Analyzing a Decision Support System for Resource Planning and Surgery Scheduling

AbstractThis study aims to propose a decision support system based on optimization modelling for operating room resource planning and sequence dependent scheduling of surgery operations. We conduct a simulation experiment using real world data collected from the local hospital to evaluate the proposed model. The obtained results are compared with real surgery schedules, planned at the local hospital. The experiment shows that the efficiency of schedules produced by the proposed model are significantly improved, in terms of less surgery turnover time, increased utilization of operating rooms and minimized make-span, compared to the real schedules. Moreover, the proposed optimization based decision support system enables analysis of surgery scheduling in relation to resource planning

Development of a Waste-to-Energy Decision Support System (WTEDSS)

International audienceRapid increase in urban population has created the need for the development of efficient Decision Support Systems (DSS) guiding municipal planners to mitigate urban sprawl, pollution and waste generation, unsustainable production and consumption patterns. To ensure sustainable urban planning, a DSS must provide not only an optimal planning solution based on input assumptions, but must also help to identify concrete city challenges, determine available resources (e.g., land and energy sources) and highlight any implementation constraints. It must support the creation of flexible interactive scenarios for urban development and their realistic representation in an urban context. This paper presents a Waste-to-Energy Decision Support System (WTEDSS) that identifies the optimal long-term deployment strategy for waste-to-energy infrastructures under future uncertain operational conditions and then directly assesses its feasibility and integration into an urban environment using 3D visualization. The WTEDSS is designed as an interactive and analytical waste management planning tool integrating four modules: data analytics, optimization, simulation and a user-friendly graphical interface. Emphasis is placed on the development and integration of the optimization module and 3D urban simulation, which provides users with decision support based on 3D visualized optimum facilities deployment plans. The optimization module receives calibrated data and solves a model based on inputs obtained from the user interface. The simulation platform developed in Unity 3D provides a friendly real-world environment for studying and understanding the facility deployment process over time and space, while also considering uncertainty

A decision support methodology for strategic planning in maritime transportation

a b s t r a c t This paper presents a decision support methodology for strategic planning in tramp and industrial shipping. The proposed methodology combines simulation and optimization, where a Monte Carlo simulation framework is built around an optimization-based decision support system for short-term routing and scheduling. The simulation proceeds by considering a series of short-term routing and scheduling problems using a rolling horizon principle where information is revealed as time goes by. The approach is flexible in the sense that it can easily be configured to provide decision support for a wide range of strategic planning problems, such as fleet size and mix problems, analysis of long-term contracts and contract terms. The methodology is tested on a real case for a major Norwegian shipping company. The methodology provided valuable decision support on important strategic planning problems for the shipping company

A DSS for business decisions in air transportation: a case study

The socio-economic development leads people to a great mobility. Thus the flights identification and management is becoming a key factor for the economic growth of the areas nearby the airports. The airport management is constantly looking for methods to improve its performance, both in terms of profitability and quality of service and the proper planning of passenger flows. To address these issues, scientific research provides methods and tools for decision support at all planning levels (i.e., strategic, tactical, operational, real time). In recent literature, it is now widely recognized that the hybridization of simulation and optimization systems is a very reliable technique for such decisions. This work intends to present an efficient Decision Support System framework based on the hybridization of a discrete event simulator and a Logit model. In order to show the effectiveness of the framework, we show the results of a real case study in North Ital

A Comprehensive Optimization Framework for Designing Sustainable Renewable Energy Production Systems

As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewable resources such as biomass, there arises a need for developing strategies which can design renewable sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from energy utilization. The objective of this research is to develop and implement a novel decision-making framework for the optimal design of renewable energy systems. The proposed optimization framework is based on a distributed, systematic approach which is composed of different layers including systems-based strategic optimization, detailed mechanistic modeling and operational level optimization. In the strategic optimization the model is represented by equations which describe physical flows of materials across the system nodes and financial flows that result from the system design and material movements. Market uncertainty is also incorporated into the model through stochastic programming. The output of the model includes optimal design of production capacity of the plant for the planning horizon by maximizing the net present value (NPV). The second stage consists of three main steps including simulation of the process in the simulation software, identification of critical sources of uncertainties through global sensitivity analysis, and employing stochastic optimization methodologies to optimize the operating condition of the plant under uncertainty. To exemplify the efficacy of the proposed framework a hypothetical lignocellulosic biorefinery based on sugar conversion platform that converts biomass to value-added biofuels and biobased chemicals is utilized as a case study. Furthermore, alternative technology options and possible process integrations in each section of the plant are analysed by exploiting the advantages of process simulation and the novel hybrid optimization framework. In conjunction with the simulation and optimization studies, the proposed framework develops quantitative metrics to associate economic values with technical barriers. The outcome of this work is a new distributed decision support framework which is intended to help economic development agencies, as well as policy makers in the renewable energy enterprises

A Comprehensive Simulator for Hydropower Investment Decisions

Due to a higher share of power production from renewable sources with high short-term variation, hydro systems must more often operate closer to their components' physical limits. To simulate system behaviour, a hydropower system simulator must therefore include most physical details. We present a simulator for hydropower investment analysis that combines a medium-term production planning model based on stochastic dual dynamic programming principles with a detailed and deterministic short-term hydro scheduling model. To reduce computation times, the system description for the short-term model may include only a snipped subset of the plants and reservoirs without deteriorating the results. The simulator is verified in a case study where an investment decision has been analysed for a Norwegian hydropower producer. The combination of medium-term optimization and short-term, detailed simulation is a useful decision support tool and provides both economic results and detailed physical information about the system behaviour.A Comprehensive Simulator for Hydropower Investment DecisionsacceptedVersio

Expert Systems for Integrated Development: A Case Study of Shanxi Province, The People's Republic of China

The research and development project described in this status report is a collaborative project between IIASA and the Tate Science and Technology Commission of the People's Republic of China (SSTCC). The project objective is to build a computer-based information and decision support system, using expert systems technology, for regional development planning in Shanxi, a coal-rich province in northwestern China. Building on IIASA's experience in applied systems analysis, the project develops and implements a new generation of computer-based tools, integrating classical approaches of operations research and applied systems analysis with new developments in computer technology and artificial intelligence (AI) into an integrated hybrid system, designed for direct practical application. To provide the required information, several databases, simulation and optimization models, and decision support tools have been integrated. This information is presented in a form directly useful to planners and decision makers. The system is therefore structured along concepts of expert systems technology, includes several AI components, and features an easy-to-use color graphics user interface. The study is being carried out with intensive collaboration between IIASA, and Chinese academic, industrial, and governmental institutions, especially the regional government of Shanxi Province. The report describes the status of the project after one year of research, summarizing the problem area, the design principles of the software and the current status of prototype implementations

Development of emergency response systems by intelligent and integrated approaches for marine oil spill accidents

Oil products play a pervasive role in modern society as one of the dominant energy fuel sources. Marine activities related to oil extraction and transportation play a vital role in resource supply. However, marine oil spills occur due to such human activities or harsh environmental factors. The emergency accidents of spills cause negative impacts on the marine environment, human health, and economic loss. The responses to marine oil spills, especially large-scale spills, are relatively challenging and inefficient due to changing environmental conditions, limited response resources, various unknown or uncertain factors and complex resource allocation processes. The development of previous research mainly focused on single process simulation, prediction, or optimization (e.g., oil trajectory, weathering, or cleanup optimization). There is still a lack of research on comprehensive and integrated emergency responses considering multiple types of simulations, types of resource allocations, stages of accident occurrence to response, and criteria for system optimizations. Optimization algorithms are an important part of system optimization and decision-making. Their performance directly affacts the quality of emergency response systems and operations. Thus, how to improve efficiency of emergency response systems becomes urgent and essential for marine oil spill management. The power and potential of integrating intelligent-based modeling of dynamic processes and system optimization have been recognized to better support oil spill responders with more efficient response decisions and planning tools. Meanwhile, response decision-making combined with human factor analysis can help quantitatively evaluate the impacts of multiple causal factors on the overall processes and operational performance after an accident. To address the challenges and gaps, this dissertation research focused on the development and improvement of new emergency response systems and their applications for marine oil spill response in the following aspects: 1) Realization of coupling dynamic simulation and system optimization for marine oil spill responses - The developed Simulation-Based Multi-Agent Particle Swarm Optimization (SA-PSO) modeling investigated the capacity of agent-based modeling on dynamic simulation of spill fate and response, particle swarm optimization on response allocation with minimal time and multi-agent system on information sharing. 2) Investigation of multi-type resource allocation under a complex simulation condition and improvement of optimization performance - The improved emergency response system was achieved by dynamic resource transportation, oil weathering and response simulations and resource allocation optimization. The enhanced particle swarm optimization (ME-PSO) algorithm performed outstanding convergence performance and low computation cost characteristics integrating multi-agent theory (MA) and evolutionary population dynamics (EPD). 3) Analysis and evaluation of influencing factors of multiple stages of spill accidents based on human factors/errors and multi-criteria decision making - The developed human factors analysis and classification system for marine oil spill accidents (HFACS-OS) framework qualitatively evaluated the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response). The framework was further coupled with quantitative data analysis by Fuzzy-based Technique for Order Preference by Similarity to Idea Solution (Fuzzy-TOPSIS) to enhance decision-making during response operations under multiple criteria. 4) Development of a multi-criteria emergency response system with the enhanced optimization algorithm, multi-mode resource transportation and allocation and a more complex and realistic simulation modelling - The developed multi-criteria emergency response system (MC-ERS) system integrated dynamic process simulations and weighted multi-criteria system optimization. Total response time, response cost and environmental impacts were regarded as multiple optimization goals. An improved weighted sum optimization function was developed to unify the scaling and proportion of different goals. A comparative PSO was also developed with various algorithm-improving methods and the best-performing inertia weight function. The proposed emergency response approaches in studies were examined by oil spill case studies related to the North Atlantic Ocean and Canada circumstances to analyze the modelling performance and evaluate their practicality and applicability. The developed optimization algorithms were tested by benchmarked functions, other optimization algorithms, and an oil spill case. The developed emergency response systems and the contained simulation and optimization algorithms showed the strong capability for decision-making and emergency responses by recommending optimal resource management or evaluations of essential factors. This research was expected to provide time-efficient, and cost-saving emergency response management approaches for handling and managing marine oil spills. The research also improved our knowledge of the significance of human factors/errors to oil spill accidents and response operations and provided improved support tools for decision making. The dissertation research helped fill some important gaps in emergency response research and management practice, especially in marine oil spill response, through an innovative integration of dynamic simulation, resource optimization, human factor analysis, and artificial intelligence methods. The research outcomes can also provide methodological support and valuable references for other fields that require timely and effective decisions, system optimizations, process controls, planning and designs under complicated conditions, uncertainties, and interactions

The integrated control of production-inventory systems

In this thesis, we investigate a multi-product, multi-machine production-inventory (PI) system that is characterized by: ?? relatively high and stable demand; ?? uncertainty in the precise timing of demand; ?? variability in the production process; ?? job shop routings; ?? considerable setup times and costs. This type of PI system can be found in the supply chain of capital goods. Typically, it represents a manufacturer of parts that are assembled in later stages of the supply chain. Our exploratory research aims at identifying promising control approaches for this type of PI systems and the conditions in which they are applicable. The control approaches developed in this thesis are based on an integrated view of the PI system. The objective of the control approaches is to minimize the sum of setup costs, work-in-process holding costs and ¿nal inventory holding costs, while target customer service levels are satis¿ed. The research reveals that the exact analysis and optimization of this type of PI systems is impossible. Therefore, we are restricted to the development of heuristic control approaches. We propose two control strategies that are based on distinct control principles. For each of the control strategies, we develop and test decision-support systems that can be used to determine cost-e¢ cient (but not necessarily optimal) control decisions. Part I of this thesis deals with the ¿rst approach, called Coordinated Batch Control (CBC). This strategy uses a periodic review, order-up-to inventory pol- icy to control the stock points. The replenishment orders generated by this inventory policy are manufactured by the production system. The CBC strat- egy integrates production and inventory control decisions by determining cost- e¢ cient review periods. There is no further integration of control decisions. At the shop ¿oor, a myopic rule is used to sequence the orders, which ensures a certain degree of ¿exibility for responding to unexpected circumstances. We develop three decision-support systems for the CBC approach. The ¿rst decision-support system is based on an approximate analytical model of the PI system. In the approximate analytical model, we apply standard results from inventory theory, queueing theory and renewal theory. The second and third decision-support system use simulation optimization techniques to determine the near-optimal review periods. The three heuristic decision-support systems for CBC are tested in an exten- sive simulation study. The test bed consists of ¿ve basic problem con¿gurations, which de¿ne a routing structure, processing times, etc. We vary four factors over several levels: setup costs, setup times, net utilization and target ¿ll rates. In this way, we obtain 48 instances based on the same basic problem con¿guration, leading to 5 x 48 = 240 problem instances. The simulation study shows that the use of simulation optimization resulted in relatively small improvements over the solution obtained from the approximate analytical model. Since simulation optimization requires large amounts of computation e¤ort, we decide that the use of the decision-support system based on the approximate analytical model is justi¿ed. Part II is concerned with the Cyclical Production Planning (CPP) strategy. This strategy approaches the control of the PI system from a totally di¤erent angle. In this strategy, a detailed production schedule is used to control the production system. The schedule prescribes the sequence in which the orders are produced on the work centers and this schedule is repeated at regular time intervals. The time that elapses between the start of two schedules is called the ¿cycle time¿. The schedule is determined such that the total costs are minimized. The stock points are controlled with periodic review, order-up-to policies. The main advantage of the use of a production schedule is that ¿ow of the orders through the production system is controlled better, which results in more re- liable throughput times. A drawback of this approach is that the production frequencies of the di¤erent products need to be matched in order to make a cyclic production schedule. Hence, there is less ¿exibility in setting the lot sizes, which may result in higher costs. Another drawback of the CPP approach is that production capacity may be wasted by strictly following the prespeci¿ed processing sequences. We propose a decision-support system for the CPP strategy which is based on a deterministic model of the PI system. The decision-support system is used to determine cost-e¢ cient production plans. We present a heuristic method to approximately minimize the total costs of the deterministic model. When the solution of the deterministic model is used in a stochastic environment, the solution may be instable or nearly instable. Therefore, we use a simulation procedure to check whether the proposed solution is stable. If not, slack-time is added to the schedule and deterministic model is solved again. We test the decision-support system for CPP in an extensive simulation study. The test bed is identical to the one used in Part I. We test wether the Summary 273 decision-support system responds soundly to changes in the factors. Further- more, we investigate the estimation quality of the deterministic model that is embedded in the decision-support system. Finally, we test the optimization quality of the decision-support system. Based on the results of these tests, we decide that it is acceptable to use the proposed decision-support system to determine the control variables of the CPP strategy. Part III compares the performance of the CBC and the CPP strategy. Both strategies are compared in a simulation study consisting of the same instances as in Part I and II. We compare the strategies in terms of realized total costs. In about 62% of the instances, the CPP strategy outperforms the CBC strategy. In the remaining 38% of the instances, the CBC strategy realizes lower costs than the CPP strategy. An analysis of variance reveals that the following factors have a signi¿cant impact on the performance di¤erence between CPP and CBC: ?? net utilization; ?? setup costs; ?? interaction between setup costs and net utilization; ?? basic problem con¿guration. Based on our investigations, we can provide an explanation for these obser- vations. The simulation results show that the performance di¤erence is pro- portional to the di¤erence between the average review periods (CBC) and the common cycle length (CPP), denoted as dR. The factors mentioned above have an in¿uence on dR through their impact on capacity utilization. At low lev- els of capacity utilization, we observe that dR is low, which indicates that the CPP and CBC strategy operate with comparable review periods and common cycle lengths. In situations where the CBC strategy operates at higher levels of capacity utilization (because net utilization increases and/or setup costs de- crease), it becomes more di¢ cult for the CPP strategy to ¿nd a feasible cyclical production schedule, mainly because production capacity is wasted by strictly following a prespeci¿ed processing sequence. In these cases, the CPP strategy needs to increase the common cycle length to free up production capacity that is used to compensate for the loss of capacity. This leads to increases in dR and to higher costs. The speci¿c characteristics of a problem instance have a strong in¿uence on the magnitude of this e¤ect. Based on the insights obtained from our research, we formulate some guidelines for the application of CPP and CBC

Water and energy systems in sustainable city development: a case of Sub-saharan Africa

Current urban water and energy systems are expanding while increasing attention is paid to their social, economic and environmental impacts. As a research contribution that can support real-world decision making and transitions to sustainable cities and communities, we have built a model-based and data-driven platform combining comprehensive database, agent-based simulation and resource technology network optimization for system level water and energy planning. Several use cases are demonstrated based on the Greater Accra Metropolitan Area (GAMA) city-region in Ghana, as part of the Future Cities Africa (FCA) project. The outputs depict an overall resource landscape of the studied urban area, but also provide the energy, water, and other resource balance of supply and demand from both macro and micro perspectives, which is used to propose environmental friendly and cost effective sustainable city development strategies. This work is to become a core component of the resilience.io platform as an open-source integrated systematic tool gathering social, environmental and economic data to inform urban planning, investment and policy-making for city-regions globally