Introducing Psychological Factors Paper-Making Enterprises Optimal Scheduling Model

In the production scheduling model based on the traditional basis, from the multi-multi-objective optimization modeling to genetic algorithm’s optimization, introduces incentive mechanisms and the psychological factors, presents a paper-making enterprise based on genetic algorithm optimization Scheduling model. a simulation study and comparison of algorithms to verify the feasibility of the program and solve the problem of scheduling the order of optimization.

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Method of Improving Production Scheduling Based on the Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.411.415 ◽

2011 ◽

Vol 411 ◽

pp. 415-418

Author(s):

Yong Gao ◽

Ming Yu Li ◽

Jian Ping Wang

Keyword(s):

Genetic Algorithm ◽

Production Scheduling ◽

Model Simulation ◽

Optimization Method ◽

Production Model ◽

Forward Simulation ◽

Production Schedule ◽

Scheduling Optimization ◽

Simulation Data ◽

Scheduling Model

In order to improve the inventory control efficiency and quality in manufacturing company, one production scheduling optimization method is put forward. Simulation of production model is firstly constructed, such as description of the production model, simulation data, machine processes and scheduling model. Moreover, Genetic Algorithm is applied to generate a production schedule for efficient running of machine. The simulation result is analyzed to verify the method by comparing product simulation with actual production.

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Optimal Scheduling for Port Logistics Alliance Operations Based on Genetic Taboo Hybrid Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.71-78.4207 ◽

2011 ◽

Vol 71-78 ◽

pp. 4207-4210

Author(s):

Wei Zhang ◽

Rong Wu

Keyword(s):

Genetic Algorithm ◽

Hybrid Algorithm ◽

Optimal Allocation ◽

Optimal Scheduling ◽

Multi Objective Optimization ◽

Service Cost ◽

Logistics Service ◽

Port Logistics ◽

Climbing Ability ◽

Solution Efficiency

In this paper, optimal scheduling on logistics task and logistics resources for the port logistics alliance operations is discussed, the logistics service cost and time of each node is comprehensively considered, in order to establish a multi-objective optimization model. At the same time, improve genetic algorithm in use of the advantage of tabu search climbing ability to design a genetic taboo hybrid algorithm, which enhances the solution efficiency. Hope this study can effectively realize optimal allocation on logistics resources and tasks for port logistics alliance.

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Multi-Objective Optimization of Laboratory Technicians Scheduling using Binary Genetic Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7204.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 1349-1356

Keyword(s):

Genetic Algorithm ◽

Mathematical Model ◽

Computing Time ◽

Learning Activity ◽

Objective Functions ◽

Multi Objective Optimization ◽

Workload Balancing ◽

Multi Objective ◽

Scheduling Model ◽

Binary Genetic Algorithm

A laboratory needs at least one technician to maintain the laboratory’s activity every day. The technicians should prevent any technical interference in a daily learning activity. The technicians must be placed in a different lab the next day to check the work of the technician previously. This scheduling model has assigned 4 technicians into 3 laboratories in a month. We proposed a mathematical model for multi-objective optimization of laboratory technicians scheduling since it has many objective functions such as avoid collisions, workload balancing of technicians, and works distribution in the laboratories. We presented a Binary Genetic Algorithm to find the best technicians scheduling that can be used to support daily operations. As a result, we noticed that Binary GA could effectively be used in daily operational since the computing time was relatively short in finding the best laboratory technicians scheduling. From ten times of testing, the best solution needs 285.406s to calculate with the minimum function value is 2.

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Genetic Algorithm Based Methodology for Optimal Maintenance Scheduling of Multi-Unit Systems

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.450.539 ◽

2010 ◽

Vol 450 ◽

pp. 539-543 ◽

Cited By ~ 2

Author(s):

Ji Hong Yan ◽

Yu Yan Wang ◽

Xu Zhang

Keyword(s):

Genetic Algorithm ◽

Weibull Distribution ◽

Dynamic Characteristics ◽

Maintenance Scheduling ◽

Optimal Scheduling ◽

Maintenance Cost ◽

Cost Rate ◽

Unit System ◽

Scheduling Model ◽

Optimal Maintenance

This paper presents a novel methodology for optimal maintenance scheduling of multi-unit systems under predictive maintenance (PdM) environment. A maintenance scheduling model for multi-unit system is established considering performance degradation of units, dynamic characteristics of the system, economic dependence and structural dependence between units, and constraints of maintenance resources. The deterioration of units is modeled by Weibull distribution. Three maintenance actions, as minor repair, imperfect overhaul and replacement are considered to arrange the PdM schedule of a system. The genetic algorithm based methodology is employed to obtain the near optimal scheduling which results in a relatively minimal maintenance cost rate. The scheduling results demonstrate that the proposed methodology is feasible and effective.

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A new multi-objective optimization method for master production scheduling problems based on genetic algorithm

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-008-1481-x ◽

2008 ◽

Vol 41 (5-6) ◽

pp. 549-567 ◽

Cited By ~ 15

Author(s):

Marcio M. Soares ◽

Guilherme E. Vieira

Keyword(s):

Genetic Algorithm ◽

Production Scheduling ◽

Optimization Method ◽

Scheduling Problems ◽

Multi Objective Optimization ◽

Multi Objective ◽

Master Production Scheduling

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Research of Vehicle Scheduling with Time Window Based on Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.409-410.1307 ◽

2013 ◽

Vol 409-410 ◽

pp. 1307-1310

Author(s):

Xiao Rong Zhou ◽

Meng Tian Song ◽

Yu Ling Zhang

Keyword(s):

Genetic Algorithm ◽

Time Window ◽

Time Windows ◽

Distribution Center ◽

Optimal Scheduling ◽

Vehicle Scheduling ◽

Search Process ◽

Long Distance ◽

Soft Time Windows ◽

Scheduling Model

This paper based on the genetic algorithm,introduced part search process and respectively established the mathematical scheduling model of full loads vehicle optimal scheduling with soft time windows and of non-full loads on the basis of the long distance logistics transportation situation of some companys distribution center in Shenzhen. Then programmed to achieve the scheduling of multi-vehicle touting and selection, and conducted example analysis.

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