Method of Improving Production Scheduling Based on the Genetic Algorithm

2011 ◽  
Vol 411 ◽  
pp. 415-418
Author(s):  
Yong Gao ◽  
Ming Yu Li ◽  
Jian Ping Wang

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.

2012 ◽  
Vol 263-266 ◽  
pp. 3177-3183
Author(s):  
Fang Li ◽  
Yu Wang ◽  
Ying Chun Zhong ◽  
Zhi Tan

An optimization of multi-varieties and small-batch of production scheduling is proposed, which is embodied the utilization ratio of equipment. First, the production scheduling model with multi-varieties and small-batch is improved by adding a new constraint. Second, the feeding behavior, clustering and rear collision of artificial fish algorithm are improved in order to describe the multi-varieties and small-batch of production scheduling. Finally, the optimizing results influenced by iteration times and quantity of artificial fish are analyzed. The experiments show that the utilization ratio of equipments are nearly same and the Man Hour is decreased obviously while the optimization method is used, which testifies the validity of the new optimization method.


Author(s):  
Guoxi He ◽  
Yongtu Liang ◽  
Limin Fang ◽  
Qi Zheng ◽  
Liying Sun

The disconnect between the optimization systems of upstream production and downstream demand poses a legitimate problem for China’s refined oil industry in terms of overproduction waste. Established methods only partially model the refinery system and are unable to integrate detailed production plans or meet market demands. Therefore, the research on production scheduling optimization combined with the demand of downstream pipeline network has very real applications that not only reduce the consumption of human/material resources, but also increase economic efficiency. This paper aims to optimize the production scheduling of refined oil transportation based on the demand of downstream product pipelines by analyzing the relationships between crude oil supply, refinery facility capacities and refinery tanks storage. The new model will minimize the refined production surplus therefore minimizing refinery costs and wastage. This is done by implementing models custom designed to optimize the three subsystems of the overall process: oil product blending scheduling optimization, producing and processing equipment scheduling optimization, and mixed crude oil scheduling optimization. We first analyzed the relationship between all the production units from the crude oil to the distributional destinations of oil products. A mathematical model of the refinery production scheduling was then built with minimum total surplus inventory as the objective function. We assumed a known downstream demand and used a step by step model to optimize oil stocks. The oil blending plan, production scheduling, amount of crude oil, and refined oil mixing ratios were all derived from the model using three methods: a nonlinear method called Particle Swarm Optimization (PSO), the simplex method and the enumeration method. The evidence laid out in this paper verifies our models functionality and suggests that systems can be significantly optimized by using these methods which can provide solutions for industries with similar challenges. Optimization of the refinery’s overall production process is achieved by implementing models for each of the three distinguished subsystems: oil blending model, plant scheduling model, and the mixed crude oil refining model. The demand dictates the final production quantities. From those figures we are able to place constraining limits on the input crude oil. The refined oil production scheme is continuously enhanced by determining the amount of constituent feed on the production equipment according to the results of previous production cycle. After optimization, the minimum surplus inventory of the five oil components approach their lower limits that were calculated using our models. We compare the literature on scheduling optimization challenges both in China and abroad while providing a detailed discussion of the present situation of Chinese refineries. The interrelationships of production processes on each other are revealed by analyzing the system and breaking it down to three fundamental parts. Basing the final production predictions on the downstream demand, we are able to achieve a minimum refinery surplus inventory by utilizing a comprehensive refinery scheduling model composed of three sub-models.


2015 ◽  
Vol 744-746 ◽  
pp. 1827-1831
Author(s):  
Cheng Zhi Chang ◽  
Xu Mei Chen ◽  
Meng Wang

The goal is to minimize the sum of operating cost and passengers’ travel cost, and establish an optimized combinational scheduling model of Bus Rapid Transit (BRT) combined with regular bus, express bus and shuttle bus. A mixed genetic algorithm based on tabu search algorithm (GA-TS) has been designed after analyzing the fundamental principle of genetic algorithm (GA) and tabu search (TS). A case study has been carried out on the combinational scheduling optimization of a selecting BRT line. By adopting the combinational scheduling model, 5.24% of the total system cost can be saved, which is quite prominent. The mixed genetic algorithm based on GA-TS can optimize the BRT scheduling system, shorten the turnaround time of operating BRT vehicles, effectively reduce the total system cost of BRT and improve decision-making efficiency and service quality.


2011 ◽  
Vol 219-220 ◽  
pp. 370-374
Author(s):  
Ming Hui Li ◽  
Xian Kun Meng

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.


2014 ◽  
Vol 1036 ◽  
pp. 885-890 ◽  
Author(s):  
Iwona Paprocka ◽  
Wojciech M. Kempa ◽  
Krzysztof Kalinowski ◽  
Cezary Grabowik

In the paper, a production model with maintenance is presented. Successive failure-free times of a bottleneck are supposed to have predefined distributions and are followed by distributed times of repair. Having values of parameters: Mean Time To Failure and Mean Time of Repair, a predictive schedule is generated. To assess wastes due to unplanned events of the bottleneck, such as unplanned downtime the Overall Equipment Effectiveness indicator is applied. To assess failure rate of the bottleneck the Parts Per Million Opportunities indicator is applied. Prediction capability, detection capability of a failure and effects of the failure occurrence are evaluated and registered in the Exploitation Failure Mode and Effects Analysis form. The objective of the presented predictive scheduling model is to achieve: zero machines failures, zero defects, zero accidents at work.


2013 ◽  
Vol 307 ◽  
pp. 443-446 ◽  
Author(s):  
Iwona Paprocka ◽  
Bożena Skołud

In the paper a production model with failures is presented where successive failure-free times are supposed to have normal distributions and are followed by normally distributed times of repairs. Unknown parameters of the distribution are estimated using e.g. empirical moments approach. Predictions of unknown parameters are done using classical regression method. Having Mean Time To First Failure, and Mean Time of Repair a disturbance robust predictive schedule is generated using an immune algorithm and rule Minimal Impact of Disturbed Operation on the Schedule.


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