Solving the machine-loading problem in a flexible manufacturing system using a combinatorial auction-based approach

Srinivas, Vivek; Tiwari, Manoj Kumar; Allada, Venkat

doi:10.1080/00207540310001649530

Cited by 42 publications

(15 citation statements)

References 29 publications

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“…A genetic-algorithmbased-methodology is developed to solve the problem. Srinivas et al [21] addressed the FMS machine-loading problem where job selection and operation allocation on machines were to be performed such that there was a minimization of system unbalance and a maximization of throughput. The methodology of winner determination using the combinatorial auction process was employed to solve the FMS machine-loading problem.…”

Section: Heuristic-based Approachesmentioning

confidence: 99%

A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system

Nagarjuna¹,

Mahesh²,

Rajagopal

2006

Robotics and Computer-Integrated Manufacturing

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Section: Heuristic-based Approachesmentioning

confidence: 99%

A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system

Nagarjuna¹,

Mahesh²,

Rajagopal

2006

Robotics and Computer-Integrated Manufacturing

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“…minimize the idle time of the machines, maximize the machine utilization) by defining jobs allocation of each part type to be produced to a given number of machines also satisfying technological constraints. Therefore, due to the different and simultaneous factors to be set, the problem of machine loading belongs to the NP-hard problems (Srinivas et al, 2004). …”

Section: Introductionmentioning

confidence: 99%

Solving machine loading problem of flexible manufacturing systems using a modified discrete firefly algorithm

Bottani¹,

Centobelli²,

Cerchione³

et al. 2017

10.5267/j.ijiec

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This paper proposes a modified discrete firefly algorithm (DFA) applied to the machine loading problem of the flexible manufacturing systems (FMSs) starting from the mathematical formulation adopted by Swarnkar & Tiwari (2004). The aim of the problem is to identify the optimal jobs sequence that simultaneously maximizes the throughput and minimizes the system unbalance according to given technological constraints (e.g. available tool slots and machining time). The results of the algorithm proposed have been compared with the existing and most recent swarm-based approaches available in the open literature using as benchmark the set of ten problems proposed by Mukhopadhyay et al. (1992). The algorithm shows results that are comparable and sometimes even better than most of the other approaches considering both the quality of the results provided and the computational times obtained.

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“…Heuristic approaches can deliver very fast solutions but tend to be myopic in nature [15]. Some dispatching rules such as the shortest processing time and earliest due date, are popular and provide good results [9,16]. However, these rules independently cannot consider real-time information and are unable to consider parallel and alternative process plans.…”

Section: Introductionmentioning

confidence: 99%

A data mining integrated architecture for shop floor control

Srinivas

Harding

2008

Proceedings of the Institution of Mechanical Engineers, Part B:

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The dynamics of the market are forcing shop floor control systems to enhance their intelligence and reactivity to change. The control system must also have mechanisms to process the vast amount of data, which is generated with the application of cheap data logging systems and should also incorporate a mechanism for continuous learning. Data mining provides a mechanism for analysing data and generating new knowledge. This paper presents an intelligent knowledge-based decision support system for shop floor control, which incorporates data mining techniques and intelligent agent technology to provide useful information, knowledge, and a mechanism for continuous learning. The functioning of the system is described and the architecture of different agents that constitute the system has been discussed in terms of the different modules required. A process to integrate the individual data mining agents associated with the different processes has also been described. Finally a prototype of the system is presented to demonstrate the application of this agent-based system.

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Solving the machine-loading problem in a flexible manufacturing system using a combinatorial auction-based approach

Cited by 42 publications

References 29 publications

A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system

A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system

Solving machine loading problem of flexible manufacturing systems using a modified discrete firefly algorithm

A data mining integrated architecture for shop floor control

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