scholarly journals Strategic Behavior of Customers and Optimal Control for Batch Service Polling Systems with Priorities

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Tao Jiang ◽  
Xingzheng Lu ◽  
Lu Liu ◽  
Jun Lv ◽  
Xudong Chai
Keyword(s):  
Strategic Behavior ◽  
Particle Swarm Optimization Algorithm ◽  
Polling Systems ◽  
Batch Service ◽  
Polling System ◽  
Swarm Optimization ◽  
Equilibrium Strategies ◽  
The Past ◽  
Optimal Service ◽  
Revenue Function

During the past few years, batch service systems have attracted considerable attention due to their wide area of applications. In this present paper, we study a special batch service polling system (the so-called Israeli queue) with priorities. Different from the previous papers which focus on the performance analysis, we aim to investigate the strategic behavior of customers and optimal design for the underlying queueing model. By considering two levels of information (observable and unobservable) provided upon customers’ arrival, we, respectively, derive the equilibrium strategies of high-priority and low-priority customers, regarding the joining or balking dilemma. We also present some numerical examples to reveal the impacts of several parameters on the equilibrium strategies, together with some intuitive explanations. Finally, we formulate the revenue function of the service provider and present the Particle Swarm Optimization algorithm to seek the optimal service prices for the high-priority and low-priority customers to maximize the service provider’s revenue under the two levels of information.

Parameter Estimation of Nonlinear Muskingum Models Based on OEPSO

2014 ◽  
Vol 599-601 ◽  
pp. 1588-1592 ◽  
Author(s):  
Bin Li ◽  
Jian Cang Xie ◽  
Gang Zhang
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization Algorithm ◽  
Swarm Optimization ◽  
Competition And Cooperation ◽  
The Past ◽  
Muskingum Model ◽  
Muskingum Routing ◽  
Nonlinear Relation ◽  
Parameter Estimation Techniques ◽  
Two Parameter

In the past, various methods have been used to estimate the parameters in the nonlinear three-parameter Muskingum model to allow the model to more closely approximate a nonlinear relation compared to the original two-parameter Muskingum model. In this study, the particle swarm optimization algorithm based on the organizational evolutionary (OEPSO), which the evolutional operations are acted on organizations directly in the algorithm, and gained the global convergence ends through competition and cooperation, and overcome the shortcomings of the traditional PSO, is introduced. The OEPSO is proposed for the purpose of estimating the parameters of nonlinear Muskingum routing model. The performance of this approach is compared with other reported parameter estimation techniques. Results of the application of this approach to an example with high nonlinearity between storage and weighted-flow, show that the OEPSO approach is efficient in estimating parameters of the nonlinear routing models.

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