scholarly journals Embedding ensemble learning into simulation-based optimisation: a learning-based optimisation approach for construction planning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kailun Feng ◽  
Shiwei Chen ◽  
Weizhuo Lu ◽  
Shuo Wang ◽  
Bin Yang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Large Scale ◽  
Quality Measurement ◽  
Construction Planning ◽  
Content Type ◽  
Engineering Construction ◽  
Alternative Approach ◽  
Simulation Based ◽  
Computing Platforms ◽  
Searching Ability

PurposeSimulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.Design/methodology/approachThis study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.FindingsA large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.Originality/valueThe core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Post-hazard labor wage fluctuations: a comparative empirical analysis among different sub-sectors of the U.S. construction sector

2020 ◽  
Vol 25 (3) ◽  
pp. 313-330
Author(s):  
Navid Ahmadi Esfahani ◽  
Mohsen Shahandashti
Keyword(s):  
Large Scale ◽  
Civil Engineering ◽  
Labor Cost ◽  
Industrial Building ◽  
County Level ◽  
Construction Sector ◽  
Location Quotient ◽  
Content Type ◽  
Engineering Construction ◽  
Industry Group

Purpose The primary objectives of this study are to (1) highlight subsectors and industry groups of the construction sector that are most vulnerable to weather-related disasters (with highest labor cost escalation) and (2) analyze how immediate this labor wage escalation happens in different subsector of the construction sector. Design/methodology/approach The research methodology consists of three steps: (i) integrating various data sources to enable measurement of the county-level labor wage changes following large-scale weather-related disasters; (ii) measuring postdisaster labor wage changes at the county level; and (iii) comparing amount and timing of postdisaster labor wage changes among all sub-sectors (and industry groups) of the construction sector. Findings The results show that among the three construction subsectors (Heavy and Civil Engineering Construction subsector, Construction of Buildings subsector, and Specialty Trade Contractors sub-sector), Heavy and Civil Engineering Construction subsector is the most vulnerable to weather-related disasters. The industry groups under the Heavy and Civil Engineering Construction subsector showed the same vulnerability level; however, under the Construction of Buildings subsector, Industrial Building Construction industry group showed to be the most vulnerable; and under the Specialty Trade Contractors subsector, the Building Foundation and Exterior Contractors industry group is the most vulnerable. The results also showed that in approximately 75% of the damaged counties, there were increases in wages of all construction labors, over the following three quarter after the disasters. In average, labor wages in Construction of Buildings subsector and the Specialty Trade Contractors subsector decreased by 0.6% and 0.8%, respectively, in the quarter of disaster and gradually increased by 4.4% and 4.6%, respectively, in the following three quarters. On the other hand, Heavy and Civil Engineering Construction’s labor wages did not experience this decrease right after the disasters; wages increased immediately after disasters hit the counties and continually increased by 8.6% in three quarters after the disasters. It is expected that the results of this study will help policy makers, cost estimators and insurers to have a better understanding of the post-disaster construction labor wage fluctuations. Originality/value This study is unique in the way it used construction labor wage data. All data are location quotient, which makes the comparison among the affected counties (with different construction size) feasible.

scholarly journals An applied approach to multi-criteria humanitarian supply chain planning for pandemic response

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Behnam Malmir ◽  
Christopher W. Zobel
Keyword(s):  
Large Scale ◽  
Primary Source ◽  
Real Data ◽  
Content Type ◽  
Simulation Based ◽  
Actual Growth Rate ◽  
Actual Growth ◽  
Deprivation Costs ◽  
Humanitarian Supply Chain ◽  
The Cost

PurposeWhen a large-scale outbreak such as the COVID-19 pandemic happens, organizations that are responsible for delivering relief may face a lack of both provisions and human resources. Governments are the primary source for the humanitarian supplies required during such a crisis; however, coordination with humanitarian NGOs in handling such pandemics is a vital form of public-private partnership (PPP). Aid organizations have to consider not only the total degree of demand satisfaction in such cases but also the obligation that relief goods such as medicine and foods should be distributed as equitably as possible within the affected areas (AAs).Design/methodology/approachGiven the challenges of acquiring real data associated with procuring relief items during the COVID-19 outbreak, a comprehensive simulation-based plan is used to generate 243 small, medium and large-sized problems with uncertain demand, and these problems are solved to optimality using GAMS. Finally, post-optimality analyses are conducted, and some useful managerial insights are presented.FindingsThe results imply that given a reasonable measure of deprivation costs, it can be important for managers to focus less on the logistical costs of delivering resources and more on the value associated with quickly and effectively reducing the overall suffering of the affected individuals. It is also important for managers to recognize that even though deprivation costs and transportation costs are both increasing as the time horizon increases, the actual growth rate of the deprivation costs decreases over time.Originality/valueIn this paper, a novel mathematical model is presented to minimize the total costs of delivering humanitarian aid for pandemic relief. With a focus on sustainability of operations, the model incorporates total transportation and delivery costs, the cost of utilizing the transportation fleet (transportation mode cost), and equity and deprivation costs. Taking social costs such as deprivation and equity costs into account, in addition to other important classic cost terms, enables managers to organize the best possible response when such outbreaks happen.

scholarly journals The V-model of service quality: an African case study

2017 ◽  
Vol 51 (5/6) ◽  
pp. 923-945
Author(s):  
Grafton Whyte ◽  
Andy Bytheway
Keyword(s):  
Data Collection ◽  
Service Quality ◽  
Large Scale ◽  
Service Management ◽  
Quality Measurement ◽  
Psychological Theory ◽  
National Study ◽  
Complex Data ◽  
Content Type ◽  
New Model

Purpose This paper aims to introduce and demonstrate a new model for service quality that separates out the measurement of service quality in ways grounded in psychological theory and methodological symmetry. Design/methodology/approach A review of experience in service quality management suggests that new approaches are needed. By seeking a way of managing service at different levels, with symmetry between data collection and data analysis, a model is presented that has more potential applicability and flexibility than is found in traditional models. Findings A national study in Namibia, Africa provided data that successfully demonstrate the method of working and illustrate the contextual, analytical and data management issues and the reporting potential out of complex service management data. Research limitations/implications This new approach to the design of service quality measurement and assessment extends the capability that is generally found in other existing approaches. It provides a new foundation for further research into complex patterns of service success and that will establish more clearly the inter-dependencies between service encounters, service attributes and service measures at the survey item level. Practical implications Studies of multiple service sectors and multiple service recipient groups can now gather and manage large complex data sets and analyse and report that data in ways appropriate to the needs of different stakeholders. Social implications In any context where service quality is a socio-economic or development issue, it is now possible to take a more careful and nuanced approach to the collection and aggregation of data, which will inform policy makers and other stakeholder groups at the national or regional level. Originality/value This new model addresses a range of problems that have been reported with historical approaches such as SERVQUAL and related methods of working. It also provides foundations for new designs for large-scale service management data collection, organisation and analysis.

An intelligent simulation-based framework for automated planning of concrete construction works

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sina Mohammadi ◽  
Mehdi Tavakolan ◽  
Banafsheh Zahraie
Keyword(s):  
Deep Level ◽  
Discrete Event ◽  
Resource Planning ◽  
Inference Engine ◽  
Construction Planning ◽  
Content Type ◽  
Simulation Based ◽  
Intelligent Simulation ◽  
Construction Product ◽  
Planning Knowledge

PurposeThis paper proposes an innovative intelligent simulation-based construction planning framework that introduces a new approach to simulation-based construction planning.Design/methodology/approachIn this approach, the authors developed an ontological inference engine as an integrated part of a constraint-based simulation system that configures the construction processes, defines activities and manages resources considering a variety of requirements and constraints during the simulation. It allows for the incorporation of the latest project information and a deep level of construction planning knowledge in the planning. The construction planning knowledge is represented by an ontology and several semantic rules. Also, the proposed framework uses the project building information model (BIM) to extract information regarding the construction product and the relations between elements. The extracted information is then converted to an ontological format to be useable by the framework.FindingsThe authors implemented the framework in a case study project and tested its usefulness and capabilities. It successfully generated the construction processes, activities and required resources based on the construction product, available resources and the planning rules. It also allowed for a variety of analyses regarding different construction strategies and resource planning. Moreover, 4D BIM models that provide a very good understanding of the construction plan can be automatically generated using the proposed framework.Originality/valueThe active integration between BIM, discrete-event simulation (DES) and ontological knowledge base and inference engine defines a new class of construction simulation with expandable applications.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammadreza Yaghoobi ◽  
Krzysztof S. Stopka ◽  
Aaditya Lakshmanan ◽  
Veera Sundararaghavan ◽  
John E. Allison ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Metals And Alloys ◽  
Analysis Tool ◽  
Computational Framework ◽  
Crystal Plasticity Finite Element ◽  
Polycrystalline Metals ◽  
Simulation Based ◽  
Open Source Framework

AbstractThe PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

The dialectics of authoring expansive learning: tracing the long tail of a Change Laboratory

2016 ◽  
Vol 28 (4) ◽  
pp. 245-262 ◽  
Author(s):  
Annalisa Sannino ◽  
Yrjö Engeström ◽  
Johanna Lahikainen
Keyword(s):  
Large Scale ◽  
Scale Transformation ◽  
Dialectical Tensions ◽  
Content Type ◽  
Organization Model ◽  
Expansive Learning ◽  
Conceptual Argument ◽  
University Library ◽  
Dialectical Process ◽  
Cultural Historical Activity

Purpose The paper aims to examine organizational authoring understood as a longitudinal, material and dialectical process of transformation efforts. The following questions are asked: To which extent can a Change Laboratory intervention help practitioners author their own learning? Are the authored outcomes of a Change Laboratory intervention futile if a workplace subsequently undergoes large-scale organizational transformations? Does the expansive learning authored in a Change Laboratory intervention survive large-scale organizational transformations, and if so, why does it survive and how? Design/methodology/approach The paper develops a conceptual argument based on cultural–historical activity theory. The conceptual argument is grounded in the examination of a case of eight years of change efforts in a university library, including a Change Laboratory (CL) intervention. Follow-up interview data are used to discuss and illuminate our argument in relation to the three research questions. Findings The idea of knotworking constructed in the CL process became a “germ cell” that generates novel solutions in the library activity. A large-scale transformation from the local organization model developed in the CL process to the organization model of the entire university library was not experienced as a loss. The dialectical tension between the local and global models became a source of movement driven by the emerging expansive object. Practitioners are modeling their own collective future competences, expanding them both in socio-spatial scope and interactive depth. Originality/value The article offers an expanded view of authorship, calling attention to material changes and practical change actions. The dialectical tensions identified serve as heuristic guidelines for future studies and interventions.

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

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