Solving machine-loading problem of a flexible manufacturing system with constraint-based genetic algorithm

Kumar, Akhilesh; Prakash, .; Tiwari, Manoj Kumar; Shankar, Ravi; Baveja, Alok

doi:10.1016/j.ejor.2005.06.025

Cited by 74 publications

(29 citation statements)

References 65 publications

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“…Currently, there are no wellestablished optimum values for these parameters and they are problem specific [7]. In order to get statistically robust results, each of the test cases was solved using the proposed GA for 10 runs (see Table 3).…”

Section: Figurementioning

confidence: 99%

A Genetic Algorithm for Solving a Container Storage Problem Using a Residence Time Strategy

Serban¹,

Carp²

2017

SIC

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At each port of destination, some containers are unloaded from a vessel and stored in the terminal to be delivered to their customers. One of the strategies used to arrange the containers in a terminal is residence time strategy: based on their delivery deadlines, each incoming container being assigned to a priority class. The aim of this study is to determine a valid arrangement of incoming containers in a block (part) of the terminal, in the shortest amount of time, with higher priority containers located above lower priority ones. In this way, some of the main objectives of a container terminal may be achieved: avoiding further reshuffles (number of relocations) and reducing the vessel berthing time. We developed a genetic algorithm and its performance is evaluated against a random stacking strategy used as benchmark for the experiments, and through several sets of tests on control parameters. All the tests showed that, if a reliable estimation of the delivery time can be assigned to every incoming container, the proposed method may be a useful tool for container terminal operators.

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Section: Figurementioning

confidence: 99%

A Genetic Algorithm for Solving a Container Storage Problem Using a Residence Time Strategy

Serban¹,

Carp²

2017

SIC

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“…Computing all these solutions to determine the optimum one is computationally intractable for medium-to large-sized problems. In addition, it is the fact that MLP of a FMS is recognized for its complexity [66]. Moreover, the MLP related to automated manufacturing system belongs to the classi cation of NP-hard problems where the computational solution times are non-polynomial in the size of the problem [67][68][69][70][71][72].…”

Section: The Hybrid Solution Algorithmsmentioning

confidence: 99%

A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy

2017

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Abstract. A exible Cell Scheduling Problem (CSP) under Time-Of-Use (TOU)electricity tari s is developed in this study. To apply a kind of energy-conscious policy, overconsumption cost of on-peak period electricity, limitations on total energy consumption by all facilities, setup time available on each cell, part defect (pert) percentage, and the total number of Automated Guided Vehicles (AGV) were considered. Additionally, an Ant Colony Optimization (ACO) algorithm was employed to nd a near-optimum solution to the proposed Mixed Integer Linear Programming (MILP) model with the objective of minimizing the total cost of CSP model. Since no benchmark is available in the literature, a lower bound was implemented as well to validate the result achieved. Moreover, to improve the quality of the results obtained by meta-heuristic algorithms, two hybrid algorithms (HGA and HACO) were proposed to solve the model. For parameter tuning of algorithms, Taguchi experimental design method was applied. Then, numerical examples were presented to prove the application of the proposed methodology. Our results were compared with the lower bound, con rming consequently that HACO is capable of nding better and near-optimum solutions.

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“…There is a need to develop efficient heuristic algorithms for more real life solution (Stecke, 1983a). Requirement of further extension of research was outlined by all researchers (Chan et al, 2004;Nagarjuna et al, 2006;Kumar et al, 2006). A real life solution to machine loading problems of FMS with a new solution methodology is still awaited (Yusof et al, 2012;Biswas & Mahapatra, 2008;Ponnambalam & Kiat, 2008;Mandal et al, 2010;Yusof, Budiarto, & Venkat, 2011;Abazari et al, 2012;Petrovic & Akoz, 2008).…”

Section: Major Findings From the Literature Reviewmentioning

confidence: 99%

Meta-hierarchical-heuristic-mathematical- model of loading problems in flexible manufacturing system for development of an intelligent approach

Singh¹,

Khan²

2016

10.5267/j.ijiec

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Flexible manufacturing system (FMS) promises a wide range of manufacturing benefits in terms of flexibility and productivity. These benefits are targeted by efficient production planning. Part type selection, machine grouping, deciding production ratio, resource allocation and machine loading are five identified production planning problems. Machine loading is the most identified complex problem solved with aid of computers. System up gradation and newer technology adoption are the primary needs of efficient FMS generating new scopes of research in the field. The literature review is carried and the critical analysis is being executed in the present work. This paper presents the outcomes of the mathematical modelling techniques for loading of machines in FMS's. It was also analysed that the mathematical modelling is necessary for accurate and reliable analysis for practical applications. However, excessive computations need to be avoided and heuristics have to be used for real-world problems. This paper presents the heuristics-mathematical modelling of loading problem with machine processing time as primary input. The aim of the present work is to solve a real-world machine loading problem with an objective of balancing the workload of the FMS with decreased computational time. A Matlab code is developed for the solution and the results are found most accurate and reliable as presented in the paper.

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Solving machine-loading problem of a flexible manufacturing system with constraint-based genetic algorithm

Cited by 74 publications

References 65 publications

A Genetic Algorithm for Solving a Container Storage Problem Using a Residence Time Strategy

A Genetic Algorithm for Solving a Container Storage Problem Using a Residence Time Strategy

A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy

Meta-hierarchical-heuristic-mathematical- model of loading problems in flexible manufacturing system for development of an intelligent approach

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