Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms

Abstract

The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed. A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem. A domain independent general purpose GA was used, which was an add-in to the spreadsheet software. An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems. Computational results are presented for a benchmark with 82 test problems, which have been constructed by other researchers. The achieved results are comparable to the previous approaches. The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.

A Trajectory Based Methods for Parallel Scheduling of Machines and AGV’S in an FMS Environment

“…In the literature reported that, the subject of design of planning for simultaneous scheduling of machines and automated guided vehicles (AGVs) using non optimization technique system has generally been set out either as a comparison of various vehicle dispatching rules in relation to a prespecified schedule and on a particular layout [7,8] or in relation with the design jobset [9,10].…”

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

“…A number of approaches describe the problem with mixed integer linear programming (Anwar and Nagi 1998;Bilge and Ulusoy 1995;Caumond et al 2009;Khayat, Langevin, and Riopel 2006;Lacomme, Moukrim, and Tchernev 2005;Nishi, Hiranaka, and Grossmann 2011;Raman, Talbot, and Rachamadgu 1986;Suri and Desiraju 1997;Zheng, Xiao, and Seo 2014), while a few works have considered PN (Lee and DiCesare 1994a;Raju and Chetty 1993;Sun, Cheng, and Fu 1994), and disjunctive graph modelling (Lacomme, Larabi, and Tchernev 2013). The other works whose methods are based on metaheuristics like genetic (Abdelmaguid et al 2004;Chaudhry, Mahmood, and Shami 2011;Jerald et al 2006;Reddy and Rao 2006;Ulusoy, Sivrikaya-Serifoglu, and Bilge 1997), differential evolution (Kumar, Janardhana, and Rao 2011), and simulated annealing (Deroussi, Gourgand, and Tchernev 2008), use a solution vector with fixed-length strings to represent a schedule called chromosome.…”

“…They improve the solutions produced by the sliding time window heuristic in Bilge and Ulusoy (1995). The other metaheuristic algorithms described in Abdelmaguid et al (2004), Reddy and Rao (2006), Deroussi, Gourgand, and Tchernev (2008), Kumar, Janardhana, andRao (2011), Chaudhry, Mahmood, andShami (2011) follow a similar approach. However, they differ in the solution representation and evaluation, and the vehicle assignment heuristic algorithms used for AGV scheduling.…”

“…Bilge and Ulusoy (1995) propose the most relevant benchmark instances on the scheduling problem which have been used by several publications (Abdelmaguid et al 2004;Chaudhry, Mahmood, and Shami 2011;Deroussi, Gourgand, and Tchernev 2008;Erol et al 2012;Khayat, Langevin, and Riopel 2006;Kumar, Janardhana, and Rao 2011;Reddy and Rao 2006;Ulusoy, Sivrikaya-Serifoglu, and Bilge 1997). The problem consists of 82 test instances with 4 different path layouts and 2 AGVs.…”

Role of batch size in scheduling optimization of flexible manufacturing system using genetic algorithm

“…The objective was to maximize AGVs utilization which has also been validated on several problems. Machines and AGVs scheduling in flexible manufacturing system was also addressed by Chaudhry et al (2011) using genetic algorithm spreadsheets to minimize total completion time. Zhang et al (2012) proposed a model for flexible job shop scheduling considering transportation constraints and bounded processing time to minimize makespan and storage.…”