A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles

Scheduling of multi load AGVs in FMS by modified memetic particle swarm optimization algorithm

“…It was found that integrated scheduling of jobs with AGVs scheduling, dispatching and routing in FMS yield optimum results. A multi-load AGV can pick and drop additional loads simultaneously while processing previous load transfer request and considerably reduce trip time and total travel distance of empty AGVs .The performance of multi load AGVs was compared with unit load AGVs by Ulusoy et al (1997). In simulation test it was observed that application of multi-load AGVs can increase throughput of the material handling system, considerably.…”

“…Unidirectional AGVs can cruise forward on a guide path in single direction only while bidirectional AGVs are capable to steer in both directions on the same guide path (Egbelu & Tanchoco, 1986;Gaskins et al, 1989;Gaskins & Tanchoco, 1987). Unit load AGV can transport single load from one work center to another while multi load AGV system is capable of picking up and deliver more than one load at the same time to any work center which increase through material handling system and FMS facility considerably (Ulusoy et al, 1997;Van Der Meer, 2000). Multi load AGVs have significant potential to increase overall FMS through put and the flexibility.…”

A review of design and control of automated guided vehicle systems

“…The heuristic solves two scheduling problems (machines and AGVs) iteratively until a sufficiently good result is obtained. Ulusoy et al (1997) improve the solution quality by using a genetic algorithm to solve the integrated scheduling problem.…”

Optimisation of process planning functions by genetic algorithms

“…This results in the restriction of the search space in some way, leaving some parts totally untouched. Although GAs are heuristic procedures themselves, they test a wealth of samplings from dierent regions of the search space for ®tness simultaneously, and sort out and exploit regions of interest very quickly [12]. It has been proved that the TSP problem which can be referred to either a combinatorial optimisation problem or a NP-complete problem cannot be solved by deterministic algorithms within an acceptable time, since it has numerous local minima.…”