Software Requirements Triage and Selection: State-of-the-Art and State-of-Practice

2012 ◽  
Author(s):  
Mahvish Khurum ◽  
Niroopa Uppalapati ◽  
Ramya Chowdary Veeramachaneni
Keyword(s):  
State Of The Art ◽  
Software Requirements ◽  
State Of Practice ◽  
Art And State

scholarly journals Equipment Replacement Decision Making:Opportunities and Challenges

2013 ◽  
Author(s):  
Wei (David) Fan ◽  
Mason D. Gemar ◽  
Randy Machemehl
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
State Of The Art ◽  
Stochastic Dynamic ◽  
Comprehensive Review ◽  
Equipment Replacement ◽  
The Right ◽  
Art And State ◽  
Practice Literature ◽  
Solution Methodology

The primary function of equipment managers is to replace the right equipment at the right time and at the lowest overall cost. In this paper, the opportunities and challenges associated with equipment replacement optimization (ERO) are discussed in detail. First, a comprehensive review of the state-of-the art and state-of-the practice literature for the ERO problem is conducted. Second, a dynamic programming (DP) based optimization solution methodology is presented to solve the ERO problem. The Bellman’s formulation for the ERO deterministic (DDP) and stochastic dynamic programming (SDP) problems are discussed in detail. Finally, comprehensive ERO numerical results and implications are given.

Software Requirements: A Report on the State of the Art.

1980 ◽  
Author(s):  
Raymond T. Yeh ◽  
Pamela Zave ◽  
Alex Paul Conn ◽  
George E. Cole ◽  
Jr
Keyword(s):  
State Of The Art ◽  
The State ◽  
Software Requirements

scholarly journals Grand challenges in model-driven engineering: an analysis of the state of the research

2020 ◽  
Vol 19 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Antonio Bucchiarone ◽  
Jordi Cabot ◽  
Richard F. Paige ◽  
Alfonso Pierantonio
Keyword(s):  
Open Source ◽  
State Of The Art ◽  
The State ◽  
Model Driven Engineering ◽  
Grand Challenges ◽  
Model Driven ◽  
New Challenges ◽  
State Of Practice ◽  
State Of Research ◽  
Research Initiatives

AbstractIn 2017 and 2018, two events were held—in Marburg, Germany, and San Vigilio di Marebbe, Italy, respectively—focusing on an analysis of the state of research, state of practice, and state of the art in model-driven engineering (MDE). The events brought together experts from industry, academia, and the open-source community to assess what has changed in research in MDE over the last 10 years, what challenges remain, and what new challenges have arisen. This article reports on the results of those meetings, and presents a set of grand challenges that emerged from discussions and synthesis. These challenges could lead to research initiatives for the community going forward.

Practices of preventive maintenance planning in discrete manufacturing industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Antti Salonen ◽  
Maheshwaran Gopalakrishnan
Keyword(s):  
Preventive Maintenance ◽  
Manufacturing Industry ◽  
State Of The Art ◽  
The State ◽  
Data Driven ◽  
Manufacturing Companies ◽  
Automotive Manufacturing ◽  
Content Type ◽  
Discrete Manufacturing ◽  
State Of Practice

PurposeThe purpose of this study was to assess the readiness of the Swedish manufacturing industry to implement dynamic, data-driven preventive maintenance (PM) by identifying the gap between the state of the art and the state of practice.Design/methodology/approachAn embedded multiple case study was performed in which some of the largest companies in the discrete manufacturing industry, that is, mechanical engineering, were surveyed regarding the design of their PM programmes.FindingsThe studied manufacturing companies make limited use of the existing scientific state of the art when designing their PM programmes. They seem to be aware of the possibilities for improvement, but they also see obstacles to changing their practices according to future requirements.Practical implicationsThe results of this study will benefit both industry professionals and academicians, setting the initial stage for the development of data-driven, diversified and dynamic PM programmes.Originality/ValueFirst and foremost, this study maps the current state and practice in PM planning among some of the larger automotive manufacturing industries in Sweden. This work reveals a gap between the state of the art and the state of practice in the design of PM programmes. Insights regarding this gap show large improvement potentials which may prove important for academics as well as practitioners.

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