An investigation into modeling and simulation approaches for sustainable operations management

Modeling and simulation (M&S) studies have been widely used in industry to gain insights into existing or proposed systems of interest. The majority of these studies focus on productivity-related measures to evaluate systems' performance. This paradigm, however, needs to be shifted to cope with the advent of sustainability, as it is increasingly becoming an important issue in the managerial and the organizational agendas. The application of M&S to evaluate the often-competing metrics associated with sustainable operations management (SOM) is likely to be a challenge. The aim of this review is to investigate the underlying characteristics of SOM that lend towards modeling of production and service systems, and further to present an informed discussion on the suitability of specific modeling techniques in meeting the competing metrics for SOM. The triple bottom line, which is a widely used concept in sustainability and includes environmental, social, and economic aspects, is used as a benchmark for assessing this. Findings from our research suggest that a hybrid (combined) M&S approach could be an appropriate method for SOM analysis; however, it has its challenges.This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

Towards More Nuanced Patient Management: Decomposing Readmission Risk with Survival Models

Unplanned hospital readmissions are costly and associated with poorer patient outcomes. Overall readmission rates have also come to be used as performance metrics in reimbursement in healthcare policy, further motivating hospitals to identify and manage high-risk patients. Many models predicting readmission risk have been developed to facilitate the equitable measurement of readmission rates and to support hospital decision-makers in prioritising patients for interventions. However, these models consider the overall risk of readmission and are often restricted to a single time point. This work aims to develop the use of survival models to better support hospital decision-makers in managing readmission risk. First, semi-parametric statistical and nonparametric machine learning models are applied to adult patients admitted via the emergency department at Gold Coast University Hospital (n = 46,659) and Robina Hospital (n = 23,976) in Queensland, Australia. Overall model performance is assessed based on discrimination and calibration, as measured by time-dependent concordance and D-calibration. Second, a framework based on iterative hypothesis development and model fitting is proposed for decomposing readmission risk into persistent, patient-specific baselines and transient, care-related components using a sum of exponential hazards structure. Third, criteria for patient prioritisation based on the duration and magnitude of care-related risk components are developed. The extensibility of the framework and subsequent prioritisation criteria are considered for alternative populations, such as outpatient admissions and specific diagnosis groups, and different modelling techniques. Time-to-event models have rarely been applied for readmission modelling but can provide a rich description of the evolution of readmission risk post-discharge and support more nuanced patient management decisions than simple classification models

Valuing adaptation under rapid change

AbstractThe methods used to plan adaptation to climate change have been heavily influenced by scientific narratives of gradual change and economic narratives of marginal adjustments to that change. An investigation of the theoretical aspects of how the climate changes suggests that scientific narratives of climate change are socially constructed, biasing scientific narratives to descriptions of gradual as opposed rapid, non-linear change. Evidence of widespread step changes in recent climate records and in model projections of future climate is being overlooked because of this. Step-wise climate change has the potential to produce rapid increases in extreme events that can cross institutional, geographical and sectoral domains.Likewise, orthodox economics is not well suited to the deep uncertainty faced under climate change, requiring a multi-faceted approach to adaptation. The presence of tangible and intangible values range across five adaptation clusters: goods; services; capital assets and infrastructure; social assets and infrastructure; and natural assets and infrastructure. Standard economic methods have difficulty in giving adequate weight to the different types of values across these clusters. They also do not account well for the inter-connectedness of impacts and subsequent responses between agents in the economy. As a result, many highly-valued aspects of human and environmental capital are being overlooked.Recent extreme events are already pressuring areas of public policy, and national strategies for emergency response and disaster risk reduction are being developed as a consequence. However, the potential for an escalation of total damage costs due to rapid change requires a coordinated approach at the institutional level, involving all levels of government, the private sector and civil society.One of the largest risks of maladaptation is the potential for un-owned risks, as risks propagate across domains and responsibility for their management is poorly allocated between public and private interests, and between the roles of the individual and civil society. Economic strategies developed by the disaster community for disaster response and risk reduction provide a base to work from, but many gaps remain.We have developed a framework for valuing adaptation that has the following aspects: the valuation of impacts thus estimating values at risk, the evaluation of different adaptation options and strategies based on cost, and the valuation of benefits expressed as a combination of the benefits of avoided damages and a range of institutional values such as equity, justice, sustainability and profit.The choice of economic methods and tools used to assess adaptation depends largely on the ability to constrain uncertainty around problems (predictive uncertainty) and solutions (outcome uncertainty). Orthodox methods can be used where both are constrained, portfolio methodologies where problems are constrained and robust methodologies where solutions are constrained. Where both are unconstrained, process-based methods utilising innovation methods and adaptive management are most suitable. All methods should involve stakeholders where possible.Innovative processes methods that enable transformation will be required in some circumstances, to allow institutions, sectors and communities to prepare for anticipated major change.Please cite this report as: Jones, RN, Young, CK, Handmer, J, Keating, A, Mekala, GD, Sheehan, P 2013 Valuing adaptation under rapid change, National Climate Change Adaptation Research Facility, Gold Coast, pp. 192.The methods used to plan adaptation to climate change have been heavily influenced by scientific narratives of gradual change and economic narratives of marginal adjustments to that change. An investigation of the theoretical aspects of how the climate changes suggests that scientific narratives of climate change are socially constructed, biasing scientific narratives to descriptions of gradual as opposed rapid, non-linear change. Evidence of widespread step changes in recent climate records and in model projections of future climate is being overlooked because of this. Step-wise climate change has the potential to produce rapid increases in extreme events that can cross institutional, geographical and sectoral domains.Likewise, orthodox economics is not well suited to the deep uncertainty faced under climate change, requiring a multi-faceted approach to adaptation. The presence of tangible and intangible values range across five adaptation clusters: goods; services; capital assets and infrastructure; social assets and infrastructure; and natural assets and infrastructure. Standard economic methods have difficulty in giving adequate weight to the different types of values across these clusters. They also do not account well for the inter-connectedness of impacts and subsequent responses between agents in the economy. As a result, many highly-valued aspects of human and environmental capital are being overlooked.Recent extreme events are already pressuring areas of public policy, and national strategies for emergency response and disaster risk reduction are being developed as a consequence. However, the potential for an escalation of total damage costs due to rapid change requires a coordinated approach at the institutional level, involving all levels of government, the private sector and civil society.One of the largest risks of maladaptation is the potential for un-owned risks, as risks propagate across domains and responsibility for their management is poorly allocated between public and private interests, and between the roles of the individual and civil society. Economic strategies developed by the disaster community for disaster response and risk reduction provide a base to work from, but many gaps remain.We have developed a framework for valuing adaptation that has the following aspects: the valuation of impacts thus estimating values at risk, the evaluation of different adaptation options and strategies based on cost, and the valuation of benefits expressed as a combination of the benefits of avoided damages and a range of institutional values such as equity, justice, sustainability and profit.The choice of economic methods and tools used to assess adaptation depends largely on the ability to constrain uncertainty around problems (predictive uncertainty) and solutions (outcome uncertainty). Orthodox methods can be used where both are constrained, portfolio methodologies where problems are constrained and robust methodologies where solutions are constrained. Where both are unconstrained, process-based methods utilising innovation methods and adaptive management are most suitable. All methods should involve stakeholders where possible.Innovative processes methods that enable transformation will be required in some circumstances, to allow institutions, sectors and communities to prepare for anticipated major change

Economic Impact Assessment of Nature-Based Coastal Resilience Solutions in Charleston. Estimating Local Economic Effects With Algorithm-Based Supporting Tool.

Coastal cities are at the forefront of the risks induced by climate change. Local communities are adversely affected, but the essential cultural assets and economies are also at risk of damage or destruction. In the efforts to limit hazard risk exposure, local governments are increasingly planning for long-term flood protection. One prospective flood risk mitigation measure is living shorelines or nature-based adaptation. The coastal ecosystems, such as beaches, wetlands, barrier islands, oyster reefs, and salt marshes, deliver multiple benefits to communities, including recreation, natural resources, freshwater, and carbon sequestration. Moreover, when combined with structural solutions, they can effectively reduce water and storm wave energy levels. Despite these positive effects, the implementation of NbS is limited by a scarce understanding of their performance against uncertain sea level rise projections, their economic impacts, and funding gaps. In this research, I aimed to enforce this knowledge by assessing the economic values of various nature-based coastal adaptation solutions applied to the Charleston Peninsula. Tourism and recreation sectors substantially contribute to the local economy, and my assessment model focuses on them as a potential source of sharing resilience upfront costs. The nature-based adaptation generative algorithm model is spatially explicit and scenario-based, which helps reduce uncertainties. The adaptive interface helps answer my research questions and estimate the performance of Nature-based and Hybrid adaptation strategies in preventing tourism operation disruption over the planning scenarios. The model also evaluates recreational activity induced by proposed green infrastructure to expand the range of ecosystem services incorporated in the methods of economic impact assessment

Blending Efficiency and Resilience in the Performance Assessment of Urban Intersections: A Novel Heuristic Informed by Literature Review

Urban mobility underscores the vital importance of ensuring traffic efficiency on road segments, intersections, and transportation networks, especially in challenging circumstances. In this perspective, the essential approach to improving urban intersection efficiency should involve understanding critical factors for maintaining operational performance in the face of disruptions such as storms. This paper, inspired by a systematic literature review, presents a novel heuristic for evaluating urban intersection efficiency, with resilience as its guiding principle. The methodological path was designed to address the fundamental question: How can urban intersections be designed and managed to ensure efficiency and resilience in the face of disruptions? Drawing inspiration from the Highway Capacity Manual procedure, the methodological approach encompasses both pre-storm and post-storm scenarios, comparing delay times at roundabouts and signalized intersections before and after a storm. The results reveal significant changes in delay times for traffic signals, although the choice between roundabouts and signalized intersections should be context-specific, considering factors like traffic conditions, resilience requirements, and associated trade-offs. By shedding light on the interplay between intersection design, control strategies, and urban resilience, this research provides valuable insights into integrating resilience considerations into intersection performance assessment and management strategies. It also underscores how particular intersection designs can impact efficiency and recovery, essential considerations when assessing whether a road or intersection project is resilient

Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation

Reports of successful implementation of humanitarian optimization models in the field are scarce. Incorporating real conditions and the perspective of decision-makers in the analysis is crucial to enhance the practical value and managerial implications. Although it is known that implementation can be hindered by the lack of practitioner input in the structure of the model, its priorities, and the practicality of solution times, the way these aspects have been introduced in humanitarian optimization models has not been investigated. This study looks at the way research has involved practitioners in different aspects of the design of optimization models to promote implementation. It investigates the aspects affecting the implementation of the models and opportunities to guide future optimization contributions. The article introduces a systematic literature review of 105 articles to answer the research questions. The results are contrasted with a multi-criteria decision analysis using responses from Mexican practitioners. The study found that only 10% of the articles involved practitioners for modelling decisions, which was confirmed by a major gap between the objectives used in the literature and the priorities of Mexican practitioners. In terms of swift decision-making, fewer than 22% of the articles surveyed introduced new solution methods to deliver results in a sensible time. The study also identified very limited inclusion of environmental concerns in the objective functions even though these are a priority in the global agenda. These findings are discussed to propose research directions and suggest best practices for future contributions to promote the implementation of humanitarian logistics models

Critical analysis for big data studies in construction: significant gaps in knowledge

Purpose The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry. Design/methodology/approach The paper adopts systematic literature review (SLR) approach to observe and understand trends and extant patterns/themes in the big data analytics (BDA) research area particularly in construction-specific literature. Findings A significant rise in construction big data research is identified with an increasing trend in number of yearly articles. The main themes discussed were big data as a concept, big data analytical methods/techniques, big data opportunities – challenges and big data application. The paper emphasises “the implication of big data in to overall sustainability” as a gap that needs to be addressed. These implications are categorised as social, economic and environmental aspects. Research limitations/implications The SLR is carried out for construction technology and management research for the time period of 2007–2017 in Scopus and emerald databases only. Practical implications The paper enables practitioners to explore the key themes discussed around big data research as well as the practical applicability of big data techniques. The advances in existing big data research inform practitioners the current social, economic and environmental implications of big data which would ultimately help them to incorporate into their strategies to pursue competitive advantage. Identification of knowledge gaps helps keep the academic research move forward for a continuously evolving body of knowledge. The suggested new research avenues will inform future researchers for potential trending and untouched areas for research. Social implications Identification of knowledge gaps helps keep the academic research move forward for continuous improvement while learning. The continuously evolving body of knowledge is an asset to the society in terms of revealing the truth about emerging technologies. Originality/value There is currently no comprehensive review that addresses social, economic and environmental implications of big data in construction literature. Through this paper, these gaps are identified and filled in an understandable way. This paper establishes these gaps as key issues to consider for the continuous future improvement of big data research in the context of the construction industry