A genetic algorithm with shuffle for job shop scheduling problems

• Job shop scheduling problems are computationally complex combinatorial optimization problems. Genetic algorithms have been used in various forms and in combination with other algorithms to solve job shop scheduling problems. A partially flexible job shop with precedence constraints increases this complex behaviour. There are two main parts to optimizing ajob shop, the routing and the scheduling. The objective here is to get consistent optimal makespan using a genetic algorithm. This paper firstly, presents a simulation approach for the considered partially flexible job shop scheduling problem. Which take into account the precedence constraints and reduce situations of deadlock. To solve the partially flexible job shop scheduling problem a genetic algorithm was used and improved. It utilise a genetic crossovers for routing and a new random shuffle feature is introduced for the scheduling. The computational results have shown that the algorithm performs well in terms of finding a consistent optimal schedule for the given problem

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

Nyckelord

Simulation based optimisation

genetic algorithm

job shop scheduling

random shuffle

Manufacturing and materials engineering

Produktions- och materialteknik

Production Technology

Produktionsteknik

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)