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Showing 3 results for Fuzzy Random

Jiuping Xu, Pei Wei,
Volume 10, Issue 1 (3-2012)

In this paper, a location allocation (LA) problem in construction and demolition (C&D) waste management (WM) is studied. A bi-level model for this problem under a fuzzy random environment is presented where the upper level is the governments who sets up the processing centers, and the lower level are the administrators of different construction projects who control C&D waste and the after treatment materials supply. This model using an improved particle swarm optimization program based on a fuzzy random simulation (IPSO-based FRS) is able to handle practical issues. A case study is presented to illustrate the effectiveness of the proposed approach. Conclusions and future research directions are discussed.

Yanfang Ma, Jiuping Xu,
Volume 12, Issue 2 (6-2014)

In this paper, a bi-level decision making model is proposed for a vehicle routing problem with multiple decision-makers (VRPMD) in a fuzzy random environment. In our model, the objective of the leader is to minimize total costs by deciding the customer sets, while the follower is trying to minimize routing costs by choosing routes for each vehicle. Demand for each item has considerable uncertainty, so customer demand is considered a fuzzy random factor in this paper. After setting up the bi-level programming model for VRPMD, a bi-level global-local-neighbor particle swarm optimization with fuzzy random simulation (bglnPSO-frs) is developed to solve the bi-level fuzzy random model. Finally, the proposed model and method are applied to construction material transportation in the Yalong River Hydropower Base in China to illustrate its effectiveness.
Jiuping Xu, Qiurui Liu, Zhonghua Yang,
Volume 15, Issue 1 (1-2017)

To fully explain hydropower unit operational problems, an optimal multi-objective dynamic scheduling model is presented which seeks to improve the efficiency of reservation regulation management. To reflect the actual hydropower engineering project environment, fuzzy random uncertainty and an integrated consideration of the natural resource constraints, such as load balance, system power balance, generation limits, turbine capacity, water head, discharge capacities, reservoir storage volumes, and water spillages, were included in the model. The aim of this research was to concurrently minimize discharges and maximize economic benefit. Subsequently, a new hybrid dynamic-programming based multi-start multi-objective simulated annealing algorithm was developed to solve the hydro unit operational problem. The proposed model and intelligent algorithm were then applied to the Xiaolongmen Hydraulic and Hydropower Station in China. The computational unit commitment schedule results demonstrated the practicality and efficiency of this optimization method.

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