Planning Decision Support Research Articles

Land Use and Land Cover Changes and Prediction Based on Multi-Scenario Simulation: A Case Study of Qishan County, China

Research on land use change is helpful to better understand the processes and mechanisms of land use changes and provide a decision base for reasonable land development. However, studies on LUCC were mainly conducted for megalopolises and urban agglomerations in China, but there is a gap in the scholarly community when it comes to shrinking small cities where the population decreased sharply under the influence of the urban expansion of megacities. Hence, it is necessary to investigate the evolution rule of land use in these regions. This study takes Qishan County in Shanxi Province as the research subject and analyzes the land use change over the last 20 years with remote sensing technology. Comparing the two LUCC models of the CA-Markov Model and the LCM Model, an optimal model is used to predict and simulate land use change under three potential scenarios in 2030. The conclusions are stated as follows: (1) From 2000 to 2020, the cultivated land area increased originally and subsequently decreased, and forest land continued to decrease at a progressively slower speed. In contrast, the urban land area expanded significantly. (2) The comprehensive dynamic change in water land is the most significant, indicating that this is an unstable land resource in the region and more attention should be given to this matter. (3) The scenario of water area protection indicates that the inhibition of the transition of water areas can protect their vulnerable ecological environment without negatively impacting economic development. Furthermore, the ongoing focus on economic development in the region is related to the rapid disappearance of cultivated land, which is not an optimistic perspective for the area’s ecosystem. The results of this study implied land transition features and mechanisms in Qishan County, providing novel insights for decision support for county-level land use planning.

Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

Sustainable environmental management often involves long-term time horizons and multiple conflicting objectives and, by nature, is affected by different sources of uncertainty. Many sources of uncertainty, such as climate change or government policies, cannot be addressed using probabilistic models, and, therefore, they can be seen to contain deep uncertainty. In this setting, the variety of possible future states is represented as a set of scenarios lacking any information about the likelihood of occurring. Integrating deep uncertainty into multiobjective decision support increases complexity, calling for the elaboration of appropriate methods and tools. This paper proposes a novel interactive multi-scenario multiobjective approach to support decision-making and trade-off analysis in sustainable forest landscape planning under multiple sources of uncertainty. It includes new preference simulation models aimed at reducing the decision-maker’s cognitive load and supporting the preference elicitation process. The proposed approach is applied in a case study of long-term forest landscape planning with four sustainability objectives in 12 scenarios and a forestry expert as the decision-maker. The approach is demonstrated to be efficient in exploring trade-offs in different scenarios, helping the expert gain deep insights into the problem, understand the consequences of alternative strategies, and find the most preferred robust strategy.

The ASSISTANT project: AI for high level decisions in manufacturing

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments.

Prediction of network public opinion features in urban planning based on geographical case-based reasoning

ABSTRACT As a significant part of sustainable urban development proposed by the United Nations, urban planning is related to the ecological environment and transportation, especially affecting quality of life and social well-being. In the process of urban planning, the public express their opinions on open network platforms, resulting in large quantities of network public opinion data, which has important implications for evaluating urban planning. Based on the idea of geographical case-based reasoning (CBR), this paper constructs an expression framework for urban planning cases in the form of a ‘case problem–case attribute–case result’ triad. On this basis, this paper proposes a similarity calculation method of urban planning cases that integrates case attribute. Finally, based on an improvement to the traditional k-nearest neighbors method, the proposed public opinion feature calculation model considers similarity weights, which allow us to predict network public opinion features, including viewpoint-level emotional tendency and concerned groups of urban planning cases. The experimental result shows similarity weights (SWs) model could effectively improve the prediction accuracy, where the average MIC-F1 score reached more than 74%. Based on CBR, the proposed method can predict the development trends of public opinion in future planning cases, and provide scientific and reasonable decision support for urban planning.

Developing a spatial information system of biomass potential from crop residues over India: A decision support for planning and establishment of biofuel/biomass power plant

The study demonstrates effective utilization of geospatial technology to amalgamate crop statistics, satellite derived crop maps and productivity proxies to generate spatial maps of biomass potential from crop residues over a diverse country like India. District level crop production statistics were used to estimate gross biomass potential (GBP) of the selected crops (rice, wheat, sugarcane and cotton). Multi-temporal satellite data were used to generate crop maps. District level GBP was disaggregated at 1 km grid using satellite derived crop fraction and seasonal Gross Primary Production. The GBP maps were further converted to surplus biomass potential (SBP) followed by bioenergy potential using district specific surplus factors (ground survey) and crop specific calorific values respectively. These maps could capture the spatial variations of the available biomass resources and prospective zones to prioritize its utilization. The study found the presence of rice-wheat based SBP over Punjab and Haryana (>0.30 Ktonnes km−2), eastern Uttar Pradesh (0.15–0.30 Ktonnes km−2); sugarcane-wheat based SBP over western Uttar Pradesh (>0.25 Ktonnes km−2); cotton-wheat based SBP over the southern Haryana and western Punjab (>0.20 Ktonnes km−2); and sugarcane-cotton based SBP over central India and Gujarat (>0.20 Ktonnes km−2). Geospatial maps of crop residue biomass along with other thematic layers were hosted in a geoportal (BHUVAN-JAIVOORJA) with an online query module to assess the amount and types of biomass, land resources, logistics support based on user defined point of interest and fetch/buffer area towards planning/establishment of tailor-made biofuel/biomass plants.

Simulation-based optimisation approach to improve emergency department resource planning: A case study of Tunisian hospital.

The emergency department (ED) is a gateway to hospitals and is in the centre of hospital management efforts. It is often saturated by a continuous flow of patients, which causes excessive patient waiting time. This study integrates simulations with optimisation to design planning decision support for an ED. We considered all the processes of the ED, from triage to bed assignment. This study's main objective was to determine the optimal number of doctors, nurses, and beds required to schedule patients with different acuity levels to minimise both the total patient waiting time and the patient average length of stay and balance the resource utilisation rates. The problem is also characterised by multiple uncertainties, such as the patient arrival rate and service times in each stage of the process. We first propose a stochastic mixed-integer programing model that is solved using the sample average approximation approach. The resulting resource sizing is then evaluated using a discrete-event simulation model by comparing different patient scheduling rules. Numerical experiments highlight the performance of the proposed approach using data from a Tunisian ED hospital.

Analysis of the trade-off between hydroelectricity generation and ecological protection from the perspective of eco-efficiency in Southwest China

Driven by the global net-zero carbon emission goal, even though the impact of hydropower on river ecosystems cannot be ignored, hydropower scale and intensity will be expanded in Southwest China (SC). Therefore, the trade-off between hydroelectricity generation and ecological protection remains an issue requiring urgent attention at the moment. This paper proposes an eco-efficiency (EE) indicator system of hydropower adopting the water consumption of hydropower product (PWC), labor, and capital as inputs and the power generation, ecological flow deviation (EFD) as outputs and then explores the factors influencing the EE value of hydropower in SC. The results indicate that (1) The EE incorporates the impact of each plant on fish, provides decision support for hydropower development planning and serves as the foundation for eco-dispatch operations. (2) Hydropower generation affects fish reproduction in SC, and the EE value of the Wu River is relatively low. (3) The scale of power generation and degree of fish protection are two significant factors that can increase the EE value of hydropower. To compensate for plant inadequacies in these two aspects, equivalent changes in hydropower planning and eco-dispatch operations should be implemented.

A SUDS Planning Decision Support Tool to Maximize Ecosystem Services

In the past years, alternative drainage approaches have emerged, such as Sustainable Urban Drainage Systems (SUDS), to prevent run-off and flooding impacts on the most vulnerable zones of the cities. These systems not only provide the benefit of water regulation but also promote other types of ecosystem services. Several studies have developed optimization tools to assist SUDS selection, location, and design. However, they do not consider a comprehensive set of ecosystem services (e.g., provision, regulation, cultural, and support services). This research proposes a flexible and adaptable methodology to incorporate SUDS in different stages of urban projects using a multi-objective optimization technique to minimize run-off, maximize ecosystem services and minimize cost. The methodology comprises four phases: (1) the preliminary analysis of ecosystem services potentially generated by each SUDS type, (2) the priority and opportunity index quantification, (3) the physical feasibility analysis, and (4) the multi-objective optimization tool implementation. The methodology was successfully applied to three different urban areas of Bogotá city (Colombia). Results evidence that the interaction of the budget constraints and the available area restrict the potential benefits of SUDS implementation. These results are helpful to support different urban planning stages.

Planning a circular economy system for electric vehicles using network simulation

The fast-growing market for electric vehicles (EVs) offers great potential for reducing the carbon footprint of the transportation sector but is heavily reliant on electric motors and batteries, which are in turn dependent on the availability of critical materials, e.g., rare earth elements and lithium. Such critical materials are subject to substantial supply chain risk, indicating a potential lack of source materials to meet EV production. To ameliorate this supply chain uncertainty that may compromise the economic competitiveness of EVs, it is essential to establish an end-of-life (EoL) infrastructure that is focused on closing the material loops within the EV life cycle, thus achieving a circular economy. However, there is a lack of research devoted to enabling technologies and supporting tools to design the reverse logistic chain for EVs that considers EoL processing of components in a holistic manner. To close this knowledge gap, this study develops a stochastic activity network to simulate an EV renewal system (EVRS) that has the goal of rebuilding used EVs and extracting maximum value from EoL EV components and materials. Based on the simulation outputs, the process flow and economic performance of the proposed system are enhanced through operation optimization and pricing optimization. The results indicate the technical viability of the EVRS from an operational standpoint and suggest its promising economic potential in industrial practice. As industries around the globe transition to a circular economy, the proposed network simulation framework has enormous potential to be adapted to other products and scenarios to provide decision support in system planning.

Comparative analysis of groundwater potentiality zone using fuzzy AHP, frequency ratio and Bayesian weights of evidence methods

Groundwater resource management in the urban area is one of the important aspects because of growing population demand and having inadequate water supply. So, proper information is needed to manage the future urban planning for such kinds of areas. This study emphasizes groundwater potentiality zone (GPZ) assessment in the Asansol urban agglomeration (AUA) region, West Bengal, India. For this purpose, we have incorporated eight conditioning factors namely LULC, lithology, slope, elevation, rainfall, drainage density, lineaments density, and soil map using conventional and remote sensing data in GIS software. All these conditioning factors have been reclassified in ArcGIS and processed by the fuzzy analytical hierarchy process (FAHP), frequency ratio (FR), and Bayesian weights of evidence (BWOE) statistical methods. Then, the groundwater potential index has been formulated, and finally, GPZ maps are generated based on of selected three models. The result shows that very high area of GPZ, e.g. 9.13%, 11.62% and 7.43% are under BWOE, FR and FAHP models, respectively. The receiver operating characteristic validation curves show that FR method (AUC = 96.4%) is well obtained for GPZ in comparison with both BWOE (AUC = 83.8%) and FAHP methods (AUC = 82.9%). Therefore, this statistical method is highly recommended for the study of groundwater potential assessment and this outcome is very suitable for the groundwater resources management in future land use planning. Precautionary works in low potential areas should be given priority for long-term planning. Thus, this study can be considered as a good document for decision support in water exploitation planning and sustainable groundwater management in AUA region.

Goal Programming and Mathematical Modelling for Developing a Capacity Planning Decision Support System-Based Framework in Higher Education Institutions

Achieving the Saudi Kingdom’s vision 2030 in the higher education sector requires higher education institutions to make a significant simultaneous change in their current practices. This encompasses the transitioning of government-funded educational institutions to be financially independent. Therefore, a prompt, agile transition is required while maintaining a positive socioeconomic impact, entrepreneurship and innovation, and high-quality education. This necessitates the transition to lean processes and the review of current practices. One of the most vital processes in educational institutions is student admission/enrollment capacity planning. This study puts forward a capacity planning decision support system (DSS)-based framework for university student enrollment. The framework was applied to the case of KAU, where current practice and challenges are presented, and from which data were collected. A top-down/bottom-up approach was followed and applied using the goal programming technique and a developed mathematical model, respectively. Results show that the proposed framework effectively affects student admission/enrollment capacity planning on strategic and operational levels. Moreover, it can be used in other planning aspects of higher education in universities, such as human resources planning, teaching load planning, faculty-to-student ratios, accreditation, quality requirements, lab capacity planning, equipment/teaching aids procurement, and financial planning, to mention a few. The implications of this study include assisting decision-makers in higher education institutions in matching their admission/enrollment capacity of student numbers between the macro-strategic and the micro-operational level.

AWR: Anticipate, Withstand, and Recover Resilience Metric for Operational and Planning Decision Support in Electric Distribution System

With the increasing number of catastrophic weather events and resulting disruption in the energy supply to essential loads, the distribution grid operators’ focus has shifted from reliability to resiliency against high impact, low-frequency events. Given the enhanced automation to enable the smarter grid, there are several assets/resources at the disposal of electric utilities to enhances resiliency. However, with a lack of comprehensive resilience tools for informed operational decisions and planning, utilities face a challenge in investing and prioritizing operational control actions for resiliency. The distribution system resilience is also highly dependent on system attributes, including network, control, generating resources, location of loads and resources, as well as the progression of an extreme event. In this work, we present a novel multi-stage resilience measure called the Anticipate-Withstand-Recover (AWR) metrics. The AWR metrics are based on integrating relevant ‘system characteristics based factors’, before, during, and after the extreme event. The developed methodology utilizes a pragmatic and flexible approach by adopting concepts from the national emergency preparedness paradigm, proactive and reactive controls of grid assets, graph theory with system and component constraints, and multi-criteria decision-making process. The proposed metrics are applied to provide decision support for a) the operational resilience and b) planning investments, and validated for a real system in Alaska during the entirety of the event progression.

Intelligent Decision Support in Beds Management and Hospital Planning

Worldwide, due to the constant overcrowding experienced in hospitals, hospital beds are one of the most needed resources, proving to be an extremely important feature in hospitalization planning and management, since the main purpose is to optimize their occupancy rate. This study aims to predict the future flow of patients after admission to a particular inpatient specialty to allow a more assertive planning based on demographic data. All data sources were made available by the Centro Hospitalar do Tâmega e Sousa (CHTS) and are relative to a 5-year period, 2017 to 2021. From the results achieved with the Machine Learning (ML) models developed was possible to conclude that these can prove to be an asset for the hospital, since being known the flow of patients allows a more informed and careful management of the management of beds.

Technology-enabled, Rapid-Response Fresh Food Supply Chains: A Paradigm and Case Study

Terra-fresh stands for TEchnology-Enabled, Rapid-Response FRESH supply chains. It is an integrated planning, analytics, and coordination environment that seeks to exploit the new technological realities of the supply chain of fresh agricultural. It constructs short, market-oriented supply chains for fresh fruits and vegetables (FFV) based on the effective utilization of market intelligence, information technology, negotiation, grower collaboration, coordination, and planning decision support tools. Terra-fresh supply chains are resilient through rapid reconfigurability based on data and status estimation of the underlying physical supply chain and environmental conditions. This paper gives an overview of the different components that conform our vision of the integrated environment and presents a case study that illustrates the formation of coalitions of small growers to capture a market opportunity using some of the decision support tools encapsulated in Terra-fresh. Some of the underlying techniques for the formation of these coalitions include mathematical programming, forecasting and statistical monitoring.

Towards a unifying domain model of construction safety, health and well-being: SafeConDM

Specific occupational construction safety, health, and well-being related knowledge and information are scattered and fragmented. Despite technological advancements of information and knowledge management, a link between safety management and information models is still missing. In this paper we present first steps towards a unifying formal (logic-based) domain model of construction safety, called SafeConDM, that consists of: (1) a semantically rich ontology of hazard, safety concepts, and concept relationships that builds on, and integrates with, existing construction safety ontologies and building information models; (2) a set of first-order if-then rules linking construction site states with the potential for specific hazards to occur that we define in a novel way using spatial artefacts. We present a prototype software tool, based on our ASP4BIM tool that implements SafeConDM for construction hazard analysis and safe construction planning decision support, and empirically evaluate our tool on three real-world construction building models.

Spatial optimization for urban green space (UGS) planning support using a heuristic approach

Urban green space (UGS) has rich ecological and social benefits, and is closely related to the well-being of residents. Its spatial equity is a major concern for both residents and planners. However, effective decision-making support tools for UGS planning are still lacking. This study therefore proposes a UGS spatial optimization method that takes spatial equity and conversion cost into account to optimize the UGS layout in Xuzhou, China. The essence of the purposed optimization method is to constantly adjust the conversion probability of alternative land parcels into UGS to obtain a UGS layout that minimizes the overall conversion cost under the constraints of per capita UGS and spatial equity requirements. It is suggested that UGS spatial configuration is highly complex and that basic spatial units, population distribution, spatial equity, and the costs of conversion all need to be considered when planning UGS, which moreover needs to be optimized based on various spatial analysis. UGS spatial optimization should be integrated with planning regulated indicators such as per capita UGS and spatial equity, the implementation of which should be feasible based on the current UGS layout and land use situation. The proposed UGS spatial optimization method can generate optimized UGS layouts that meet controlling demands of planning, lowering UGS construction costs while improving spatial utility. It can provide decision support for UGS planning and has broad application prospect.

Embedding OR modelling as decision support in health capacity planning: insights from an evaluation

ABSTRACT Literature reviews over five decades have reported the paucity of examples of OR methods being routinely used to support decision-making in health and social care. This paper presents insights from an independent evaluation of a project intended to overcome some of the barriers to implementation by establishing a “community of practice” in Kent (England). The project itself was undertaken by practitioners, and had two main aims: providing training in system dynamics modelling to analysts, and making senior managers aware of the benefits of modelling. The findings largely confirmed previous studies, but also raised issues about style of training delivery and selection of problems to be modelled. Project leaders fully understood the barriers to embedding OR modelling skills, and made considerable efforts to avoid them, but nevertheless the main barrier, pressures on people’s time, remained an obstacle. The paper concludes with general reflections and advice.

Digital Twin-Enabled Decision Support in Mission Engineering and Route Planning

This article presents a Model-Based Systems Engineering (MBSE) methodology for the development of a Digital Twin (DT) for an Unmanned Aerial System (UAS) with the ability to demonstrate route selection capability with a Mission Engineering (ME) focus. It reviews the concept of ME and integrates ME with a MBSE framework for the development of the DT. The methodology is demonstrated through a case study where the UAS is deployed for a Last Mile Delivery (LMD) mission in a military context where adversaries are present, and a route optimization module recommends an optimal route to the user based on a variety of inputs including potential damage or destruction of the UAS by adversary action. The optimization module is based on Multiple Attribute Utility Theory (MAUT) which analyzes predefined criteria which the user assessed would enable the successful conduct of the UAS mission. The article demonstrates that the methodology can execute a ME analysis for route selection to support a user’s decision-making process. The discussion section highlights the key MBSE artifacts and also highlights the benefits of the methodology which standardizes the decision-making process thereby reducing the negative impact of human factors which may deviate from the predefined criteria.

Evaluation of field visit planning heuristics during rapid needs assessment in an uncertain post-disaster environment

A Rapid Needs Assessment process is carried out immediately after the onset of a disaster to investigate the disaster’s impact on affected communities, usually through field visits. Reviewing practical humanitarian guidelines reveals that there is a great need for decision support for field visit planning in order to utilize resources more efficiently at the time of great need. Furthermore, in practice, there is a tendency to use simple methods, rather than advanced solution methodologies and software; this is due to the lack of available computational tools and resources on the ground, lack of experienced technical staff, and also the chaotic nature of the post-disaster environment. We present simple heuristic algorithms inspired by the general procedure explained in practical humanitarian guidelines for site selection and routing decisions of the assessment teams while planning and executing the field visits. By simple, we mean methods that can be implemented by practitioners in the field using primary resources such as a paper map of the area and accessible software (e.g., Microsoft Excel). We test the performance of proposed heuristic algorithms, within a simulation environment , which enables us to incorporate various uncertain aspects of the post-disaster environment in the field, ranging from travel time and community assessment time to accessibility of sites and availability of community groups. We assess the performance of proposed heuristics based on real-world data from the 2011 Van earthquake in Turkey. Our results show that selecting sites based on an approximate knowledge of community groups’ existence leads to significantly better results than selecting sites randomly. In addition, updating initial routes while receiving more information also positively affects the performance of the field visit plan and leads to higher coverage of community groups than an alternative strategy where inaccessible sites and unavailable community groups are simply skipped and the initial plan is followed. Uncertainties in travel time and community assessment time adversely affect the community group coverage. In general, the performance of more sophisticated methods requiring more information deteriorates more than the performance of simple methods when the level of uncertainty increases.

Implementing clinical decision support for oncology advanced care planning: A systems engineering framework to optimize the usability and utility of a machine learning predictive model in clinical practice.

330 Background: Machine learning models are well-positioned to transform cancer care delivery by providing oncologists with more accurate or accessible information to augment clinical decisions. Many machine learning projects, however, focus on model accuracy without considering the impact of using the model in real-world settings and rarely carry forward to clinical implementation. We present a human-centered systems engineering approach to address clinical problems with workflow interventions utilizing machine learning algorithms. Methods: We aimed to develop a mortality predictive tool, using a Random Forest algorithm, to identify oncology patients at high risk of death within 30 days to move advance care planning (ACP) discussions earlier in the illness trajectory. First, a project sponsor defined the clinical need and requirements of an intervention. The data scientists developed the predictive algorithm using data available in the electronic health record (EHR). A multidisciplinary workgroup was assembled including oncology physicians, advanced practice providers, nurses, social workers, chaplain, clinical informaticists, and data scientists. Meeting bi-monthly, the group utilized human-centered design (HCD) methods to understand clinical workflows and identify points of intervention. The workgroup completed a workflow redesign workshop, a 90-minute facilitated group discussion, to integrate the model in a future state workflow. An EHR (Epic) analyst built the user interface to support the intervention per the group’s requirements. The workflow was piloted in thoracic oncology and bone marrow transplant with plans to scale to other cancer clinics. Results: Our predictive model performance on test data was acceptable (sensitivity 75%, specificity 75%, F-1 score 0.71, AUC 0.82). The workgroup identified a “quality of life coordinator” who: reviews an EHR report of patients scheduled in the upcoming 7 days who have a high risk of 30-day mortality; works with the oncology team to determine ACP clinical appropriateness; documents the need for ACP; identifies potential referrals to supportive oncology, social work, or chaplain; and coordinates the oncology appointment. The oncologist receives a reminder on the day of the patient’s scheduled visit. Conclusions: This workgroup is a viable approach that can be replicated at institutions to address clinical needs and realize the full potential of machine learning models in healthcare. The next steps for this project are to address end-user feedback from the pilot, expand the intervention to other cancer disease groups, and track clinical metrics.