811 research outputs found
Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks
Previously held under moratorium from 14 December 2016 until 19 January 2022There is an increasing interest in the resilience of supply chains given the growing
awareness of their vulnerabilities to natural and man-made hazards. Contemporary
academic literature considers, for example, so-called resilience enablers and strategies,
such as improving the nature of collaboration and flexibility within the supply chain.
Efforts to analyse resilience tend to view the supply chain as a complex system. The
present research adopts a distinctive approach to the analysis of supply resilience by
building formal models from the perspective of the responsible manager. Dynamic
Bayesian Networks (DBNs) are selected as the modelling method since they are capable
of representing the temporal evolution of uncertainties affecting supply. They also
support probabilistic analysis to estimate the impact of potentially hazardous events
through time. In this way, the recovery rate of the supply chain under mitigation action
scenarios and an understanding of resilience can be obtained.
The research is grounded in multiple case studies of manufacturing and retail supply
chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each
case involves building models to estimate the resilience of the supply chain given
uncertainties about, for example, business continuity, lumpy spare parts demand and
operations of critical infrastructure. DBNs have been developed by using relevant data
from historical empirical records and subjective judgement. Through the modelling
practice, It has been found that some SC characteristics (i.e. level of integration,
structure, SC operating system) play a vital role in shaping and quantifying DBNs and
reduce their elicitation burden. Similarly, It has been found that the static and dynamic
discretization methods of continuous variables affect the DBNs building process.
I also studied the effect of level of integration, visibility, structure and SC operating
system on the resilience level of SCs through the analysis of DBNs outputs. I found that
the influence of the integration intensity on supply chain resilience can be revealed
through understanding the dependency level of the focal firm on SC members resources. I
have also noticed the relationship between the span of integration and the level of
visibility to SC members. This visibility affects the capability of SC managers in the focal
firm to identify the SC hazards and their consequences and, therefore, improve the
planning for adverse events. I also explained how some decision rules related to SC
operating system such as the inventory strategy could influence the intermediate ability of
SC to react to adverse events. By interpreting my case data in the light of the existing
academic literature, I can formulate some specific propositions.There is an increasing interest in the resilience of supply chains given the growing
awareness of their vulnerabilities to natural and man-made hazards. Contemporary
academic literature considers, for example, so-called resilience enablers and strategies,
such as improving the nature of collaboration and flexibility within the supply chain.
Efforts to analyse resilience tend to view the supply chain as a complex system. The
present research adopts a distinctive approach to the analysis of supply resilience by
building formal models from the perspective of the responsible manager. Dynamic
Bayesian Networks (DBNs) are selected as the modelling method since they are capable
of representing the temporal evolution of uncertainties affecting supply. They also
support probabilistic analysis to estimate the impact of potentially hazardous events
through time. In this way, the recovery rate of the supply chain under mitigation action
scenarios and an understanding of resilience can be obtained.
The research is grounded in multiple case studies of manufacturing and retail supply
chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each
case involves building models to estimate the resilience of the supply chain given
uncertainties about, for example, business continuity, lumpy spare parts demand and
operations of critical infrastructure. DBNs have been developed by using relevant data
from historical empirical records and subjective judgement. Through the modelling
practice, It has been found that some SC characteristics (i.e. level of integration,
structure, SC operating system) play a vital role in shaping and quantifying DBNs and
reduce their elicitation burden. Similarly, It has been found that the static and dynamic
discretization methods of continuous variables affect the DBNs building process.
I also studied the effect of level of integration, visibility, structure and SC operating
system on the resilience level of SCs through the analysis of DBNs outputs. I found that
the influence of the integration intensity on supply chain resilience can be revealed
through understanding the dependency level of the focal firm on SC members resources. I
have also noticed the relationship between the span of integration and the level of
visibility to SC members. This visibility affects the capability of SC managers in the focal
firm to identify the SC hazards and their consequences and, therefore, improve the
planning for adverse events. I also explained how some decision rules related to SC
operating system such as the inventory strategy could influence the intermediate ability of
SC to react to adverse events. By interpreting my case data in the light of the existing
academic literature, I can formulate some specific propositions
Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions
The focus of this paper is on solving multi-robot planning problems in
continuous spaces with partial observability. Decentralized partially
observable Markov decision processes (Dec-POMDPs) are general models for
multi-robot coordination problems, but representing and solving Dec-POMDPs is
often intractable for large problems. To allow for a high-level representation
that is natural for multi-robot problems and scalable to large discrete and
continuous problems, this paper extends the Dec-POMDP model to the
decentralized partially observable semi-Markov decision process (Dec-POSMDP).
The Dec-POSMDP formulation allows asynchronous decision-making by the robots,
which is crucial in multi-robot domains. We also present an algorithm for
solving this Dec-POSMDP which is much more scalable than previous methods since
it can incorporate closed-loop belief space macro-actions in planning. These
macro-actions are automatically constructed to produce robust solutions. The
proposed method's performance is evaluated on a complex multi-robot package
delivery problem under uncertainty, showing that our approach can naturally
represent multi-robot problems and provide high-quality solutions for
large-scale problems
Experimental Study of Physio-Mechanical and Engineering Properties of Clayey Soil Incorporating Hydraulic Lime and Nano-Silica
Soil is one of the most abundant and frequently used materials in geotechnical engineering and construction. Soil is heterogenous material with different minerals some of which are classified as problematic minerals. The soil is classified as unacceptable for construction purpose if some of these minerals are present in the soil. Soil improvement utilizing nano materials is a novel technique to upgrade engineering and shear strength parameters of problematic soils. The effects of varying amounts of nano-silica and lime in clayey soil are investigated in this study. For this purpose, soil samples were moulded by incorporating 0, 3, 6, 9% nano silica and 0, 5, and 10% lime. The samples were tested for Atterberg limits, Plasticity index, optimum moisture content OMC, maximum dry density MDD, swelling, and unconfined compressive strength (UCS) at 7 and 28 days. The results revealed that addition of nano-silica in lime treated high plastic clay improved the plasticity index as well as UCS and swelling behaviour by a significant margin. Results highlighted that incorporation of nano silica reduced the plasticity index. UCS values of the soil increased by adding nano silica 28 days UCS increased by 10 times as compared to 7 days strength. The swelling in soil samples with 10% lime and 9% nano silica is reduced by almost 32% as compared to controlled samples. OMC is also increased by 17% meanwhile MDD is reduced by 9% when nano silica was added. The findings of this study can be used in any project that requires improved engineering and geotechnical properties of high plastic clayey soil for shallow foundation
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