45 research outputs found
Critical chain identication and buffer sizing for efficient project management
No full textProject management organises about 30% of the world's economy (Hu et al., 2015b). Many recent projects apply critical chain project management (CCPM) methodology, which requires critical chain identification and design of project and feeding buffers. Critical chain identification is fundamental as it provides a baseline schedule without resource contentions. Subsequently, accurate sizing of the time buffers is essential, because too small buffers result in emergency procedures to prevent late delivery, whereas too large buffers result in uncompetitive bids and lost contracts. Previous research simply treats the former as a standard resource-constrained project scheduling problem (RCPSP) and predominantly focuses on the buffer sizing problem. The work typically results in excessive buffers and in critical chains being challenged by the insertion of feeding buffers, leading to inconsistent performance in project makespan estimation.
In this research, we start with an explicit definition for the problem of critical chain identification considering how to deal with resource contentions. In addition to the RCPSP method that avoids concurrent processing of tasks involved, three new methods that allow for concurrent processing of tasks via trade-off between time and cost/resource are proposed and represented in mathematical programming models, which are actually generalised RCPSPs and potentially provide shorter critical chains and CCPM schedules. Then, heuristics are proposed to solve these NP-hard models. Experimental analysis on wide-ranging real-life project data confirms the effectiveness of these methods and tests the validity of the proposed heuristics against benchmarks.
Given that the critical chain and baseline schedule are determined, we develop a new buffer sizing procedure based on analytical network decomposition. The procedure is implementable for any project network and offers logical advantages over previous ones. First, the size of a feeding buffer is determined from all associated noncritical chains. Second, the project buffer incorporates safety margins outside the critical chain by comparing feeding chains with their parallel critical counterparts. Computational testing on a case study of a real project and extensive simulated data shows that our procedure delivers much greater accuracy in estimating project makespan, and smaller feeding buffers. Furthermore, the resulting critical chain is never challenged. Additional benefits include delayed expenditure, and reductions in work-in-process, rework, and multitasking.
Then, an improved CCPM method is obtained by combining the critical chain identification and buffer sizing procedures. We conduct a numerical study on the time performance of the CCPM compared to traditional critical path methods, using diverse real-life project data and considering different scenarios of uncertainties and risk preferences. The results indicate consistent advantages of CCPM regarding short and accurate project makespan estimates. Comprehensive information of how each method performs in each scenario is also provided to help with the decision making of appropriate scheduling techniques for any specified project.
Overall, this research fundamentally improves the CCPM methodology to deliver efficient project schedules and provides clear guidelines for project managers to choose the right scheduling techniques for real-life projects
Buffer sizing in critical chain project management by network decomposition
No full textProject management organizes about 30% of the world's economy. Many recent projects apply critical chain project management (CCPM) methodology, which requires the design of project and feeding buffers. Accurate sizing of these buffers is essential, because too small buffers result in emergency procedures to prevent late delivery, whereas too large buffers result in uncompetitive bids and lost contracts. Previous buffer sizing research, focused predominantly on the critical chain, typically results in excessive buffers, and in critical chains being challenged by feeding buffers during planning. This work also performs inconsistently, for example in makespan estimation, at execution. We propose a new procedure for buffer sizing based on network decomposition, which offers logical advantages over previous ones. First, the size of a feeding buffer is determined from all associated noncritical chains. Second, the project buffer incorporates safety margins outside the critical chain by comparing feeding chains with their parallel critical counterparts. Computational testing on a case study of a real project and extensive simulated data shows that our procedure delivers much greater accuracy in estimating project makespan, and smaller feeding buffers. Furthermore, the resulting critical chain is never challenged. Additional benefits include delayed expenditure, and reductions in work-in-process, rework, and multitasking
Data repository for manuscript "A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi"
No full textDataset to accompany the publication “A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi” by Margherita Molaro, Sakshi Mohan, Bingling She, Martin Chalkley, Tim Colbourn, Joseph H. Collins, Emilia Connolly, Matthew M. Graham, Eva Janoušková, Ines Li Lin, Gerald Manthalu, Emmanuel Mnjowe, Dominic Nkhoma, Pakwanja D. Twea, Andrew N. Phillips, Paul Revill, Asif U. Tamuri, Joseph Mfutso-Bengo, Tara Mangal, and Timothy B. Hallett.
The Thanzi La Onse (TLO) model used to produce this data is open source and available for review and usage at https://github.com/UCL/TLOmodel. In particular, the outputs analysed in this study can be reproduced from model tag "Molaro_et_al_2024_HBP_design" (accessible at https://github.com/UCL/TLOmodel/tags) using the scenario file src/scripts/healthsystem/impact_of_policy/scenario_impact_of_policy.py. All analysis scripts used to generate the plots in the manuscript are located in the same directory and have filenames beginning with "analysis_impact_of_policy_".
This repository contains post-processed simulation outputs, which were generated using the script src/scripts/healthsystem/impact_of_policy/analysis_extract_data.py (available from the same tag). The data included have the following structure:
"Draw": Represents a specific prioritisation-policy, identified by the acronyms listed in Table 1 of the publication.
"Run": Represents a single simulation instance of a draw. Each draw was simulated 10 times, each with independent random sampling, resulting in 10 "runs" per draw.
The data files included in this repository are:
DALYS_by_cause_with_time.csv: DALYs (as defined in the publication) incurred on a given year due to each of the causes of DALYs considered.
HSIs_requested_by_type_and_facility_level_with_time.csv: total number of requested HSIs on a given year, broken down by HSI type and the facility level at which they were requested.
HSIs_delivered_by_type_and_facility_level_with_time.csv:total number of HSIs delivered on a given year broken down by HSI type and the facility level at which they were delivered.
Population_with_time.csv:total population size on a given year
Tail behaviour analysis and robust regression meets modern methodologies
No full textDiese Arbeit stellt Modelle und Methoden vor, die für robuste Statistiken und ihre Anwendungen in verschiedenen Bereichen entwickelt wurden.
Kapitel 2 stellt einen neuartigen Partitionierungs-Clustering-Algorithmus vor, der auf Expectiles basiert. Der Algorithmus bildet Cluster, die sich an das Endverhalten der Clusterverteilungen anpassen und sie dadurch robuster machen. Das Kapitel stellt feste Tau-Clustering- und adaptive Tau-Clustering-Schemata und ihre Anwendungen im Kryptowährungsmarkt und in der Bildsegmentierung vor. In Kapitel 3 wird ein faktorerweitertes dynamisches Modell vorgeschlagen, um das Tail-Verhalten hochdimensionaler Zeitreihen zu analysieren. Dieses Modell extrahiert latente Faktoren, die durch Extremereignisse verursacht werden, und untersucht ihre Wechselwirkung mit makroökonomischen Variablen mithilfe des VAR-Modells. Diese Methodik ermöglicht Impuls-Antwort-Analysen, Out-of-Sample-Vorhersagen und die Untersuchung von Netzwerkeffekten. Die empirische Studie stellt den signifikanten Einfluss von durch finanzielle Extremereignisse bedingten Faktoren auf makroökonomische Variablen während verschiedener Wirtschaftsperioden dar. Kapitel 4 ist eine Pilotanalyse zu Non Fungible Tokens (NFTs), insbesondere CryptoPunks. Der Autor untersucht die Clusterbildung zwischen digitalen Assets mithilfe verschiedener Visualisierungstechniken. Die durch CNN- und UMAP-Regression identifizierten Cluster werden mit Preisen und Merkmalen von CryptoPunks in Verbindung gebracht.
Kapitel 5 stellt die Konstruktion eines Preisindex namens Digital Art Index (DAI) für den NFT-Kunstmarkt vor. Der Index wird mithilfe hedonischer Regression in Kombination mit robusten Schätzern für die Top-10-Liquid-NFT-Kunstsammlungen erstellt. Es schlägt innovative Verfahren vor, nämlich Huberisierung und DCS-t-Filterung, um abweichende Preisbeobachtungen zu verarbeiten und einen robusten Index zu erstellen. Darüber hinaus werden Preisdeterminanten des NFT-Marktes analysiert.This thesis provides models and methodologies developed on robust statistics and their applications in various domains. Chapter 2 presents a novel partitioning clustering algorithm based on expectiles. The algorithm forms clusters that adapt to the tail behavior of the cluster distributions, making them more robust. The chapter introduces fixed tau-clustering and adaptive tau-clustering schemes and their applications in crypto-currency market and image segmentation. In Chapter 3 a factor augmented dynamic model is proposed to analyse tail behavior of high-dimensional time series. This model extracts latent factors driven by tail events and examines their interaction with macroeconomic variables using VAR model. This methodology enables impulse-response analysis, out-of-sample predictions, and the study of network effects. The empirical study presents significant impact of financial tail event driven factors on macroeconomic variables during different economic periods. Chapter 4 is a pilot analysis on Non Fungible Tokens (NFTs) specifically CryptoPunks. The author investigates clustering among digital assets using various visualization techniques. The clusters identified through regression CNN and UMAP are associated with prices and traits of CryptoPunks. Chapter 5 introduces the construction of a price index called the Digital Art Index (DAI) for the NFT art market. The index is created using hedonic regression combined with robust estimators on the top 10 liquid NFT art collections. It proposes innovative procedures, namely Huberization and DCS-t filtering, to handle outlying price observations and create a robust index. Furthermore, it analyzes price determinants of the NFT market
Emotional State as a Key Driver of Public Preferences for Flower Color
No full textFlowers, as integral elements of urban landscapes, are critical not only for aesthetic purposes but also for fostering human–nature interactions in green spaces. However, research on flower color preferences has largely been descriptive, and there is a lack of exploration of potential mechanisms influencing flower color preferences, such as economic and social factors. This study created visual samples through precise color adjustment techniques and introduced the L*, a*, and b* parameters from the CIELAB color system to quantify the flower colors of the survey samples, conducting an online survey with 354 Chinese residents. The complex aesthetic process’s driving factors were unveiled through a comprehensive analysis using a Generalized Additive Model (GAM), a piecewise Structural Equation Model (SEM), and linear regression models. The results show that the public’s flower color preference is primarily related to the a* and b* parameters, which represent color dimensions in the CIELAB color space, and it is not significantly related to L* (lightness). Factors such as age, annual household income level (AI), personal income sources (PI), nature experience, and emotional state (TMD) significantly influence color preferences, with emotional state identified as the most critical factor. Lastly, linear regression models further explain the potential mechanism of the influencing factors. This study proposes a framework to assist urban planners in selecting flower colors that resonate with diverse populations, enhancing both the attractiveness of urban green spaces and their potential to promote pro-environmental behavior. By aligning flower color design with public preferences, this study contributes to sustainable urban planning practices aimed at improving human well-being and fostering deeper connections with nature
Incorporating an economic approach to production in a health system model
No full textAs computational capacity increases, it becomes possible to model health systems in greater detail. Multi-disease health system models (HSMs) represent a new development, building on individual level epidemiological models of multiple diseases and capturing how healthcare delivery systems respond to population health needs. The Thanzi la Onse (TLO) model of Malawi is the first of its kind in these respects. In this article, we discuss how we have been bringing economic concepts into the TLO model, and how we are continuing to develop this line of research. This has involved incorporating more sophisticated approaches to account for the effects of the unavailability of healthcare workers, and we are working towards establishing the role of different forms of ownership of healthcare facilities and different management practices. Not only does this broad approach make the model more flexible as a tool for understanding the impact of resource constraints, it opens up the possibility of analysing considerably richer policy scenarios; for example establishing an estimate of the health gain that could be achieved through expanding the workforce or reducing healthcare worker absence
An individual-based modelling study estimating the impact of maternity service delivery on health in Malawi - Code and data repository
No full textThis is a release of the Thanzi La Onse model (https://www.tlomodel.org/) code specifically for the analysis conducted in the paper "An individual-based modelling study estimating the impact of maternity service delivery on health in Malawi". In addition, the unprocessed logfiles for the analysis conducted in this paper are available here
The impact of precipitation on ANC service utilisation and healthcare access in Malawi
No full textMalawi is vulnerable to climate-related shocks, which are projected to worsen. Whilst some dimensions of this vulnerability have been characterised, little is known about healthcare sector resilience. Coupling facility-specific data on antenatal care (ANC) service provision in Malawi with gridded precipitation data from 2012-2024 we use linear regression analyses to characterise the historic relationship between precipitation and healthcare access. We estimate that precipitation negatively impacted ANC service utilisation in Malawi, with up to 1 in 20 appointments disrupted annually in some districts. Projecting further to 2060 indicates that, cumulatively, up to 250,000 pregnancies could be affected. Notably, if precipitation patterns from 1941 to 1953 had persisted into the 21st century, disruptions between 2012 and 2024 would be a hundred times less frequent, highlighting the significant influence of anthropogenic climate change on healthcare access. In a country already facing high maternal and neonatal mortality, such disruptions could further hinder access to care and worsen health outcomes. To mitigate this, interventions should focus on preserving or improving the physical accessibility of facilities, particularly through resilient transport services and road networks
Conceptualising Health Systems for Economic Evaluation:A Review and Framework for Health System Models
No full textMulti-disease Health System Models (HSMs) represent a new frontier in economic evaluation, enabling decision analysis for sector-wide resource allocation in the context of interacting health needs, system capacity and financial constraints. To support the development of conceptually grounded HSMs, we conducted a meta-narrative review of conceptual frameworks from the economic evaluation and health system assessment literature to understand how health systems and their relationships with different types of outcomes have previously been represented. Four main approaches were identified: health systems as a set of functions, as production constraints on overall healthcare, as intervention-specific constraints along the care pathway, and as complex adaptive systems. We assess the strengths and limitations of each in informing the structure, scope, and causal logic of HSMs. Drawing from our experience developing the Thanzi La Onse model of Malawi’s health system, we propose a new conceptual framework for HSMs, grounded in theory of change principles and informed by prior literature on health system assessment. The framework is designed to support the full lifecycle of HSMs, from model design and intervention representation to the transparent communication of assumptions and results. By clarifying causal pathways, enabling the representation of diverse interventions, and facilitating stakeholder engagement and policy translation, the framework provides a bridge between high-level conceptual thinking and the operational needs of policy-relevant modelling. This work seeks to advance how health sector policies and investments are conceptualised in economic evaluation and to guide the continued development of health system modelling approaches
