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.

A New Solution Seed for Job Shop Scheduling Problem

2011 ◽  
Vol 110-116 ◽  
pp. 3899-3905
Author(s):  
Parviz Fattahi ◽  
Mojdeh Shirazi Manesh ◽  
Abdolreza Roshani
Keyword(s):  
Tabu Search ◽  
Large Scale ◽  
Production Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Np Hard ◽  
Shop Scheduling ◽  
Large Scale Problems

Scheduling for job shop is very important in both fields of production management and combinatorial optimization. Since the problem is well known as NP-Hard class, many metaheuristic approaches are developed to solve the medium and large scale problems. One of the main elements of these metaheuristics is the solution seed structure. Solution seed represent the coding structure of real solution. In this paper, a new solution seed for job shop scheduling is presented. This solution seed is compared with a famous solution seed presented for the job shop scheduling. Since the problem is well known as NP-Hard class, a Tabu search algorithm is developed to solve large scale problems. The proposed solution seed are examined using an example and tabu search algorithm.

Job-Shop Scheduling Approach to Order-Picking Problem

2012 ◽  
Author(s):  
Yukiyasu Iwasaki ◽  
Ikuo Suzuki ◽  
Masahito Yamamoto ◽  
Masashi Furukawa
Keyword(s):  
Numerical Experiments ◽  
Large Scale ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Order Picking ◽  
Scheduling Problem ◽  
Mail Order ◽  
Job Shop Scheduling Problem ◽  
Rule Based ◽  
Shop Scheduling

In recent years, a large-scale logistic center plays an important role in mail-order business with Internet. In the logistic center, the efficient managing is required to deliver products to customers as soon as possible. Researches to efficiently control the logistic center have been done in the various approaches. This study proposed a new method for the order-picking problem considering worker’s jamming at the same shelf in the logistic center. In the proposed method, we formulate worker’s scheduling in the logistic center as Job-shop Scheduling Problem and optimize this problem. Numerical experiments show the proposed method improve worker’s scheduling compared with rule-based scheduling.

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.

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