Data Processing with a Genetic Algorithm for Flow Line Processing Scheduling Problem

2014 ◽  
Vol 685 ◽  
pp. 630-633
Author(s):  
Fang Guo He
Keyword(s):  
Genetic Algorithm ◽  
Data Processing ◽  
Flow Line ◽  
Flow Time ◽  
Scheduling Problem ◽  
Computational Results ◽  
Total Flow ◽  
Scheduling Optimization ◽  
Encoding Method ◽  
Working Procedure

The scheduling optimization of the flow line is a core of modern managing technology and Data Processing. The goal of the problem is to minimize the sum of the total flow time. Aiming at machine scheduling of production process, this paper presents a genetic algorithm based heuristic for the problem. An encoding method based working procedure and parthenogenetic operations are applied to solve the flow line scheduling problem. The computational results indicate that the proposed approach is effective in terms of reduced makespan for the attempted problems.

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.

An Efficient Batch Scheduling Model for Hospital Sterilization Services Using Genetic Algorithm

2021 ◽  
pp. 928-946
Author(s):  
Shubin Xu ◽  
John Wang
Keyword(s):  
Genetic Algorithm ◽  
Medical Devices ◽  
Programming Model ◽  
Flow Time ◽  
Nonlinear Integer Programming ◽  
Batch Scheduling ◽  
Scheduling Problem ◽  
Work In Process ◽  
Completion Times ◽  
Sum Of Completion Times

A major challenge faced by hospitals is to provide efficient medical services. The problem studied in this article is motivated by the hospital sterilization services where the washing step generally constitutes a bottleneck in the sterilization services. Therefore, an efficient scheduling of the washing operations to reduce flow time and work-in-process inventories is of great concern to management. In the washing step, different sets of reusable medical devices may be washed together as long as the washer capacity is not exceeded. Thus, the washing step is modeled as a batch scheduling problem where washers have nonidentical capacities and reusable medical device sets have different sizes and different ready times. The objective is to minimize the sum of completion times for washing operations. The problem is first formulated as a nonlinear integer programming model. Given that this problem is NP-hard, a genetic algorithm is then proposed to heuristically solve the problem. Computational experiments show that the proposed algorithm is capable of consistently obtaining high-quality solutions in short computation times.

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