Ilke, Natashia Boland, Brian Dandurand, and Alan Erera. "Sampling Scenario Set Partition Dual Bounds for Multistage Stochastic Programs." INFORMS Journal on Computing 32, no. 1 (2020): 145–63. http://dx.doi.org/10.1287/ijoc.2018.0885.

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We consider multistage stochastic programming problems in which the random parameters have finite support, leading to optimization over a finite scenario set. There has been recent interest in dual bounds for such problems, of two types. One, known as expected group subproblem objective (EGSO) bounds, require solution of a group subproblem, which optimizes over a subset of the scenarios, for all subsets of the scenario set that have a given cardinality. Increasing the subset cardinality in the group subproblem improves bound quality, (EGSO bounds form a hierarchy), but the number of group subproblems required to compute the bound increases very rapidly. Another is based on partitions of the scenario set into subsets. Combining the values of the group subproblems for all subsets in a partition yields a partition bound. In this paper, we consider partitions into subsets of (nearly) equal cardinality. We show that the expected value of the partition bound over all such partitions also forms a hierarchy. To make use of these bounds in practice, we propose random sampling of partitions and suggest two enhancements to the approach: sampling partitions that align with the multistage scenario tree structure and use of an auxiliary optimization problem to discover new best bounds based on the values of group subproblems already computed. We establish the effectiveness of these ideas with computational experiments on benchmark problems. Finally, we give a heuristic to save computational effort by ceasing computation of a partition partway through if it appears unpromising.

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Lee, Yongdae, Sheung-Kown Kim, and Ick Hwan Ko. "Multistage stochastic linear programming model for daily coordinated multi-reservoir operation." Journal of Hydroinformatics 10, no. 1 (2008): 23–41. http://dx.doi.org/10.2166/hydro.2008.007.

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Operation planning for a coordinated multi-reservoir is a complex and challenging task due to the inherent uncertainty in inflow. In this study, we suggest the use of a new, multi-stage and scenario-based stochastic linear program with a recourse model incorporating the meteorological weather prediction information for daily, coordinated, multi-reservoir operation planning. Stages are defined as prediction lead-time spans of the weather prediction system. The multi-stage scenarios of the stochastic model are formed considering the reliability of rainfall prediction for each lead-time span. Future inflow scenarios are generated by a rainfall–runoff model based on the rainfall forecast. For short-term stage (2 days) scenarios, the regional data assimilation and prediction system (RDAPS) information is employed, and for mid-term stage (more than 2 days) scenarios, precipitation from the global data assimilation and prediction system (GDAPS) is used as an input for the rainfall–runoff model. After the 10th day (third stage), the daily historical rainfall data are used following the ensemble streamflow prediction (ESP) procedure. The model is applied to simulate the daily reservoir operation of the Nakdong River basin in Korea in a real-time operational environment. The expected benefit of the stochastic model is markedly superior to that of the deterministic model with average rainfall information. Our study results confirm the effectiveness of the stochastic model in real-time operation with meteorological forecasts and the presence of inflow uncertainty.

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Javadi Gargar, Farnaz i., and Mehdi Seifbarghy. "Solving multi-objective supplier selection and quota allocation problem under disruption using a scenario-based approach." Engineering review 40, no. 3 (2020): 78–89. http://dx.doi.org/10.30765/er.40.3.08.

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Nowadays, experts believe there are abundant sources of risks in a supply chain. An important group of risks against a supply chain is the disruption risks group, which disturbs the flow of material in the chain and may lead to inefficiency in providing the final product in the supply chain. The aim of this article is to investigate the control of costs of disruption in a supply chain by considering the possibility of disruption. In fact, this research focuses on determining the best combination of suppliers and quota allocation with regards to disruption in suppliers. The proposed multi-objective mathematical model in this paper is a mixed-integer programming (MIP) model with objective functions to minimize transaction costs of suppliers, expected costs of purchasing goods, expected percentages of delayed products, expected returned products, and to maximize expected evaluation scores of the selected suppliers. Due to the uncertainty of demand and supplier disruption in the real world, their values are also considered uncertain; the proposed multi-objective model is studied by using a scenario-based stochastic programming (SP) method. In this method, all possible predictions for demand and disruption values are simultaneously included in the model; objective function results have more optimal value than a separate solution of the model for each predicted value.

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Li, Xuan, Yu Zhou, Tianjun Liao, and Yajun Hu. "A Scenario Tree based Stochastic Programming Approach for Multi-Stage Weapon Equipment Mix Production Planning in Defense Manufacturing." MATEC Web of Conferences 51 (2016): 01010. http://dx.doi.org/10.1051/matecconf/20165101010.

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37

Zhang, Yuli, Shiji Song, Cheng Wu, and Wenjun Yin. "Dynamic Programming and Heuristic for Stochastic Uncapacitated Lot-Sizing Problems with Incremental Quantity Discount." Mathematical Problems in Engineering 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/582323.

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The stochastic uncapacitated lot-sizing problems with incremental quantity discount have been studied in this paper. First, a multistage stochastic mixed integer model is established by the scenario analysis approach and an equivalent reformulation is obtained through proper relaxation under the decreasing unit order price assumption. The proposed reformulation allows us to extend the production-path property to this framework, and furthermore we provide a more accurate characterization of the optimal solution. Then, a backward dynamic programming algorithm is developed to obtain the optimal solution and considering its exponential computation complexity in term of time stages, we design a new rolling horizon heuristic based on the proposed property. Comparisons with the commercial solver CPLEX and other heuristics indicate better performance of our proposed algorithms in both quality of solution and run time.

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Huang, Lufei, Liwen Murong, and Wencheng Wang. "Green closed-loop supply chain network design considering cost control and CO2 emission." Modern Supply Chain Research and Applications 2, no. 1 (2020): 42–59. http://dx.doi.org/10.1108/mscra-02-2019-0005.

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PurposeEnvironmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control.Design/methodology/approachA biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand.FindingsThe proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO2 emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO2 emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO2 emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO2 Emissions Control Reduction.” It can provide managers with a way to double-target conversion.Originality/valueWe proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.

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Hildebrandt, Torsten, and Jürgen Branke. "On Using Surrogates with Genetic Programming." Evolutionary Computation 23, no. 3 (2015): 343–67. http://dx.doi.org/10.1162/evco_a_00133.

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One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP.

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Chen, Yin-Yann, and Hsiao-Yao Fan. "An Application of Stochastic Programming in Solving Capacity Allocation and Migration Planning Problem under Uncertainty." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/741329.

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The semiconductor packaging and testing industry, which utilizes high-technology manufacturing processes and a variety of machines, belongs to an uncertain make-to-order (MTO) production environment. Order release particularly originates from customer demand; hence, demand fluctuation directly affects capacity planning. Thus, managing capacity allocation is a difficult endeavor. This study aims to determine the best capacity allocation with limited resources to maximize the net profit. Three bottleneck stations in the semiconductor packaging and testing process are mainly investigated, namely, die bond (DB), wire bond (WB), and molding (MD) stations. Deviating from previous studies that consider the deterministic programming model, customer demand in the current study is regarded as an uncertain parameter in formulating a two-stage scenario-based stochastic programming (SP) model. The SP model seeks to respond to sharp demand fluctuations. Even if future demand is uncertain, migration decision for machines and tools will still obtain better robust results for various demand scenarios. A hybrid approach is proposed to solve the SP model. Moreover, two assessment indicators, namely, the expected value of perfect information (EVPI) and the value of the stochastic solution (VSS), are adopted to compare the solving results of the deterministic planning model and stochastic programming model. Sensitivity analysis is performed to evaluate the effects of different parameters on net profit.

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Gençer, Hüseyin, and M. Hulusi Demir. "Optimization of Empty Container Repositioning in Liner Shipping." Business and Management Horizons 8, no. 1 (2020): 1. http://dx.doi.org/10.5296/bmh.v8i1.16327.

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Empty container repositioning (ECR), which arises due to imbalances in world trade, causes extra costs for the container liner carrier companies. Therefore, one of the main objectives of all liner carriers is to reduce ECR costs. Since ECR decisions involve too many parameters, constraints and variables, the plans based on real-life experiences cannot be effective and are very costly. For this purpose, this study introduces two mathematical programming models in order to make ECR plans faster, more efficient and at the lowest cost. The first mathematical programming model developed in this study is a mixed-integer linear programming (MILP) model and the second mathematical programming model is a scenario-based stochastic programming (SP) model, which minimize the total ECR costs. Unlike the deterministic model, the SP model takes into account the uncertainty in container demand. Both models have been tested with real data taken from a liner carrier company. The numerical results showed that, in a reasonable computational time, both models provide better results than real-life applications of the liner carrier company.

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Esmaeili, Saeid, Amjad Anvari-Moghaddam, Erfan Azimi, Alireza Nateghi, and João P. S. Catalão. "Bi-Level Operation Scheduling of Distribution Systems with Multi-Microgrids Considering Uncertainties." Electronics 9, no. 9 (2020): 1441. http://dx.doi.org/10.3390/electronics9091441.

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A bi-level operation scheduling of distribution system operator (DSO) and multi-microgrids (MMGs) considering both the wholesale market and retail market is presented in this paper. To this end, the upper-level optimization problem minimizes the total costs from DSO’s point of view, while the profits of microgrids (MGs) are maximized in the lower-level optimization problem. Besides, a scenario-based stochastic programming framework using the heuristic moment matching (HMM) method is developed to tackle the uncertain nature of the problem. In this regard, the HMM technique is employed to model the scenario matrix with a reduced number of scenarios, which is effectively suitable to achieve the correlations among uncertainties. In order to solve the proposed non-linear bi-level model, Karush–Kuhn–Tucker (KKT) optimality conditions and linearization techniques are employed to transform the bi-level problem into a single-level mixed-integer linear programming (MILP) optimization problem. The effectiveness of the proposed model is demonstrated on a real-test MMG system.

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Wang, Changjun, and Shutong Chen. "Planning of Cascade Hydropower Stations with the Consideration of Long-Term Operations under Uncertainties." Complexity 2019 (November 28, 2019): 1–23. http://dx.doi.org/10.1155/2019/1534598.

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In the location-related planning of a hydropower system, the consideration of future operations under uncertainties can make the decisions sustainable and robust. Then, it is of great importance to develop an effective approach that deals with the long-term stochasticity due to the long-lasting effects of the location selections. Thus, we propose a multistage stochastic programming model to optimize the planning decisions of cascade hydropower stations and the long-term stochastic operations in an integrated way. The first stage (i.e., the planning stage) in the model deals with the location and capacity decisions of the hydropower stations, while the subsequent stages implement the scheduling decisions under each stagewise stochastic scenario. To address the curse of dimensionality caused by the long-term stochastic operations, we further propose a novel dimensionality reduction approach based on dual equilibrium to transform the multistage model into a tractable two-stage stochastic program. The applicability of our approach is validated by a case study based on a basin of Yangtze River, China, and corresponding sensitivity analysis.

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Xie, Shiwei, Zhijian Hu, Daming Zhou, et al. "Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network." Applied Energy 219 (June 2018): 207–25. http://dx.doi.org/10.1016/j.apenergy.2018.03.023.

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Zhang, Joyce Li, and K. Ponnambalam. "Stochastic control for risk under deregulated electricity market — a case study using a new formulation." Canadian Journal of Civil Engineering 32, no. 4 (2005): 719–25. http://dx.doi.org/10.1139/l05-030.

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This paper describes the implementation of a new solution approach — Fletcher-Ponnambalam model (FP) — for risk management in hydropower system under deregulated electricity market. The FP model is an explicit method developed for the first and second moments of the storage state distributions in terms of moments of the inflow distributions. This method provides statistical information on the nature of random behaviour of the system state variables without any discretization and hence suitable for multi-reservoir problems. Also avoiding a scenario-based optimization makes it computationally inexpensive, as there is little growth to the size of the original problem. In this paper, the price uncertainty was introduced into the FP model in addition to the inflow uncertainty. Lake Nipigon reservoir system is chosen as the case study and FP results are compared with the stochastic dual dynamic programming (SDDP). Our studies indicate that the method could achieve optimum operations, considering risk minimization as one of the objectives in optimization.Key words: reservoir operations, explicit method, uncertainty, stochastic programming, risk.

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Nishimura, Haruki, and Mac Schwager. "SACBP: Belief space planning for continuous-time dynamical systems via stochastic sequential action control." International Journal of Robotics Research 40, no. 10-11 (2021): 1167–95. http://dx.doi.org/10.1177/02783649211037697.

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We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends sequential action control to stochastic dynamics. The resulting algorithm, which we name SACBP, does not require discretization of spaces or time and synthesizes control signals in near real-time. SACBP is an anytime algorithm that can handle general parametric Bayesian filters under certain assumptions. We demonstrate the effectiveness of our approach in an active sensing scenario and a model-based Bayesian reinforcement learning problem. In these challenging problems, we show that the algorithm significantly outperforms other existing solution techniques including approximate dynamic programming and local trajectory optimization.

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Wang, Lingling, Xu Wang, Chuanwen Jiang, Shuo Yin, and Meng Yang. "Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems." Applied Sciences 9, no. 6 (2019): 1129. http://dx.doi.org/10.3390/app9061129.

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This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly.

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Yu, L., Y. P. Li, B. G. Shan, G. H. Huang, and L. P. Xu. "A scenario-based interval-stochastic basic-possibilistic programming method for planning sustainable energy system under uncertainty: A case study of Beijing, China." Journal of Cleaner Production 197 (October 2018): 1454–71. http://dx.doi.org/10.1016/j.jclepro.2018.06.248.

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Bornapour, Mosayeb, Rahmat-Allah Hooshmand, and Moein Parastegari. "An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids." Renewable Energy 130 (January 2019): 1049–66. http://dx.doi.org/10.1016/j.renene.2018.06.113.

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Zhu, Mo, Michael Chen, and Murat Kristal. "MODELLING THE IMPACTS OF UNCERTAIN CARBON TAX POLICY ON MARITIME FLEET MIX STRATEGY AND CARBON MITIGATION." Transport 33, no. 3 (2018): 707–17. http://dx.doi.org/10.3846/transport.2018.1579.

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The maritime transport industry continues to draw international attention on significant Greenhouse Gas emissions. The introduction of emissions taxes aims to control and reduce emissions. The uncertainty of carbon tax policy affects shipping companies’ fleet planning and increases costs. We formulate the fleet planning problem under carbon tax policy uncertainty a multi-stage stochastic integer-programming model for the liner shipping companies. We develop a scenario tree to represent the structure of the carbon tax stochastic dynamics, and seek the optimal planning, which is adaptive to the policy uncertainty. Non-anticipativity constraint is applied to ensure the feasibility of the decisions in the dynamic environment. For the sake of comparison, the Perfect Information (PI) model is introduced as well. Based on a liner shipping application of our model, we find that under the policy uncertainty, companies charter more ships when exposed to high carbon tax risk, and spend more on fleet operation; meanwhile the CO2 emission volume will be reduced.

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