A Biased Random Key Genetic Algorithm for the Flexible Job Shop Problem with Transportation

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

S. Mahdi Homayouni (Author) Other; Dalila M. M. Fontes (Author) FEP; Fernando A. C. C. Fontes (Author) FEUP

Conference proceedings International

Title: XIII Metaheuristics International Conference MIC

Pages: 149-151

Other information

Authenticus ID: P-011-4WS

Abstract (EN): This work addresses the Flexible Job Shop Scheduling Problem with Transportation resources (FJSPT), which can be seen as an extension of both the Flexible Job Shop Scheduling Problem (FJS) and the Job Shop Scheduling Problem with Transportation resources (JSPT). Regarding the former case, the FJSPT additionally considers that the jobs need to be transported to the machines they are processed in; while regarding the latter, in the FJSP the specific machine processing each operation also needs to decided. The FJSPT is NP-hard since, it extends NP-hard problems. In here, we propose an operation based biased random key genetic algorithm to efficiently find good quality solutions.

Language: English

Type (Professor's evaluation): Scientific

Notes: https://www.researchgate.net/publication/335004588_A_Biased_Random_Key_Genetic_Algorithm_for_the_Flexible_Job_Shop_Problem_with_Transportation/link/5d49fdbd4585153e59413ca2/download

No. of pages: 3