The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 89-190
Full Name: Jorge Magalhaes-Mendes
Position: Assistant Professor
Age: ON
Sex: Male
Address: Department of Civil Engineering ISEP, Rua Dr. António Bernardino de Almeida, 431 – 4200-072 Porto
Country: PORTUGAL
Tel: 228340500
Tel prefix: 00351
Fax: 228321159
E-mail address: jjm@isep.ipp.pt
Other E-mails: jorgemagalhaes7@gmail.com
Title of the Paper: Complex Scheduling Problems Using An Optimization Methodology
Authors as they appear in the Paper: Jorge Magalhaes-Mendes
Email addresses of all the authors: jjm@isep.ipp.pt
Number of paper pages: 10
Abstract: Scheduling operations problems arise in diverse areas such as flexible manufacturing, production planning and scheduling, logistics, supply chain problem, etc. A common feature of many of these problems is that no efficient solution algorithms are known that solve each instance to optimality in a time bounded polynomially in the size of the problem, Dorndorf and Pesch [22]. Discrete optimization can help to overcome these difficulties. This paper presents an optimization approach to solve the complex scheduling problem in a job shop environment. This problem is also known as the Job Shop Scheduling Problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. Genetic algorithms are an optimization methodology based on a direct analogy to Darwinian natural selection and mutations in biological!
reproduction. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach.
Keywords: Scheduling, manufacturing, heuristics, genetic algorithm, optimization, local search, JSSP
EXTENSION of the file: .pdf
Special (Invited) Session: An Optimization Approach for the Job Shop Scheduling Problem
Organizer of the Session: 697-218
How Did you learn about congress: Prof. Pina Marques (pmarques@fe.up.pt);
IP ADDRESS: 85.138.56.214
