A multiobjective genetic algorithm for scheduling a flexible manufacturing system

Sankar, S. Saravana; Ponnanbalam, S. G.; Rajendran, Chandrasekharan

doi:10.1007/s00170-002-1464-2

Cited by 42 publications

(10 citation statements)

References 22 publications

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“…x v x (10) y v y (11) where v x and v y are the velocity of the AS/R vehicle along x and y (as defined in Fig. 1); (x, y) i is the location of slot i. Batch xy rearrangement is based on the following Chebyshev distance:…”

Section: Results and Factorial Analysismentioning

confidence: 99%

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Design and control of an AS/RS

Manzini

Gamberi

Regattieri

2005

Int J Adv Manuf Technol

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Automated storage/retrieval systems (AS/RSs) are a combination of equipment and controls which automatically handle, store and retrieve materials (components, tools, raw material and subassemblies) with great speed and accuracy. Consequently, they are widely used in industrial companies to manage products with cost-effective utilization of time, space and equipment. This paper presents a multi-parametric dynamic model of a product-to-picker storage system with class based storage allocation of products. Thousands of what-if scenarios are simulated in order to measure the impact of alternative design and operating configurations on the expected system performance and to identify the most critical factors and combinations of factors affecting the response of the system. Class based storage proves to be a very effective way of both reducing the picking cycle time and maximizing the throughput of the system. The rapid effectiveness of visual interactive simulation (VIS) in supporting the design (redesign) and control of new (existing) warehouses emerges, responding to the need for flexibility which modern companies need in order to adapt to strongly changing operating conditions quickly

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Section: Results and Factorial Analysismentioning

confidence: 99%

“…They present algorithms and procedures to support the optimization (e.g., storage allocation of materials, storage and retrieval sequencing) of the whole production system [7][8][9][10].…”

Section: Literature Overviewmentioning

confidence: 99%

Design and control of an AS/RS

Manzini

Gamberi

Regattieri

2005

Int J Adv Manuf Technol

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“…This issue begets higher complexity for the FMS scheduling and makes its complexity rise to (n!) m and belongs to the class of problems as NP-hard (Liu and MacCarthy 1997;Sankar, Ponnanbalam, and Rajendran 2003;Taghavifard, Heydar, and Mousavi 2009). It requires an exponential time to be solved in optimality.…”

Section: Complexity Of the Fms Scheduling Problemmentioning

confidence: 99%

Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem

Candan

Yazg̈an

2014

International Journal of Production Research

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The flexible manufacturing system (FMS) scheduling problem is one of the most difficult NP-hard combinatorial optimisation problems. The exact solution of an FMS scheduling problem cannot be found within a reasonable amount of time, even for small size problems. Therefore, a metaheuristic algorithm is required to solve such a problem. The objective of this study is to develop a genetic algorithm (GA) approach to minimise makespan of the scheduling problem. A Taguchi orthogonal array is proposed instead of a full factorial experimental design for determining the parameters of the GA. The effects of the GA parameters on the minimum makespan values are determined and an analysis of variance is performed to investigate significance factors on the results.Keywords: flexible manufacturing system scheduling; genetic algorithm; Taguchi orthogonal arrays method 1. Introduction A flexible manufacturing system (FMS) is a highly automated machine cell consisting of a group of processing workstations (usually CNC machine tools), interconnected by an automated material handling and storage system and controlled by a distributed computer system (Groover 2001). Another definition of the FMS is that it is a reprogrammable manufacturing system capable of producing a variety of products automatically (Chryssolouris 2006). In the global competitive environment, developing technology in order to meet customer demands and expectations has brought flexibility in production. The FMS has been studied over the last 25 years. In the last decade, they became important elements in the success of enterprises (Chan and Chan 2004). The FMS is a complex discrete event dynamic system and complete utilisation of the available resources in a system is extremely important to optimise its productivity. The most important objective of FMS scheduling is to increase the utilisation of resources and to reduce the idle time. The resource utilisation is improved by scheduling the set of tasks so as to reduce the makespan (C max ) (Jyothi 2012).One way of achieving high productivity in an FMS is to solve scheduling problems optimal or near optimal. The scheduling problem can be considered more complex in an FMS than in a traditional manufacturing system (transfer line, job shop, flow shop, etc.). In general, it is very difficult to determine optimal solutions for FMS scheduling problems (Lee and Kim 1999). Various methods are used to find appropriate job schedules.In this study, a metaheuristic genetic algorithm (GA) technique commonly used as a stochastic search method in recent years was employed and an appropriate job schedule was obtained. The effects of the GA's factors affecting the scheduling were determined using Taguchi experimental design technique. The objective of the problem was to minimise the makespan (C max ) of an FMS scheduling problem. Fourty two different FMS problem data-sets were generated to find out parameters of the proposed GA. A bench-mark problem was also solved to illustrate effectiveness of the proposed approach.Although there ...

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“…In comparison, using a meta-heuristic optimizer, such as a genetic algorithm (GA), to generate the optimal schedules directly, which is referred as a direct approach in this paper, may be advantageous if searching for ''optimal'' solutions is desired. There are many studies that compare these two approaches and some of them provide results showing the use of GAs to generate detailed schedules that can obtain better solutions [8,9] than those ones obtained using PDRs. Methods that combine GA and PDR, meaning that the GA search for the optimal combination of PDRs, can be found in [10,11] and will be referred to as the indirect approach in this paper.…”

Section: Introductionmentioning

confidence: 97%