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.

Dynamic Scheduling Method for Steelmaking-Continuous Casting

2010 ◽  
Vol 44-47 ◽  
pp. 2162-2167 ◽  
Author(s):  
Dong Feng He ◽  
Ai Jun Xu ◽  
Gang Yu ◽  
Nai Yuan Tian
Keyword(s):  
Genetic Algorithm ◽  
Continuous Casting ◽  
Dynamic Scheduling ◽  
Utilization Rate ◽  
Production Data ◽  
High Quality ◽  
Static Scheduling ◽  
Scheduling Method ◽  
Scheduling Plan ◽  
Generation Period

A method of dynamic scheduling of steelmaking-continuous casting is proposed, which includes static scheduling based on genetic algorithm and dynamic scheduling based on scheduling rules, mathematical model and complete rescheduling utilizing genetic algorithm. The simulation with eight hours’ production data in S steel plant showed that the method could draw quickly a high quality and performable dynamic scheduling plan according to random production disturbance. The average utilization rate of converter could reach 95% and the generation period of initial scheduling is less than 3 minutes. The max dynamic scheduling adjustment period did not exceed 1 minute.

scholarly journals A Dynamic Scheduling Method of Earth-Observing Satellites by Employing Rolling Horizon Strategy

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Qiu Dishan ◽  
He Chuan ◽  
Liu Jin ◽  
Ma Manhao
Keyword(s):  
Integer Programming ◽  
Dynamic Scheduling ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Scheduling Algorithms ◽  
Scheduling Problem ◽  
Rolling Horizon ◽  
Static Scheduling ◽  
Event Triggering ◽  
Scheduling Method

Focused on the dynamic scheduling problem for earth-observing satellites (EOS), an integer programming model is constructed after analyzing the main constraints. The rolling horizon (RH) strategy is proposed according to the independent arriving time and deadline of the imaging tasks. This strategy is designed with a mixed triggering mode composed of periodical triggering and event triggering, and the scheduling horizon is decomposed into a series of static scheduling intervals. By optimizing the scheduling schemes in each interval, the dynamic scheduling of EOS is realized. We also propose three dynamic scheduling algorithms by the combination of the RH strategy and various heuristic algorithms. Finally, the scheduling results of different algorithms are compared and the presented methods in this paper are demonstrated to be efficient by extensive experiments.

scholarly journals A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Building Blocks ◽  
Allocation Problem ◽  
Accurate Analysis ◽  
Allocation Problems ◽  
Scheduling Method ◽  
Project Scheduling Problem ◽  
Block Based ◽  
Better Than

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

Solving Machine Loading Problem of FMS

2011 ◽  
pp. 19-43 ◽  
Author(s):  
Anoop Prakash ◽  
Nagesh Shukla ◽  
Ravi Shankar ◽  
Manoj Kumar Tiwari
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Random Search ◽  
Search Algorithms ◽  
Ant Colony ◽  
Complex Nature ◽  
Machine Loading ◽  
Loading Problem

Artificial intelligence (AI) refers to intelligence artificially realized through computation. AI has emerged as one of the promising computer science discipline originated in mid-1950. Over the past few decades, AI based random search algorithms, namely, genetic algorithm, ant colony optimization, and so forth have found their applicability in solving various real-world problems of complex nature. This chapter is mainly concerned with the application of some AI based random search algorithms, namely, genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA), artificial immune system (AIS), and tabu search (TS), to solve the machine loading problem in flexible manufacturing system. Performance evaluation of the aforementioned search algorithms have been tested over standard benchmark dataset. In addition, the results obtained from them are compared with the results of some of the best heuristic procedures in the literature. The objectives of the present chapter is to make the readers fully aware about the intricate solutions existing in the machine loading problem of flexible manufacturing systems (FMS) to exemplify the generic procedure of various AI based random search algorithms. Also, the present chapter describes the step-wise implementation of search algorithms over machine loading problem.

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.

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