Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</jats:p

Tools and methods in participatory modeling: Selecting the right tool for the job

© 2018 Elsevier Ltd Various tools and methods are used in participatory modelling, at different stages of the process and for different purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We offer a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justification or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant effect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based on expert opinion and a survey of modelers engaged in participatory processes, we offer practical guidelines to improve decisions about method selection at different stages of the participatory modeling process

Effective modeling for integrated water resource management: a guide to contextual practices by phases and steps and future opportunities

The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow, and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors’ collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and uncertainty management. © 2019 Elsevier Lt

A military fleet mix problem for high-valued defense assets: A simulation-based optimization approach

The military fleet mix problem is traditionally used when several modernization operations are required to transition a force from an outdated fleet to a more modern one over a planning period. The main objective of such a transition is to enhance the capability of a military fleet to deploy its forces for missions at any moment by continually improving its operational readiness with minimum cost and time. The complexity of the problem is significantly increased with the increase in the number of assets to be modernized, sub-divisions (Brigades) and the length of the planning period. In this paper, to effectively address the fleet mix problem in the military context, a simulation–optimization approach is introduced with the aim of providing decision-makers with a fleet modernization schedule that enables the military to achieve maximum deployment of its available assets/forces while minimizing the total cost and full deployment time. The proposed approach uses a combination of (1) an enhanced genetic algorithm (GA) to generate initial solutions, explore and exploit the generated solutions till achieving the optimal one, and (2) a capability simulation model to evaluate each solution and determine its quality. Furthermore, the proposed GA comprises several enhanced components to further improve its performance and hence find the optimal solution in lesser computational time. (a) A heuristic repairing method, to provide feasible solutions through repairing a certain number of solutions from the initial population which can significantly guide GA search towards (near-)optimal solutions, (b) a new solution representation, that better represents the fleet schedule decision variables and characteristics, (c) improved designs of crossover and mutations, that can help GA to reach optimum solutions in a faster manner, and (d) new sorting and selection functions, that use many objectives values to allow the better solutions to survive through generations, are all examples of such enhanced GA's components. The proposed approach has been validated by solving a case study that addresses recent fleet modernization strategies of the Australian Army to recapitalize its forces over the next decade and in a continual process. Two scenarios of the case study with different scales have been used. The experimental results show that the proposed approach can effectively provide efficient modernization fleet schedules for both scenarios which allow the maximum deployment with the minimum cost and time. Compared with traditional GA and Differential Evolution (DE), the proposed approach yields 2.37% and 7.17% more deployability, and 99.59% and 98.81% less total cost than DE and GA, respectively. A surrogate model is also presented to replace the computationally expensive simulation model for fitness evaluation. The results show that the surrogate model can significantly reduce the computational time but its accuracy for expecting the optimal objectives still need more enhancements

A Discrete Differential Evolution Algorithm for a Military Fleet Modernization Problem

Differential evolution has a long track record of successfully solving optimization problems in continuous domain due it its powerful Euclidean distance-based learning concept. Although this affects its suitability for solving several problems with permutation variables, several studies show that it can be applicable for effectively solving permutation-based problems. In this paper, an improved design of differential evolution is introduced to solve a military fleet modernization problem with discrete parameters. In this problem, several modernization oper-ations are required to transition a military force from an outdated fleet to a more modern one with the objective of maximizing the force's deployment at the minimum cost over a pre-determined planning period. The proposed differential evolution incorporates a new solution representation, a proposed repairing heuristic method, a modified mutation operator and mapping method for efficiently tackling the discrete characteristics of the targeted problem and is coupled with a simulation model to evaluate the fitness of the generated solutions. To judge its performance, the proposed algorithm has been implemented to solve a case study that addresses recent fleet modernization strategies of the Australian Army to recapitalize its forces over the next decade and in a continual process. The experimental results show that the proposed algorithm can provide more efficient fleet modernization schedules which are 29.32% and 51.43% better than those obtained by other two comparative algorithms

A decision support methodology to support military asset and resource planning

This paper presents a decision-support methodology to support the development and assessment of military asset and resource strategies. The methodology is built around a system dynamics model that allows users to examine the performance of a strategy over time. The novelty of the model lies in its flexibility and ability to address questions about asset planning from both holistic and lifecycle viewpoints. From the user perspective, the decision-support methodology is structured around three phases: (1) Design an asset management strategy, (2) Evaluate strategy using system dynamics simulation model, and (3) Generate performance indicators and analyse results. The methodology is developed and used in a real case study to support the modernisation of the Australian Defence Force. This paper demonstrates that system dynamics offers useful methods to study the dynamics of supply-demand, and support the development of systemic asset and resource management strategies

A joint problem of strategic workforce planning and fleet renewal: With an application in defense

Reductions in defense expenditure require holistic and coordinated planning of two critical and interconnected defense capabilities, namely a fleet of assets and the workforce required. In this paper, we model and solve a joint problem of strategic workforce planning and fleet renewal in a military context. The joint problem studied involves addressing a trade-off among several costs (e.g., workforce, maintenance, operating, etc.) and operational readiness (availability) of the fleet. To make such trade-offs, the decisions associated with workforce planning (i.e., the recruitment and the career progression of the workforce) and fleet renewal (i.e., the timing of asset replacements) strategies have to be simultaneously considered and optimized. We develop a simulation-optimization approach by coupling a system dynamics (SD) simulation model and a genetic algorithm (GA) to solve the joint problem. In the developed approach, the GA generates candidate workforce planning and fleet renewal strategies to find the best joint strategy. Then, the candidate workforce planning and renewal strategies are passed to the SD model which simulates both the career progression of the workforce and the life-cycle of assets to evaluate the total cost. We illustrate the applicability and effectiveness of the joint model on a realistic case study motivated by the recent modernization efforts of the Royal Australian Navy. The results obtained indicate that this approach leads to a considerable cost reduction and identifies the causes of inferior performance. We also test the robustness of the optimized strategies under uncertainty by sensitivity and scenario discovery analyses to infer further insights

A bottom-up approach to improve local-scale understanding and decision making in responding to climate change

To forecast the energy consumtion and carbon emissions in China, this paper established an input-output model with 17 sectors on the macroeconomy level, and an agentbased model on the microeconomy level simulating firms’ innovations in each sector. Results show that due to the uncertainty of innovation, the peak years of energy and emission are also uncertain. The energy peak year will subject to a normal distribution from 2025 to 2036; while the distribution of emission peak year is also identified as a normal distribution from 2024 to 2033. The year with the maximum probability for energy peak will be 2031 with the probability of 23.57%; and 2029 will be the year with the maximum probality 33.51% for emission peak. Taking the average of 50 simulations, it is indicated that the energy peak will be 5146Mtce in 2029 with a decline by 2050 to 4086Mtce, and the emission peak will be 2.7GtC in 2029 with a decline by 2050 to 2.05GtC

The future of military medical evacuation: literature analysis focused on the potential adoption of emerging technologies and advanced decision-analysis techniques

A fundamental component of any military medical support system is medical evacuation. The main goal of evacuation is to reduce mortality among critically injured combat casualties. To achieve this goal, several decision problems including, the location of medical treatment facilities, relocation, dispatching, and routing have to be effective across all levels (tactical, operational, and strategic). This study concentrates on the three key types of medical evacuation (MEDEVAC) systems—forward, tactical, and strategic—and the related decision problems. Even though, over the last few years, some review papers have discussed the different topics of MEDEVAC systems (e.g., the evolution of MEDEVAC, evacuation timelines, and types of injuries), no research has been conducted on the full range (i.e., total care pathway) of MEDEVAC systems and the adoption of emerging technologies to improve future MEDEVAC. In this paper, a systematic review of the literature is described, including the decision problems involved in the total military medical evacuation process. This paper also reviews forecast challenges of future MEDEVAC and potential emerging technologies, concepts, and advanced decision-analysis methods to tackle these challenges. In future MEDEVAC processes, emerging technologies and concepts will be important to support improved medical capability; however, military planners will also need to adopt advanced decision-support techniques to efficiently employ these technologies

Developing shared qualitative models for complex systems

Understanding complex systems is essential to ensure their conservation and effective management. Models commonly support understanding of complex ecological systems and, by extension, their conservation. Modeling, however, is largely a social process constrained by individuals’ mental models (i.e., a small-scale internal model of how a part of the world works based on knowledge, experience, values, beliefs, and assumptions) and system complexity. To account for both system complexity and the diversity of knowledge of complex systems, we devised a novel way to develop a shared qualitative complex system model. We disaggregated a system (carbonate coral reefs) into smaller subsystem modules that each represented a functioning unit, about which an individual is likely to have more comprehensive knowledge. This modular approach allowed us to elicit an individual mental model of a defined subsystem for which the individuals had a higher level of confidence in their knowledge of the relationships between variables. The challenge then was to bring these subsystem models together to form a complete, shared model of the entire system, which we attempted through 4 phases: develop the system framework and subsystem modules; develop the individual mental model elicitation methods; elicit the mental models; and identify and isolate differences for exploration and identify similarities to cocreate a shared qualitative model. The shared qualitative model provides opportunities to develop a quantitative model to understand and predict complex system change