Stackcelberg Game Inventory Model With Progressive Permissible Delay of Payment Scheme

Author(s):  
Gede Agus Widyadana ◽  
Nita H. Shah ◽  
Daniel Suriawidjaja Siek

Supplier has many schemes to motivate retailer to buy more and of them one is a progressive permissible delay of payment. Instead of analyst from the retailer side alone, in this chapter, we develop the inventory model of supplier and retailer. In reality, some suppliers and retailers cannot have collaboration and they try to optimize their own decision so we develop a Stackelberg Game model. Two models are developed wherein the first model supplier acts as the leader and in the second model, the retailer acts a leader. Since the models are complex, a hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is developed to solve the model. A numerical analysis and sensitivity analysis are conducted to get management insights of the model. The results show that a Stackelberg Game model for progressive permissible delay of payment is sensitive in varies values of the first and second delay interest rate if supplier acts as a leader. The retailer gets less inventory cost when he acts as a leader compared to when vendor acts a leader at high interest rate of the first and second delay period.

2010 ◽  
Vol 40-41 ◽  
pp. 410-418
Author(s):  
Ting Ting Zhou ◽  
Ying Zheng ◽  
Ming Chen

Since the usable range of the frequency spectrum is limited, the frequency assignment problem (FAP) is important in mobile telephone communication. In this paper, according to the characteristics of engineering- oriented FAP, an engineering-oriented hybrid genetic algorithm (EHGA) based on traditional genetic algorithm (TGA) is proposed, combined with particle swarm optimization (PSO) and simulated annealing (SA). The results obtained by the simulation to a real-word FAP case in GSM show that the algorithm we proposed is a better approach to solve the engineering-oriented FAP.


2012 ◽  
Vol 498 ◽  
pp. 115-125 ◽  
Author(s):  
H. Hachimi ◽  
Rachid Ellaia ◽  
A. El Hami

In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted asGA-PSO, this hybrid technique incorporates concepts fromGAandPSOand creates individuals in a new generation not only by crossover and mutation operations as found inGAbut also by mechanisms ofPSO. The performance of the two algorithms has been evaluated using several experiments.


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