Spatial-temporal business partnership selection in uncertain environments

Arrais-Castro, António; Varela, Leonilde; Ribeiro, Rita A.; Putnik, Goran D.

doi:10.5937/fmet1504353a

Cited by 4 publications

(6 citation statements)

References 25 publications

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“…For example, when selecting a supplier among various potential candidates, their good or bad past behaviour should be considered in the final decision (Campanella and Ribeiro, 2011; Zulueta et al , 2013). This type of problems are commonly called DMCDM problems because they consider past behavior and/or changeable conditions of criteria ratings or even addition or removal of alternatives/criteria over time (Arrais-Castro et al , 2015b; Campanella et al , 2011; Campanella and Ribeiro, 2011; Jassbi et al , 2014a; Lin et al , 2008; Saaty, 2007; Simoes et al , 2012; Zulueta et al , 2013). In other words, DMCDM rates and ranks alternatives based on information collected at multiple time periods (Campanella and Ribeiro, 2011; Xu, 2008; Xu and Yager, 2008; Yao, 2010; Zhang et al , 2011; Zulueta et al , 2013).…”

Section: Brief Overview On Collaborative Network and Dynamic Systemsmentioning

confidence: 99%

“…Their framework (Campanella and Ribeiro, 2011) was successfully tested in two very different applications, a hazard avoidance landing of spacecraft (including a large set of alternatives (e.g. images) in changeable periods of time and another of supplier selection (Arrais-Castro et al , 2015b; Simoes et al , 2012). Later, Campanella et al (2011) applied the DMCDM model for supplier selection and combined it with an optimization process to select the optimum supplier in a network of collaborative business (B2B supplier selection).…”

Section: Brief Overview On Collaborative Network and Dynamic Systemsmentioning

confidence: 99%

“…Later, Campanella et al (2011) applied the DMCDM model for supplier selection and combined it with an optimization process to select the optimum supplier in a network of collaborative business (B2B supplier selection). In a recent study, Arrais-Castro et al (2015b) extended the concept by using a data fusion approach (Ribeiro et al , 2014) in supplier selection and applied historical, current, and forecasting information in each iteration of a dynamic decision system. In addition, Jassbi et al (2014a) proposed a new model for group DMCDM problems, which is influenced by time, population and location.…”

Section: Brief Overview On Collaborative Network and Dynamic Systemsmentioning

confidence: 99%

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Normalization techniques for collaborative networks

Vafaei

Ribeiro

Camarinha-Matos

et al. 2019

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Purpose Normalization is a crucial step in all decision models, to produce comparable and dimensionless data from heterogeneous data. As such, various normalization techniques are available but their performance depends on a number of characteristics of the problem at hand. Thus, this study aims to introduce a recommendation framework for supporting users to select data normalization techniques that better fit the requirements in different application scenarios, based on multi-criteria decision methods. Design/methodology/approach Following the proposed approach, the authors compare six well-known normalization techniques applied to a case study of selecting suppliers in collaborative networks. Findings With this recommendation framework, the authors expect to contribute to improving the normalization of criteria in the evaluation and selection of suppliers and business partners in dynamic networked collaborative systems. Originality/value This is the first study about comparing normalization techniques for selecting the best normalization in dynamic multiple-criteria decision-making models in collaborative networks.

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Section: Brief Overview On Collaborative Network and Dynamic Systemsmentioning

confidence: 99%

Section: Brief Overview On Collaborative Network and Dynamic Systemsmentioning

confidence: 99%

Section: Brief Overview On Collaborative Network and Dynamic Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Normalization techniques for collaborative networks

Vafaei

Ribeiro

Camarinha-Matos

et al. 2019

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“…In order to provide the capacity of simulations over the same scenarios, the "Environment Model Generation" is considered. Uncertainty of the environment dynamics is addressed for example in [5,6].…”

Section: Cyber-physical Production Systems (Cpps)mentioning

confidence: 99%

Cyber-Physical Production System (CPPS) decision making duration time impact on manufacturing system performance

Alveš¹,

Putnik²

2019

FME Transactions

Self Cite

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Cyber-Physical Production Systems (CPPS) are inherent to i4.0 to enhance actual manufacturing system control and management processes, and, consequently, the decision making process. This paper presents research on the influence of decision making duration time in CPPS on manufacturing system performance, for different scheduling paradigms. Different scenarios were investigated considering different decision making duration times and different variations of the environement dynamics. Results show that with the increase of the decision making duration time, the completion time of the jobs given at the input, as one of the principal manufacturing system performance measures, increases. Also, considering stable environments, the completion time variation growth in function of CPPS decision making duration time is approximately linear, while when considering dynamic environments, completion time variation growth in the function of CPPS decision making duration time is nonlinear.

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“…It is assumed that there is no flexibility of the resources scheduling (including machines and tools) for each operation of every job. It may meet the requirements of manufacturing system, such as optimization and intelligent support to bridge the lack of control and flexibility in dynamic systems with unexpected events, which may include uncertain data to be treated [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A human centered hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment

Alves¹,

Leonilde²,

Rocha³

et al. 2019

FME Transactions

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Manufacturing activities and production control are constantly growing. Despite this, it is necessary to improve the increasing variety of scheduling and layout adjustments for dynamic and flexible responses in volatile environments with disruptions or failures. Faced with the lack of realistic and practical manufacturing scenarios, this approach allows simulating and solving the problem of job shop scheduling on a production system by taking advantage of genetic algorithm and particle swarm optimization algorithm combined with the flexibility and robustness of a multi-agent system and dynamic rescheduling alternatives. Therefore, this hybrid decision support system intends to obtain optimized solutions and enable humans to interact with the system to properly adjust priorities or refine setups or solutions, in an interactive and user-friendly way. The system allows to evaluate the optimization performance of each one of the algorithms proposed, as well as to obtain decentralization in responsiveness and dynamic decisions for rescheduling due to the occurance of unexpected events.

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Spatial-temporal business partnership selection in uncertain environments

Abstract: Small and Medium (SME)

Cited by 4 publications

References 25 publications

Normalization techniques for collaborative networks

Normalization techniques for collaborative networks

Cyber-Physical Production System (CPPS) decision making duration time impact on manufacturing system performance

A human centered hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment

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