The effect of system configuration and ramp-up time on manufacturing system acquisition under uncertain demand

Niroomand, Iman; Kuzgunkaya, Onur; Bulgak, Akif Asil

doi:10.1016/j.cie.2014.04.017

Cited by 18 publications

(31 citation statements)

References 20 publications

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“…Ayman, Youssef and ElMaraghy (2006) studied the cost and time required to reconfigure the system and their observations suggest that these aspects depend on scalability and convertibility. Niroomand, Kuzgunkaya and Bulgak (2014) evidenced the impact of scalability on the reduction of reconfiguration time by discussing about the impact of the selection of a configuration of a manufacturing system (in order to ensure a certain production capacity) on the reconfiguration time.…”

Section: Why Are Reconfigurability Characteristics Relevant?mentioning

confidence: 99%

“… a second set of authors (Azab and ElMaraghy, 2007;Hees and Reinhart, 2015;Abdi and Labib, 2003;Deif and ElMaraghy, 2007a;Chaube, Benyoucef and Tiwari, 2012;Niroomand, Kuzgunkaya and Bulgak, 2014;Koren, Wang and Gu, 2016) allowed evidencing relationships between characteristics at system and factory levels;  Nejad, Niroomand and Kuzgunkaya (2014) and Niroomand, Kuzgunkaya and Bulgak (2012) allowed evidencing relationships between all three levels, i.e.…”

Section: Where Can the Six Core Characteristics Be Located?mentioning

confidence: 99%

“…RMSs have been described as machining systems provided with modular structure, both hardware and software. Their reconfiguration allows to add, remove, or modify specific process capabilities, controls, software, or machine structure to adjust production capacity in response to changing market demand or technologies (Mehrabi, Ulsoy and Koren, 2000b;Niroomand, Kuzgunkaya and Bulgak, 2014). Despite the generalizability of RMSs' definition, many authors, when referring to RMSs, limited their focus on specific systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A framework to manage reconfigurability in manufacturing

Napoleone

Pozzetti

Macchi

2018

International Journal of Production Research

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Nowadays, manufacturing firms are dealing with the unpredictability of market requirements and the frequent changes induced by technological innovation. For this reason, firms are more and more addressing the need to be responsive at an affordable cost. To do so, they are required to develop a capability called reconfigurability. This paper is a review of the existing literature because the current need makes interesting to reflect on the state of the art of reconfigurability as a concept. This reflection has led to focus on reconfigurability characteristics for both their relevance and their relationships with managerial decisions in manufacturing. To this end, a framework has been proposed. It is based on system lifecycle and production levels. These two elements have been deduced from literature and identified as relevant dimensions for decision making.

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Section: Why Are Reconfigurability Characteristics Relevant?mentioning

confidence: 99%

Section: Where Can the Six Core Characteristics Be Located?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A framework to manage reconfigurability in manufacturing

Napoleone

Pozzetti

Macchi

2018

International Journal of Production Research

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“…The problems of long ramp-up times were extensively researched, over the years [2]. A number of researchers analyzed the characteristics that influence the ramp-up and suggested several solutions to address different problems involved in this process [12], [13]. Ramp-up includes all the processes, right from the top strategic management decisions down to the lower level technical procedures [1].…”

Section: Knowledge and Learning During Ramp-upmentioning

confidence: 99%

A Symbiotic Human–Machine Learning Approach for Production Ramp-up

Doltsinis

Ferreira

Lohse

2018

IEEE Trans. Human-Mach. Syst.

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Constantly shorter product lifecycles and the high number of product variants necessitate frequent production system reconfigurations and changeovers. Shortening ramp-up and changeover times is essential to achieve the agility required to respond to these challenges. This work investigates a symbiotic human-machine environment, which combines a formal framework for capturing structured ramp-up experiences from expert production engineers with a reinforcement learning method to formulate effective ramp-up policies. Such learned policies have been shown to reduce unnecessary iterations in human decision-making processes by suggesting the most appropriate actions for different ramp-up states. One of the key challenges for machine learningbased methods, particularly for episodic problems with complex state-spaces, such as ramp-up, is the exploration strategy that can maximize the information gain while minimizing the number of exploration steps required to find good policies. This paper proposes an exploration strategy for reinforcement learning, guided by a human expert. The proposed approach combines human intelligence with machine's capability for processing data quickly, accurately, and reliably. The efficiency of the proposed human exploration guided machine learning strategy is assessed by comparing it with three machine-based exploration strategies. To test and compare the four strategies, a ramp-up emulator was built, based on system experimentation and user experience. The results of the experiments show that human-guided exploration can achieve close to optimal behavior, with far less data than what is needed for traditional machine-based strategies.

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“…The objective function aims at minimising makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs and product demand. Niroomand, Kuzgunkaya, and Bulgak (2014) proposed a methodology based on mixed integer programming to incorporate the configuration characteristics into this decision-making process. They proposed a mixed integer programming model by incorporating various ramp-up time patterns, which define system scalability lead time.…”

Section: Literature Overviewmentioning

confidence: 99%

A Decision Investment Model to Design Manufacturing Systems based on a genetic algorithm and Monte-Carlo simulation

Renna

2016

International Journal of Computer Integrated Manufacturing

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The flexibility of manufacturing systems is one of the key factors to stay competitive when the companies face increasingly frequent market changes due to the rapid introduction of new products and constantly varying product demand. Flexible manufacturing systems are characterised by high investment costs, and often offer more flexibility that what is really needed. The emergent paradigm of Focused Flexible Manufacturing System concerns hybrid systems composed both of general purpose and dedicated resources. The research proposed in this paper concerns a genetic algorithm to design hybrid manufacturing systems composed of dedicated, general purpose (flexible) and reconfigurable machines. It has been considered a production mix with the customer demand, and the manufacturing operations required. The proposed model determines the machines to acquire among dedicated, flexible and reconfigurable machines and the production allocation over the planning horizon. Moreover, the combination of the genetic algorithm with the Monte Carlo simulation allows to include the market uncertainty in the decision model. A simulation environment has been developed to integrate the genetic algorithm and Monte Carlo approach

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The effect of system configuration and ramp-up time on manufacturing system acquisition under uncertain demand

Cited by 18 publications

References 20 publications

A framework to manage reconfigurability in manufacturing

A framework to manage reconfigurability in manufacturing

A Symbiotic Human–Machine Learning Approach for Production Ramp-up

A Decision Investment Model to Design Manufacturing Systems based on a genetic algorithm and Monte-Carlo simulation

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