Suzanna Long | Missouri University of Science and Technology (original) (raw)
Papers by Suzanna Long
The objective of this chapter is to create a methodology to model the emergent behavior during a ... more The objective of this chapter is to create a methodology to model the emergent behavior during a disruption in the transportation system and that calculates economic losses due to such a disruption, and to understand how an extreme event affects the road transportation network. The chapter discusses a system dynamics approach which is used to model the transportation road infrastructure system to evaluate the different factors that render road segments inoperable and calculate economic consequences of such inoperability. System dynamics models have been integrated with business process simulation model to evaluate, design, and optimize the business process. The chapter also explains how different factors affect the road capacity. After identifying the various factors affecting the available road capacity, a causal loop diagram (CLD) is created to visually represent the causes leading to a change in the available road capacity and the effects on travel costs when the available road capacity changes
Engineering management journal, Dec 1, 2013
ABSTRACT Engineering managers are managers who have an understanding of both the technical and bu... more ABSTRACT Engineering managers are managers who have an understanding of both the technical and business aspects of organizations; however, the success of an engineering manager depends on being knowledgeable in both the business and technical functions of an organization. Many lead teams of their peers which are purely technical. There is a perception that engineers lack the people skills that are needed to be effective communicators, conflict resolvers, and leaders. Defensive routines are actions implemented as a result of being in an embarrassing or threatening situation. The goal of this research is to determine if defensive routines are more prevalent in engineering managers or non-engineering managers. The analysis was performed through a case study approach using a pre-determined situation. The results show that defensive routines are not more common in engineering managers than in non-engineering managers.
Data Science Journal, Jan 7, 2016
The majority of restoration strategies in the wake of large-scale disasters have focused on short... more The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium-to long-term restoration strategies to reconnect urban areas to national supply chain interdependent critical infrastructure systems (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection of SCICI data is necessary. The aim of this paper is to review what data are required for model construction, the accessibility of these data, and their integration with each other. While a review of publically available data reveals a dearth of real-time data to assist modeling longterm recovery following an extreme event, a significant amount of static data does exist and these data can be used to model the complex interdependencies needed. For the sake of illustration, a particular SCICI (transportation) is used to highlight the challenges of determining the interdependencies and creating models capable of describing the complexity of an urban environment with the data publically available. Integration of such data as is derived from public domain sources is readily achieved in a geospatial environment, after all geospatial infrastructure data are the most abundant data source and while significant quantities of data can be acquired through public sources, a significant effort is still required to gather, develop, and integrate these data from multiple sources to build a complete model. Therefore, while continued availability of high quality, public information is essential for modeling efforts in academic as well as government communities, a more streamlined approach to a real-time acquisition and integration of these data is essential.
Frontiers of Engineering Management, Dec 19, 2018
Maritime shipping is considered the most efficient, low-cost means for transporting large quantit... more Maritime shipping is considered the most efficient, low-cost means for transporting large quantities of freight over significant distances. However, this process also causes negative environmental and societal impacts. Therefore, environmental sustainability is a pressing issue for maritime shipping management, given the interest in addressing important issues that affect the safety, security, and air and water quality as part of the efficient movement of freight throughout the coasts and waterways and associated port facilities worldwide. In-depth studies of maritime transportation systems (MTS) can be used to identify key environmental impact indicators within the transportation system. This paper develops a tool for decision making in complex environments; this tool will quantify and rank preferred environmental impact indicators within a MTS. Such a model will help decision-makers to achieve the goals of improved environmental sustainability. The model will also provide environmental policymakers in the shipping industry with an analytical tool that can evaluate tradeoffs within the system and identify possible alternatives to mitigate detrimental effects on the environment.
In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term rec... more In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term recovery and medium-to long-term restoration are needed. While considerable effort is geared to developing strategies for the former two options, little comprehensive guidance exists on the latter. However, medium-to long-term restoration has a significant effect on local, regional and national economies and is essential to community vitality. In part, the deficit of robust strategies can be linked to the complexity in the data acquisition and limited methodologies to understand the interconnectedness of the relevant systems elements. This research utilizes infrastructure data for Supply Chain Interdependent Critical Infrastructure Systems (SCICI) such as transportation, energy, communications, or water, obtained or derived through open sources (such as The National Map of the U.S. Geological Survey) to identify, understand, and map the interdependencies between these system elements to enable restoration planning. Specifically, internal geographical relationships (herein called the 'geographical interdependency') of SCICI elements are mapped. These interdependencies highlight the stress points on the larger SCICI where failures occur and are not included in current built environment models. The mapping of these interdependencies is a key step forward in attempts to optimally restore an urban center's supply chain in the wake of an extreme event.
Current flood management models are often hampered by the lack of robust predictive analytics, as... more Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural test site are presented subject to available data and input from key stakeholders. The transportation safety or disaster planner can use these results to begin integrating deep learning methods in their planning strategies based on regionspecific geospatial data and information.
Knowledge management systems are critical for capturing, retaining, and communicating results fro... more Knowledge management systems are critical for capturing, retaining, and communicating results from projects and knowledge of staff, preventing knowledge drain, and providing a basis for lessons learned type training. This research focuses on the development of a knowledge management system using Social Network Analysis (SNA) to improve on methods to organize, disseminate, and share knowledge for a large government healthcare organization, enhancing their process improvement initiative. To better enable this knowledge sharing, a survey instrument consisting of a narrative interview protocol and follow-on questionnaire is established to collect data from key stakeholders. The goal of the survey instrument is to interview key players using the narrative interview protocol through focus groups and one-on-ones, and then deploy an extended questionnaire. The narrative protocol is first used with focus groups formed from the early adopters of process improvement methods to understand how stakeholders viewed and implemented changes to their work environment. Using these results, a Likert-style version of the questionnaire is provided to all users. Based on the survey data, social network mapping is performed using a SNA tool, and analysis was performed relevant to basic network properties, characteristics of relations, and other relevant network features. The goal of this research is to identify key players, document how information is shared within the organization, recommend methods of information sharing to retain knowledge, and measure the impact of the improvements
Transportation networks are vital elements in modern economic and social systems. These networks ... more Transportation networks are vital elements in modern economic and social systems. These networks are vulnerable to damage from the impact of extreme events. Such damage adversely affects network connectivity, as well as delaying relief and restoration operations. To better plan how to restore these infrastructure elements, this study develops network-analysis and graph theory based tools using real-world data for network restoration planning. Models are developed that identify the influential nodes to map the interdependencies between different modes of transportation and determine which network components contribute most to its connectivity. An efficient node ranking method is also proposed to aid in the restoration of the critical infrastructure network in the aftermath of a disaster. Weighting factors are used to rank and map influential nodes for prioritizing respective network regions by their actual use. This approach is applied to publicly available real-world data for St. Louis, Missouri
It is essential to develop efficient transportation strategies for the distribution of vital supp... more It is essential to develop efficient transportation strategies for the distribution of vital supplies in the aftermath of wide-scale extreme events. While most major cities have importation and distribution plans in place for their communities, the implementation efficacy of these plans is diminished once the transportation network is disrupted following a disaster. This research develops a multi-objective decision model that minimizes cost while maximizing proximity. GIS-style visualization tools are used to create planning scenarios. The methodology also integrates elements from complexity science to control emergent behaviors and cascade failures resulting from interdependent systems failures
The objective of this chapter is to create a methodology to model the emergent behavior during a ... more The objective of this chapter is to create a methodology to model the emergent behavior during a disruption in the transportation system and that calculates economic losses due to such a disruption, and to understand how an extreme event affects the road transportation network. The chapter discusses a system dynamics approach which is used to model the transportation road infrastructure system to evaluate the different factors that render road segments inoperable and calculate economic consequences of such inoperability. System dynamics models have been integrated with business process simulation model to evaluate, design, and optimize the business process. The chapter also explains how different factors affect the road capacity. After identifying the various factors affecting the available road capacity, a causal loop diagram (CLD) is created to visually represent the causes leading to a change in the available road capacity and the effects on travel costs when the available road capacity changes
Engineering management journal, Dec 1, 2013
ABSTRACT Engineering managers are managers who have an understanding of both the technical and bu... more ABSTRACT Engineering managers are managers who have an understanding of both the technical and business aspects of organizations; however, the success of an engineering manager depends on being knowledgeable in both the business and technical functions of an organization. Many lead teams of their peers which are purely technical. There is a perception that engineers lack the people skills that are needed to be effective communicators, conflict resolvers, and leaders. Defensive routines are actions implemented as a result of being in an embarrassing or threatening situation. The goal of this research is to determine if defensive routines are more prevalent in engineering managers or non-engineering managers. The analysis was performed through a case study approach using a pre-determined situation. The results show that defensive routines are not more common in engineering managers than in non-engineering managers.
Data Science Journal, Jan 7, 2016
The majority of restoration strategies in the wake of large-scale disasters have focused on short... more The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium-to long-term restoration strategies to reconnect urban areas to national supply chain interdependent critical infrastructure systems (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection of SCICI data is necessary. The aim of this paper is to review what data are required for model construction, the accessibility of these data, and their integration with each other. While a review of publically available data reveals a dearth of real-time data to assist modeling longterm recovery following an extreme event, a significant amount of static data does exist and these data can be used to model the complex interdependencies needed. For the sake of illustration, a particular SCICI (transportation) is used to highlight the challenges of determining the interdependencies and creating models capable of describing the complexity of an urban environment with the data publically available. Integration of such data as is derived from public domain sources is readily achieved in a geospatial environment, after all geospatial infrastructure data are the most abundant data source and while significant quantities of data can be acquired through public sources, a significant effort is still required to gather, develop, and integrate these data from multiple sources to build a complete model. Therefore, while continued availability of high quality, public information is essential for modeling efforts in academic as well as government communities, a more streamlined approach to a real-time acquisition and integration of these data is essential.
Frontiers of Engineering Management, Dec 19, 2018
Maritime shipping is considered the most efficient, low-cost means for transporting large quantit... more Maritime shipping is considered the most efficient, low-cost means for transporting large quantities of freight over significant distances. However, this process also causes negative environmental and societal impacts. Therefore, environmental sustainability is a pressing issue for maritime shipping management, given the interest in addressing important issues that affect the safety, security, and air and water quality as part of the efficient movement of freight throughout the coasts and waterways and associated port facilities worldwide. In-depth studies of maritime transportation systems (MTS) can be used to identify key environmental impact indicators within the transportation system. This paper develops a tool for decision making in complex environments; this tool will quantify and rank preferred environmental impact indicators within a MTS. Such a model will help decision-makers to achieve the goals of improved environmental sustainability. The model will also provide environmental policymakers in the shipping industry with an analytical tool that can evaluate tradeoffs within the system and identify possible alternatives to mitigate detrimental effects on the environment.
In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term rec... more In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term recovery and medium-to long-term restoration are needed. While considerable effort is geared to developing strategies for the former two options, little comprehensive guidance exists on the latter. However, medium-to long-term restoration has a significant effect on local, regional and national economies and is essential to community vitality. In part, the deficit of robust strategies can be linked to the complexity in the data acquisition and limited methodologies to understand the interconnectedness of the relevant systems elements. This research utilizes infrastructure data for Supply Chain Interdependent Critical Infrastructure Systems (SCICI) such as transportation, energy, communications, or water, obtained or derived through open sources (such as The National Map of the U.S. Geological Survey) to identify, understand, and map the interdependencies between these system elements to enable restoration planning. Specifically, internal geographical relationships (herein called the 'geographical interdependency') of SCICI elements are mapped. These interdependencies highlight the stress points on the larger SCICI where failures occur and are not included in current built environment models. The mapping of these interdependencies is a key step forward in attempts to optimally restore an urban center's supply chain in the wake of an extreme event.
Current flood management models are often hampered by the lack of robust predictive analytics, as... more Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural test site are presented subject to available data and input from key stakeholders. The transportation safety or disaster planner can use these results to begin integrating deep learning methods in their planning strategies based on regionspecific geospatial data and information.
Knowledge management systems are critical for capturing, retaining, and communicating results fro... more Knowledge management systems are critical for capturing, retaining, and communicating results from projects and knowledge of staff, preventing knowledge drain, and providing a basis for lessons learned type training. This research focuses on the development of a knowledge management system using Social Network Analysis (SNA) to improve on methods to organize, disseminate, and share knowledge for a large government healthcare organization, enhancing their process improvement initiative. To better enable this knowledge sharing, a survey instrument consisting of a narrative interview protocol and follow-on questionnaire is established to collect data from key stakeholders. The goal of the survey instrument is to interview key players using the narrative interview protocol through focus groups and one-on-ones, and then deploy an extended questionnaire. The narrative protocol is first used with focus groups formed from the early adopters of process improvement methods to understand how stakeholders viewed and implemented changes to their work environment. Using these results, a Likert-style version of the questionnaire is provided to all users. Based on the survey data, social network mapping is performed using a SNA tool, and analysis was performed relevant to basic network properties, characteristics of relations, and other relevant network features. The goal of this research is to identify key players, document how information is shared within the organization, recommend methods of information sharing to retain knowledge, and measure the impact of the improvements
Transportation networks are vital elements in modern economic and social systems. These networks ... more Transportation networks are vital elements in modern economic and social systems. These networks are vulnerable to damage from the impact of extreme events. Such damage adversely affects network connectivity, as well as delaying relief and restoration operations. To better plan how to restore these infrastructure elements, this study develops network-analysis and graph theory based tools using real-world data for network restoration planning. Models are developed that identify the influential nodes to map the interdependencies between different modes of transportation and determine which network components contribute most to its connectivity. An efficient node ranking method is also proposed to aid in the restoration of the critical infrastructure network in the aftermath of a disaster. Weighting factors are used to rank and map influential nodes for prioritizing respective network regions by their actual use. This approach is applied to publicly available real-world data for St. Louis, Missouri
It is essential to develop efficient transportation strategies for the distribution of vital supp... more It is essential to develop efficient transportation strategies for the distribution of vital supplies in the aftermath of wide-scale extreme events. While most major cities have importation and distribution plans in place for their communities, the implementation efficacy of these plans is diminished once the transportation network is disrupted following a disaster. This research develops a multi-objective decision model that minimizes cost while maximizing proximity. GIS-style visualization tools are used to create planning scenarios. The methodology also integrates elements from complexity science to control emergent behaviors and cascade failures resulting from interdependent systems failures