Intelligent Scheduling Method Oriented to Multi-Varieties and Small-Batch Production Mode

2012 ◽  
Vol 263-266 ◽  
pp. 1269-1274
Author(s):  
Dan Chen Zhou ◽  
Liang Zeng
Keyword(s):  
Load Balance ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Additional Constraint ◽  
Batch Production ◽  
Production Mode ◽  
Scheduling Model ◽  
Proposed Model ◽  
Scheduling Method ◽  
Annealing Algorithm

In terms of characteristics of scheduling problem in multi-varieties and small-batch production mode, three additional constraint conditions including combination constraint, priority scheduling and machine load balance are supplemented based on classical Job-shop scheduling model in order to reflect the actual scheduling activities better. An improved genetic annealing algorithm is proposed aiming at the above model, and the requirement of supplemented constraint conditions is satisfied by means of improving the decoding process of chromosomes. The key technologies of algorithm are discussed in detail. The feasibility and validity of the proposed model and algorithm is demonstrated through simulated computation.

An E2GPGP-GASA-Based Multi-Agent Job Shop Scheduling System

2012 ◽  
Vol 505 ◽  
pp. 65-74
Author(s):  
Lin Lin Lu ◽  
Xin Ma ◽  
Ya Xuan Wang
Keyword(s):  
Large Scale ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Simulation Software ◽  
Prototype System ◽  
Shop Scheduling ◽  
Model Combining ◽  
Proposed Model ◽  
Multi Agent ◽  
Global Planning

In this paper, a job shop scheduling model combining MAS (Multi-Agent System) with GASA (Simulated Annealing-Genetic Algorithm) is presented. The proposed model is based on the E2GPGP (extended extended generalized partial global planning) mechanism and utilizes the advantages of static intelligence algorithms with dynamic MAS. A scheduling process from ‘initialized macro-scheduling’ to ‘repeated micro-scheduling’ is designed for large-scale complex problems to enable to implement an effective and widely applicable prototype system for the job shop scheduling problem (JSSP). Under a set of theoretic strategies in the GPGP which is summarized in detail, E2GPGP is also proposed further. The GPGP-cooperation-mechanism is simulated by using simulation software DECAF for the JSSP. The results show that the proposed model based on the E2GPGP-GASA not only improves the effectiveness, but also reduces the resource cost.

Flexible Job-Shop Scheduling for Reduced Manufacturing Carbon Footprint

2017 ◽  
Author(s):  
Qiong Liu ◽  
Youquan Tian ◽  
Chao Wang ◽  
Freddy O. Chekem ◽  
John W. Sutherland
Keyword(s):  
Carbon Footprint ◽  
Manufacturing System ◽  
Mixed Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop Scheduling ◽  
Carbon Footprints ◽  
Shop Scheduling ◽  
Product Carbon Footprint ◽  
Proposed Model

In order to help manufacturing companies quantify and reduce product carbon footprints in a mixed model manufacturing system, a product carbon footprint oriented multi-objective flexible job-shop scheduling optimization model is proposed. The production portion of the product carbon footprint, based on the mapping relations between products and the carbon emissions within the manufacturing system, is proposed to calculate the product carbon footprint in the mixed model manufacturing system. Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to solve the proposed model. In order to help decision makers to choose the most suitable solution from the Pareto set as its execution solution, a method based on grades of product carbon footprints is proposed. Finally, the efficacy of the proposed model and algorithm are examined via a case study.

Sign in / Sign up

Export Citation Format

Share Document