A Risk Decision Method for Emergency Response Based on Cumulative Prospect Theory

2011 ◽  
Author(s):  
Yang Liu ◽  
Zhi-Ping Fan ◽  
Fa-Dong Chen
Keyword(s):  
Prospect Theory ◽  
Emergency Response ◽  
Cumulative Prospect Theory ◽  
Decision Method ◽  
Risk Decision

Research on disruption management of urgent arrival in job shop with deteriorating effect

2021 ◽  
pp. 1-13
Author(s):  
Ning Tao ◽  
Duan Xiaodong ◽  
An Lu ◽  
Gou Tao
Keyword(s):  
Prospect Theory ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Cumulative Prospect Theory ◽  
Disruption Management ◽  
Particle Swarm Algorithm ◽  
Shop Scheduling ◽  
Management Method

A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified.

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