Multi-Stage Decision-Making Skill Learning for Soccer Robot

Conditional-based maintenance (CBM) decision-making is of high interests in recent years due to its better performance on cost efficiency compared to other traditional policies. One of the most respected methods based on condition-monitoring data for maintenance decision-making is Proportional Hazards Model (PHM). It utilizes condition-monitoring data as covariates and identifies their effects on the lifetime of a component. Conventional modeling process of PHM only treats the degradation process as a whole lifecycle. In this paper, the PHM is advanced to describe a multi-zone degradation system considering the fact that the lifecycle of a machine can be divided into several different degradation stages. The methods to estimate reliability and performance prognostics are developed based on the proposed multi-zone PHM to predict the remaining time that the machine stays at the current stage before transferring into the next stage and the remaining useful life (RUL). The results illustrate that the multi-zone PHM effectively monitors the equipment status change and leads to a more accurate RUL prediction compared with traditional PHM.

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Entrepreneurial decisions: Insights into the use of support services for new business creation

South African Journal of Business Management ◽

10.4102/sajbm.v45i4.137 ◽

2014 ◽

Vol 45 (4) ◽

pp. 11-20 ◽

Cited By ~ 1

Author(s):

G. Ammetller ◽

I. Rodriguez-Ardurab ◽

J. Llados-Masllorens

Keyword(s):

Decision Making ◽

Support Services ◽

Local Development ◽

Integrative Model ◽

Resource Based View ◽

Utilization Of Services ◽

Multi Stage ◽

Business Creation ◽

New Business

This research presents an integrative model about the use of those services that have been specifically designed to support entrepreneurial initiative. By contrast with conventional perspectives from the entrepreneurship field, mainly drawn from a resource-based view, we propose a two-fold approach to explain the utilization of services that are oriented to new business creation: by considering the role of resources within the start-up's reach (internal and external); by incorporating a behavioral and decision-making approach. On the basis of the suggested decision-making framework, a multi-stage decision model is developed and tested by means of a representative sample of entrepreneurs linked to a local development agency. The results show that the adoption and use of support services for new business creation is a complex and reflexive process, triggered by the entrepreneur's internal forces.The entrepreneur searches for information throughout the process and, with assistance from internal teams and external networks, evaluates the choices of businesssupport services. Our findings offer relevant implications and recommendations for business incubators and institutions.

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Webrooming and showrooming: a multi-stage consumer decision process

Marketing Intelligence & Planning ◽

10.1108/mip-08-2020-0351 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Srabanti Mukherjee ◽

Swagato Chatterjee

Keyword(s):

Decision Making ◽

Information Search ◽

Structural Equation ◽

Product Type ◽

Equation Modeling ◽

Purchase Decision ◽

Decision Making Process ◽

Content Type ◽

Channel Choice ◽

Multi Stage

PurposeThe purpose of this research is to propose and validate a theoretical framework explaining web-rooming and showrooming as a multi-stage decision-making process. The authors have used consumer purchase decision-making theories to propose a model that identifies showrooming and webrooming as a combination of two decisions, channel choice during information search and channel choice during actual purchase. Further, the authors explored how various antecedents of showrooming and webrooming have differential effects on various stages of a purchase decision-making process and how product type moderates the relationships.Design/methodology/approachThe authors have conducted empirical research, whereby 243 responses were obtained from a cross-sectional survey. The authors have used structural equation modeling and multiple regression analysis to validate our theoretical model.FindingsWebrooming or showrooming is a multi-stage decision-making process for the consumers. First, consumers decide whether to search online or offline and then whether to buy online and offline. Different individual, purchase context-related and channel related factors impact these decisions. Product type governs which variables will be more important than others.Originality/valueThe research looks to enhance the understanding of the consumer's decision-making process during showrooming and webrooming while also helping retailers design and implement appropriate strategies that could affect consumers during information search and actual purchase.

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Multi-stage and Dynamic-Weighted Group Decision Making Process Based on Distance Degree and Close Degree Algorithm

INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences ◽

10.4156/aiss.vol5.issue5.115 ◽

2013 ◽

Vol 5 (5) ◽

pp. 968-977

Author(s):

Zhiping Lu ◽

Shanlin Yang

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Group Decision ◽

Decision Making Process ◽

Multi Stage

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Lean in higher education

Quality Assurance in Education ◽

10.1108/qae-12-2016-0089 ◽

2019 ◽

Vol 27 (1) ◽

pp. 82-102 ◽

Cited By ~ 2

Author(s):

Yigit Kazancoglu ◽

Yesim Deniz Ozkan-Ozen

Keyword(s):

Higher Education ◽

Decision Making ◽

Business School ◽

Stage Structure ◽

Fuzzy Decision Making ◽

Multi Criteria Decision Making ◽

Stage Model ◽

Content Type ◽

Road Map ◽

Multi Stage

PurposeThis research aims to investigate and define the eight wastes of lean philosophy in higher education institutions (HEIs) by proposing a multi-stage model.Design/methodology/approachThe authors have used a specific multi-criteria decision-making method, fuzzy decision-making trial and evaluation laboratory, to investigate the cause–effect relationships and importance order between criteria for wastes in HEIs. In total, 22 criteria were categorized under eight wastes of lean. The study was implemented in a business school with the participation of faculty members from different departments.FindingsThe results showed that the most important wastes in the business school selected were repeated tasks, unnecessary bureaucracy, errors because of misunderstanding/communication problems, excessive number of academic units and creation of an excessive amount of information. Another important result was that all the sub-wastes of talent were in the causes group, while motion and transportation wastes were in the effect group.Practical implicationsA road map to guide lean transformation for HEIs is proposed with a multi-stage model and potential areas for improvement in HEIs were presented.Originality/valueThis study proposes a multi-stage structure by applying multi-criteria decision-making to HEIs, focussing on wastes from a lean perspective.

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Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33012189 ◽

2019 ◽

Vol 33 ◽

pp. 2189-2196 ◽

Cited By ~ 1

Author(s):

Jun Wang ◽

Sujoy Sikdar ◽

Tyler Shepherd ◽

Zhibing Zhao ◽

Chunheng Jiang ◽

...

Keyword(s):

Machine Learning ◽

Decision Making ◽

Group Decision Making ◽

Group Decision ◽

Search Algorithms ◽

Voting Rules ◽

Practical Algorithms ◽

Multi Stage ◽

Parallel Universes ◽

Np Complete

STV and ranked pairs (RP) are two well-studied voting rules for group decision-making. They proceed in multiple rounds, and are affected by how ties are broken in each round. However, the literature is surprisingly vague about how ties should be broken. We propose the first algorithms for computing the set of alternatives that are winners under some tiebreaking mechanism under STV and RP, which is also known as parallel-universes tiebreaking (PUT). Unfortunately, PUT-winners are NP-complete to compute under STV and RP, and standard search algorithms from AI do not apply. We propose multiple DFS-based algorithms along with pruning strategies, heuristics, sampling and machine learning to prioritize search direction to significantly improve the performance. We also propose novel ILP formulations for PUT-winners under STV and RP, respectively. Experiments on synthetic and realworld data show that our algorithms are overall faster than ILP.

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