scholarly journals People-Centric Evolutionary System for Dynamic Production Scheduling

2020 ◽  
Author(s):  
Su Nguyen ◽  
Mengjie Zhang ◽  
Damminda Alahakoon ◽  
Kay Chen Tan
Keyword(s):  
Production Scheduling ◽  
Systematic Approach ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Evolutionary Process ◽  
Decision Makers ◽  
Mapping Technique ◽  
Evolutionary System ◽  
Production Environments ◽  
Dynamic Production

Evolving production scheduling heuristics is a challenging task because of the dynamic and complex production environments and the interdependency of multiple scheduling decisions. Different genetic programming (GP) methods have been developed for this task and achieved very encouraging results. However, these methods usually have trouble in discovering powerful and compact heuristics, especially for difficult problems. Moreover, there is no systematic approach for the decision makers to intervene and embed their knowledge and preferences in the evolutionary process. This article develops a novel people-centric evolutionary system for dynamic production scheduling. The two key components of the system are a new mapping technique to incrementally monitor the evolutionary process and a new adaptive surrogate model to improve the efficiency of GP. The experimental results with dynamic flexible job shop scheduling show that the proposed system outperforms the existing algorithms for evolving scheduling heuristics in terms of scheduling performance and heuristic sizes. The new system also allows the decision makers to interact on the fly and guide the evolution toward the desired solutions.

scholarly journals People-Centric Evolutionary System for Dynamic Production Scheduling

2020 ◽  
Author(s):  
Su Nguyen ◽  
Mengjie Zhang ◽  
Damminda Alahakoon ◽  
Kay Chen Tan
Keyword(s):  
Production Scheduling ◽  
Systematic Approach ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Evolutionary Process ◽  
Decision Makers ◽  
Mapping Technique ◽  
Evolutionary System ◽  
Production Environments ◽  
Dynamic Production

Evolving production scheduling heuristics is a challenging task because of the dynamic and complex production environments and the interdependency of multiple scheduling decisions. Different genetic programming (GP) methods have been developed for this task and achieved very encouraging results. However, these methods usually have trouble in discovering powerful and compact heuristics, especially for difficult problems. Moreover, there is no systematic approach for the decision makers to intervene and embed their knowledge and preferences in the evolutionary process. This article develops a novel people-centric evolutionary system for dynamic production scheduling. The two key components of the system are a new mapping technique to incrementally monitor the evolutionary process and a new adaptive surrogate model to improve the efficiency of GP. The experimental results with dynamic flexible job shop scheduling show that the proposed system outperforms the existing algorithms for evolving scheduling heuristics in terms of scheduling performance and heuristic sizes. The new system also allows the decision makers to interact on the fly and guide the evolution toward the desired solutions.

Production Scheduling and Rescheduling with Genetic Algorithms

1999 ◽  
Vol 7 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Christian Bierwirth ◽  
Dirk C. Mattfeld
Keyword(s):  
Production Scheduling ◽  
Production Control ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Dynamic Environment ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Production Environments ◽  
Efficient Decoding

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed atreasonable runtime costs.

scholarly journals Development of a Web Application for Dynamic Production Scheduling in Small and Medium Enterprises

Organizacija ◽  
2010 ◽  
Vol 43 (3) ◽  
pp. 125-135 ◽  
Author(s):  
Davorin Kofjač ◽  
Andrej Knaflič ◽  
Miroljub Kljajić
Keyword(s):  
Production Scheduling ◽  
Web Application ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Small And Medium Enterprises ◽  
Appropriate Technology ◽  
Web Based ◽  
Shop Scheduling ◽  
Medium Enterprises ◽  
Dynamic Production

Development of a Web Application for Dynamic Production Scheduling in Small and Medium EnterprisesThis article describes the development of a web-based dynamic job-shop scheduling system for small and medium enterprises. In large enterprises, scheduling is mainly performed with appropriate technology by human experts; many small and medium enterprises lack the resources to implement such a task. The main objective was to develop a cost-effective, efficient solution for job-shop scheduling in small and medium enterprises with an emphasis on accessibility, platform independence and ease of use. For these reasons, we decided to develop a web-based solution with the main emphasis on the development of an intelligent and dynamic user interface. The solution is built upon modular programming principles and enables dynamic scheduling on the basis of artificial intelligence, i.e. genetic algorithms. The solution has been developed as a standalone information system, which allows the management of virtually all scheduling activities through an administration panel. In addition, the solution covers the five main functionalities that completely support the scheduling process, i.e. making an inventory of resources available in the company, using it in the process of production planning, collecting data on production activities, distribution of up-to-date information and insight over events in the system.

A New GT Heuristic for Solving Multi Objective Job Shop Scheduling Problems

2014 ◽  
Vol 591 ◽  
pp. 184-188
Author(s):  
D. Lakshmipathy ◽  
M. Chandrasekaran ◽  
T. Balamurugan ◽  
P. Sriramya
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Heuristic Method ◽  
Flow Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Practical Applications ◽  
Shop Scheduling ◽  
Mean Flow Time

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems in manufacturing system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. New Game theory based heuristic method (GT) is used for finding optimal makespan, mean flow time, mean tardiness values of different size problems. The results show that the GT Heuristic is an efficient and effective method that gives better results than Genetic Algorithm (GA). The proposed GT Heuristic is a good problem-solving technique for job shop scheduling problem with multi criteria.

Minimizing Makespan in Job Shop Scheduling Problem Using Genetic Algorithm

2015 ◽  
Vol 813-814 ◽  
pp. 1183-1187 ◽  
Author(s):  
Aathi Muthiah ◽  
R. Rajkumar ◽  
B. Muthukumar
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Setup Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Knowing That ◽  
Batch Sizes

- Scheduling is an important tool for manufacturing and engineering, where it can have a major impact on the productivity of a process. In manufacturing, the purpose of scheduling is to minimize the production time and costs. Production scheduling aims to maximize the efficiency of the operation and reduce costs. We keep all of our machines well-maintained to prevent any problems, but there is on way to completely prevent down-time. With redundant machines we have the security of knowing that we are not going to be in trouble meeting our deadlines if a machine has any unexpected down-times. Finally we can work to get our batch sizes as small as is reasonably possible while also reducing the setup time of each batch. This allows us to eliminate a sizable portion of each part waiting while the rest of the parts in the batch are being machined.

Multi-Objective Optimization Algorithm for Job Shop Scheduling Problem in Discrete Manufacturing Enterprise

2015 ◽  
Vol 741 ◽  
pp. 860-864
Author(s):  
Li Lan Liu ◽  
Xue Wei Liu ◽  
Sen Wang ◽  
Wei Zhou ◽  
Gai Ping Zhao
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Manufacturing Enterprise ◽  
Shop Scheduling ◽  
Discrete Manufacturing ◽  
Scheduling Plan

Job Shop scheduling should satisfy the constraints of time, order and resource. To solve this NP-Hard problem, multi-optimization for job shop scheduling problem (JSSP) in discrete manufacturing plant is researched. Objective of JSSP in discrete manufacturing enterprise was analyzed, and production scheduling optimization model was constructed with the optimization goal of minimizing the bottleneck machines’ make-span and the total products’ tardiness; Then, Particle Swarm Optimization (PSO) algorithm was used to solve this model by the process-based encoding mode; To solve the premature convergence problem of PSO, advantages of Simulated Annealing (SA) algorithm, such as better global optimization performance, was integrated into PSO algorithm and a Hybrid PSO-SA Algorithm (HPSA) was proposed and the flowchart was presented; Then, this hybrid algorithm was applied in actual production scheduling of a discrete manufacturing enterprise. Finally, comparative analysis of HPSA/SA/PSO optimal methods and actual scheduling plan was carried out, which verify the result that the HPSA is effective and superiority.

scholarly journals Integrating Preventive Maintenance Planning and Production Scheduling under Reentrant Job Shop

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ruiqiu Li ◽  
Huimin Ma
Keyword(s):  
Preventive Maintenance ◽  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Particle Swarm Optimization ◽  
Highly Nonlinear ◽  
Maximum Completion Time ◽  
Independent Decision ◽  
Maintenance Plan ◽  
Semiconductor Production

This paper focuses on a preventive maintenance plan and production scheduling problem under reentrant Job Shop in semiconductor production. Previous researches discussed production scheduling and preventive maintenance plan independently, especially on reentrant Job Shop. Due to reentrancy, reentrant Job Shop scheduling is more complex than the standard Job Shop which belongs to NP-hard problems. Reentrancy is a typical characteristic of semiconductor production. What is more, the equipment of semiconductor production is very expensive. Equipment failure will affect the normal production plan. It is necessary to maintain it regularly. So, we establish an integrated and optimal mathematical model. In this paper, we use the hybrid particle swarm optimization algorithm to solve the problem for it is highly nonlinear and discrete. The proposed model is evaluated through some simple simulation experiments and the results show that the model works better than the independent decision-making model in terms of minimizing maximum completion time.

Learning the Distribution Characteristics of Critical Machines in Production Scheduling Problems

2011 ◽  
Vol 337 ◽  
pp. 142-145
Author(s):  
Rui Zhang
Keyword(s):  
Production Scheduling ◽  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Identification Algorithm ◽  
Scheduling Problem ◽  
Distribution Characteristics ◽  
Scheduling Problems ◽  
Critical Machine ◽  
Critical Machines

A critical machine identification algorithm is proposed for the job shop scheduling problem in which the total tardiness must be minimized. An optimization-based procedure is devised to learn the distribution characteristics of critical machines in a specific scheduling instance. The proposed simulated annealing algorithm optimizes the scheduling problem after the capacity constraints for each machine are modified. A genetic algorithm based on combined dispatching rules is designed to verify the effectiveness of the proposed methodology.

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