scholarly journals Dynamic Surgery Scheduling Based on an Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bingbing Zhang ◽  
Qiang Su
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Efficient Solution ◽  
Dynamic Scheduling ◽  
Idle Time ◽  
Scheduling Problem ◽  
Surgery Scheduling ◽  
Programming Formulation ◽  
Improved Genetic Algorithm ◽  
Experienced Nurses

We formulated a new stochastic programming formulation to solve the dynamic scheduling problem in a given set of elective surgeries in the day of operation. The problem is complicated by the fact that the exact surgery durations are not known in advance. Elective surgeries could be performed in parallel in a subset of operating rooms. The appointment times and assignments of surgeries were planned by an experienced nurses in advance. We present a mathematical model to capture the nature of dynamic scheduling problem. We propose an efficient solution based on an improved genetic algorithm (IGA). Our numerical results showed that dynamic scheduling with the IGA improves the resource utilization as measured by surgeon waiting time and operation room idle time.

scholarly journals A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Wenxiang Xu ◽  
Shunsheng Guo ◽  
Xixing Li ◽  
Chen Guo ◽  
Rui Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Dynamic Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Intelligent Manufacturing ◽  
Automated Guided Vehicles ◽  
Scheduling Problem ◽  
Scheduling Model ◽  
Intelligent Logistics ◽  
Simultaneous Scheduling

Aiming at the logistics dynamic scheduling problem in an intelligent manufacturing workshop (IMW), an intelligent logistics scheduling model and response method with Automated Guided Vehicles (AGVs) based on the mode of “request-scheduling-response” were proposed, and they were integrated with Internet of Things (IoT) to meet the demands of dynamic and real time. Correspondingly, a mathematical model was developed and integrated with a double-level hybrid genetic algorithm and ant colony optimization (DLH-GA-ACO) to minimize the finish time with the minimum AGVs and limited time. The mathematical model optimized the logistics scheduling process on two dimensions which include the sequence of tasks assigned to an AGV and the matching relation between transfer tasks and AGVs (AGV-task). The effectiveness of the model was verified by a set of experiments, and comparison among DLH-GA-ACO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO), and tabu search algorithm (TSA) was performed. In the experiments, the DLH-GA-ACO ran in a distributed environment for a faster computing speed. According to the comparisons, the superiority and effectiveness of DLH-GA-ACO on dynamic simultaneous scheduling problem were proved and the intelligent logistics scheduling model was also proved to be an effective model.

Research of Flexible Dynamic Scheduling Problem Based on Genetic Algorithm

2009 ◽  
Vol 16-19 ◽  
pp. 743-747
Author(s):  
Yu Wu ◽  
Xin Cun Zhuang ◽  
Cong Xin Li
Keyword(s):  
Genetic Algorithm ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Dynamic Scheduling ◽  
Scheduling Problem ◽  
Static Scheduling ◽  
Coding Method ◽  
Scheduling Method ◽  
Distribution Principle ◽  
Working Procedure

Solve the flexible dynamic scheduling problem by using “dynamic management & static scheduling” method. Aim at the property of flexible Manufacturing systems, the dynamic scheduling methods are analyzed and a coding method based on working procedure is improved in this paper. Thus it can be efficiently solve the problem of multiple working routes selection under the active distribution principle. On the other hand, the self-adaptive gene is provided and the parameters of the genetic algorithm are defined. In such a solution, the scheduling is confirmed to be simple and efficient.

Intelligent Generating Examination Paper from Item Bank Based on Improved Genetic Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 3637-3640
Author(s):  
Li Feng ◽  
Qian Wu ◽  
Jing Shao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Computational Study ◽  
Item Bank ◽  
Test Paper ◽  
Improved Genetic Algorithm ◽  
Fitness Evaluation ◽  
Simulation Results ◽  
The Mathematical Model ◽  
Examination Paper

In this paper, we analyze the disadvantage of common generating test paper algorithm. An improved genetic algorithm (IGA) is proposed and used in auto-generating examination paper algorithm. We design the mathematical model of auto-generating test paper algorithm and improved the traditional GA fitness evaluation form. A computational study is carried out to verify the algorithm. Simulation results demonstrate that the performance of IGA can work efficiently than traditional ones.

An Improved Genetic Algorithm For Just-In-Time Job-Shop Scheduling Problem

2012 ◽  
Vol 472-475 ◽  
pp. 2462-2467 ◽  
Author(s):  
Hong An Yang ◽  
Jin Yuan Li ◽  
Liang Liang Qi
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Idle Time ◽  
Just In Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Insertion Mechanism ◽  
Early Operations

This paper studies a just-in-time job-shop scheduling problem (JITJSSP) in which each operation has an earliness cost or a tardiness cost if it is completed before or after its due date and the objective function is to minimize the total earliness and tardiness costs of all operations. In order to solve this problem, an improved genetic algorithm (IGA) is introduced in this paper. IGA utilizes an operation-based scheme to represent schedules as chromosomes. Then, each chromosome is processed through a three-stage mechanism. Firstly, the semi-active decoding process is employed to expand the search space of solutions and guarantee comprehensive solutions. Secondly, the greedy insertion mechanism for tardy operations is executed to move the tardy operations left to the appropriate idle time to reduce the tardiness costs. Finally, the greedy insertion mechanism for early operations is proposed to shift the early operations right to the suitable idle time to decrease the earliness costs. After the maximum number of generations is reached, IGA continues with selection, crossover and mutation. The experimental results finally show that most of solutions on the benchmarks are improved by our algorithm.

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