Simulation-based Optimization Model Research Articles

Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling.

Outpatient Chemotherapy Appointment (OCA) planning and scheduling is a process of distributing appointments to available days and times to be handled by various resources through a multi-stage process. Proper OCAs planning and scheduling results in minimizing the length of stay of patients and staff overtime. The integrated consideration of the available capacity, resources planning, scheduling policy, drug preparation requirements, and resources-to-patients assignment can improve the Outpatient Chemotherapy Process's (OCP's) overall performance due to interdependencies. However, developing a comprehensive and stochastic decision support system in the OCP environment is complex. Thus, the multi-stages of OCP, stochastic durations, probability of uncertain events occurrence, patterns of patient arrivals, acuity levels of nurses, demand variety, and complex patient pathways are rarely addressed together. Therefore, this paper proposes a clustering and stochastic optimization methodology to handle the various challenges of OCA planning and scheduling. A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. The developed cluster-based stochastic optimization approaches showed superior performance compared with baseline and sequencing heuristics using data from a real Outpatient Chemotherapy Center (OCC).

Time-of-day pricing for toll roads under traffic demand uncertainties: A distributionally robust simulation-based optimization method

Road pricing has received increasing attention for traffic congestion mitigation, but traffic demand uncertainties cause a profound impact on its effectiveness. This study proposes a link-based time-of-day pricing distributionally robust simulation-based optimization (DRSO) model to minimize the worst-case expected total travel time (TTT). This model aims to answer where and how much to charge under origin–destination (OD) traffic demand uncertainties that are described by an ambiguity set of probability distributions, and is solved by a DRSO method with two stages. The first stage searches an infill sample after building two stochastic kriging models (i.e., SKG-I and SKG-II) to map the relation between the traffic demand uncertainty parameter and the average TTT for each decision sample, and that between the decision variable and the worst-case average TTT, respectively. The second stage introduces the optimal computational budget allocation (OCBA) method to distribute more simulation replications to more promising combination samples, which helps to improve the prediction accuracy of SKGs in promising regions. The DRSO method is first tested with an M/M/1 queuing problem, and numerical results show its outperformance compared with one counterpart algorithm. After that, it is applied to address the link-based time-of-day pricing DRSO problem on Anaheim network and two solution algorithms are also added for comparison. Numerical results show that the robust toll plan solved by the DRSO method outperforms no toll plan and two other toll plans solved by the comparison algorithms in handling traffic dynamics and different levels of traffic demand uncertainties. In conclusion, the DRSO method is promising to address the costly simulation-based optimization (SO) problems under uncertainties characterized by an ambiguity set of probability distributions.

Optimal green technology investment and emission reduction in emissions generating companies under the support of green bond and subsidy

Carbon emission is one of the major problems in emissions generating companies (EGCs) due resource consumption, spurring a need for carbon emission reduction, but most EGCs cannot reduce it due to a lack of green technology implementation. This research proposes a monopoly market under the consideration of EGCs and develops a simulation-based optimization model to measure the optimal behavior of green technology investment to reduce carbon emission. To see its implementation, we investigate the intervention of the government, which provides an optimal subsidy on green technology investment. Our study helps the government to find an optimal green technology investment and subsidy on green technology investment. Additionally, this optimal subsidy allows decision-makers to improve environment cleanliness by taking incentives in the form of green subsidy. They can also fulfill their primary objective of profit maximization by finding an optimum product price.

A simulation-based optimization scheme for phase-out of natural gas subsidies considering welfare and economic measures

Energy subsidies are granted by many governments to alleviate energy poverty, support domestic industries, promote economic development, and improve welfare levels. These energy subsidies can lead to overconsumption of energy and an increase in CO2 emissions. Hence, it is crucial to continuously investigate the overall benefits of subsidies compared to their costs and plan for their phase-out if required. The gradual phase-out and the selection of optimal period over which to phase-out subsidies are crucial in minimizing possible adverse impacts. A simulation-based optimization model is developed to find the optimal period for natural gas subsidies removal from multiple consumption sectors. Implementing the model on the case study of Iran illustrates that if the reserved fund saved from subsidies removal is invested in the natural gas industry to promote export, and a part of it is allocated to only vulnerable residential consumers and strategic industries, the aggregate economic, environmental and welfare measures improve significantly. Another finding is that the optimal phase-out periods vary for different sectors and the industrial sector needs a longer phase-out period. The presented model quantifies the advantages of allocating subsidies only to targeted groups instead of subsidies on prices, and analyzes the tradeoffs between economic and welfare criteria.

Modeling the competing demands of carriers, building managers, and urban planners to identify balanced solutions for allocating building and parking resources

While the number of deliveries has been increasing rapidly, infrastructure such as parking and building configurations has changed less quickly, given limited space and funds. This may lead to an imbalance between supply and demand, preventing the current resources from meeting the future needs of urban freight activities. The aim of this study was to discover the future delivery rates that would overflow the current delivery systems and find the optimal numbers of resources. To achieve this objective, we introduced a multi-objective, simulation-based optimization model to define the complex freight delivery cost relationships among delivery workers, building managers, and city planners, based on the real-world observations of the final 50 ft of urban freight activities at an office building in downtown Seattle, Washington, U.S.A. Our discrete-event simulation model with increasing delivery arrival rates showed an inverse relationship in costs between delivery workers and building managers, while the cost of city planners decreased up to ten deliveries/h and then increased until 18 deliveries/h, at which point costs increased for all three parties and overflew the current building and parking resources. The optimal numbers of resources that would minimize the costs for all three parties were then explored by a non-dominated sorting genetic algorithm (NSGA-2) and a multi-objective, evolutionary algorithm based on decomposition (MOEA/D). Our study sheds new light on a data-driven approach for determining the best combination of resources that would help the three entities work as a team to better prepare for the future demand for urban goods deliveries.

Simulation-Based Optimization Algorithm for the Vendor-Managed Inventory Problem for Blood Platelets

Objective: Estimate an optimal policy for the blood platelets supply chain distribution problem using a vendor-managed inventory problems approach. Methods and materials: This paper uses an integrated simulation-based optimization model to develop a Vendor-Managed Inventory approach for blood platelets. Simulation is used to estimate the performance of a defined inventory policy. On the other hand, a genetic algorithm finds optimal or near-optimal inventory policies. This approach is evaluated using a case study inspired by a real blood center in Colombia. Results and discussion: Using the proposed approach, key indicators in the blood supply chain such as total cost and outdated units are significantly improved while maintaining the service level. In terms of costs, the VMI model shows a 19.19% advantage over the non-VMI solution. Moreover, the proposed VMI solution can reduce by 42.25% the number of expired platelets. Conclusions: Using a VMI-based distribution system and a simulation-based optimization approach with genetic algorithms offers promising results in the proposed use case. This mixed methodology allows for flexible system configurations without the need for complex changes in the algorithm, and it does so without the need for excessive computational resources.

The optimal behavior of renewable energy resources and government's energy consumption subsidy design from the perspective of green technology implementation

Carbon emissions are a major problem in the industrial sector due to energy consumption. As a solution, this research first investigates the impact of energy consumption and shortfall on green technology implementation from 1000 textile firms in Punjab, Pakistan and for energy shortfall incorporates subsidy variables to make green technology implementation possible via a binary logistic regression model. Second, we develop a simulation-based optimization model to measure the optimal behavior of energy consumption parameters to clean the environment under green technology implementation. The results depict that renewable energy resources (coal, corn cobs, and wood) positively affect green technology implementation. Interestingly, energy shortfall (electricity and gas) is also impacted positively due to the move of industries towards renewable resources during this shortage period. We find a higher quantity of wood in optimal behavior analysis instead of coal and cobs due to its ease of availability. An optimal subsidy on electricity shortfall is found to be greater than the natural gas shortfall subsidy due to its higher shortfall. This study helps the government to decide the optimal energy shortfall for saving energy usage and to manage the optimal level of subsidies in order to overcome energy usage problems. It also benefits the textile industry in deciding over the optimal energy consumption level to make the environment clean in terms of green technology implementation.

Hybrid mathematical and simulation model for designing a hierarchical network of temporary medical centers in a disaster

ABSTRACT The destructions caused by natural disasters have made human societies well aware of the importance of logistical preparedness and precise human resource planning both in the pre- and post-disaster phases. Temporary Medical Centers (TMCs) are one of the most important problems for disaster response. TMCs are established in safe places to treat the injured in times of crisis. In this paper, the optimal locations for the establishment of TMCs are determined using a mixed-integer mathematical programming model. Furthermore, an optimal hierarchical structure is developed for assigning the injured from urban areas to the TMCs, clinics, and hospitals in the event of a disaster. A simulation-based optimisation model is employed to simulate the conditions at the moment the disaster occurs and the ensuing response operations. The findings indicate that the proposed hierarchical location-allocation model reduces the congestion of the injured and minimises total costs and patients’ treatment time.The destructions caused by natural disasters have made human societies well aware of the importance of logistical preparedness and precise human resource planning both in the pre- and post-disaster phases. Temporary Medical Centers (TMCs) are one of the most important problems for disaster response. TMCs are established in safe places to treat the injured in times of crisis. In this paper, the optimal locations for the establishment of TMCs are determined using a mixed-integer mathematical programming model. Furthermore, an optimal hierarchical structure is developed for assigning the injured from urban areas to the TMCs, clinics, and hospitals in the event of a disaster. A simulation-based optimisation model is employed to simulate the conditions at the moment the disaster occurs and the ensuing response operations. The findings indicate that the proposed hierarchical location-allocation model reduces the congestion of the injured and minimises total costs and patients’ treatment time.

Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model

The growth of subtransmission network aims at satisfying load growth, maintaining a contingency level, and providing a high quality and reliable electricity service. Utilities direct the investments to reinforce this system and thus a meshed network with multiple-point feeding to the transmission system arises. At this point, an efficient alternative to achieve these objectives is to carry out a diagnosis of the network architecture and, taking advantage of the switching capability, to plan the switching of the subtransmission lines. An optimal subtransmission switching approach is proposed based on constrained multi-objective optimization that deals with energy losses and reliability, in addition to using information on the characteristics of loads and generation. A simulation-based optimization framework is constructed using the non-dominated genetic classification algorithm NSGA-II in the optimization phase and reliability assessment during simulation phase. As a result, a set of non-dominated solutions approximating the Pareto front is obtained, which allows the planner to make decisions based on its priorities and needs. The performance of the proposal is assessed with a real subtransmission system of an Ecuadorian power utility. This approach to the operational planning of a meshed subtransmission network constitutes a powerful decision-making tool that could be adopted by distribution utilities.

Life cycle dynamic sustainability maintenance strategy optimization of fly ash RC beam based on Monte Carlo simulation

Small and medium span bridges account for 86% of the bridges in China, and maintenance is necessary to ensure their safety and serviceability. The infrastructure sector causes substantial CO2 emissions, and it is one of the most polluting sectors. Fly ash (FA) is often used in bridges to reduce CO2 emissions. Moreover, the lack of temporal information is an important limitation of the life cycle CO2 assessment of bridges. Taking the upper structure of small and medium span bridges as the research object, this paper proposes a Monte Carlo simulation-based life cycle multi-objective dynamic maintenance strategy optimization model to analyze the life-cycle carbon emissions, costs, and life cycle reliability of different preventive maintenance (PM) and essential maintenance (EM) methods of FA reinforced concrete (RC) beams. Considering the continuous effect of carbon emissions and the net present value of the cost, the time effect of carbon emissions and costs of maintenance and reinforcement at different times is analyzed. Through 50,000 simulations of the case beam, the parameter characteristics of different optimal reinforcement schemes and the relationship between the reliability, life cycle CO2 emissions, and costs are studied. When introducing the discount rate, the essential maintenance with higher carbon emissions and costs has a marginal effect, i.e., the longer that future carbon emissions and costs occur, the smaller the net present value. The proposed method is demonstrated to be effective at supporting decision making for bridge managers. It would be helpful for identifying the effectiveness of PM and EM, selecting optimal maintenance time interval, and providing a rational estimate of the CO2 emissions and costs of bridges.

Simulation-Based Optimization Model to Minimize Equivalent Annual Cost of Existing Buildings

Simulation-Based Optimization Model to Minimize Equivalent Annual Cost of Existing Buildings

Adaptive Simulation-Based Optimization for Production Scheduling: A Comparative Study

Production planning and control is an essential activity for all production systems, as its performance directly affects important factors that are fundamental for business competitiveness. The flow shop scheduling problem is one of the most common problems involved in the planning of production systems, being often applied in the real world. The solution to this problem cannot be easily found due to its complexity, which makes it difficult to use methods that return optimal results due to their high computational execution time. Thus, Adaptive Simulation-Based Optimization is a tool with potential application as it considers the stochasticity of the problem, seeking solutions that are aligned with the real scenario through a simulation and optimizing the desired variables. The simulation-based optimization approach consists in a loop between an optimization heuristic/meta-heuristic and a simulation. The optimization inputs the simulation with a parameter set, while the simulation tests all the models generated by the optimization and returns the fitness function values. However, the literature presents several different heuristics, simulation approaches and optimization methodologies to solve the problem. The general objective of the present work is to comparatively evaluate different adaptive simulation-based optimization models for flow shop production systems. Two methods for comparison were considered, which apply a genetic algorithm integrated with a discrete events simulation. The first method seeks to find the best dispatching rule for each machine in the production system, while the second seeks to directly find the best sequencing of production orders without a predetermined rule. The results show that the rule-free sequencing of production orders returns a result 3.56% better than annual makespan and 5.62% better in execution time compared to the model based on dispatching rules.

A Simulation-based Optimization Model for the Vehicle Routing Problem with Bimodal Stochastic Travel Time Distribution

This paper proposes a simulation-based optimization model for solving a realistic delivery problem within an urban area that respects the customers’ availability. Specifically, the originality of this work is to consider the bimodal realistic traffic pattern when solving the classic capacitated vehicle routing problem with time windows. Computational results have shown that under bimodal travel time distribution the routing solution changes considerately. Thus, this work emphasizes the need to fully integrate the traffic behavior when solving this class of routing problem with stochastic travel times. Furthermore, it highlights the easiness of such integration, when the routing problem is modeled and solved using this promising simulation-based optimization approach.

An integrated HFMEA simulation-based multi-objective optimisation model to improve the performances of hospitals: A case study

ABSTRACT There are many problems with hospital departments that decrease patients’ satisfaction and hospitals’ performance. Although there are many qualitative and quantitative methods to improve related performances, some of them consider a single department and the rest don't consider related priorities. Therefore, this paper proposes an integrated model based on Six Sigma to consider the performances of all departments with priorities based on experts’ knowledge. This model utilizes qualitative methods including fishbone diagrams and Healthcare Failure Mode and Effect Analysis to find problems and their priorities based on experts’ knowledge and data. Then, quantitative methods including a mathematical model, Discrete Event Simulation, and Non-dominated Sorting Genetic Algorithm II are utilized to find solutions for preferred problems as a simulation-based multi-objective optimization model. Based on a case study in a well-known general hospital, the proposed model finds the hospital’s most important problems and represents the impacts of different improvement plans before implementation.

Design of combined stationary and mobile battery energy storage systems.

To minimize the curtailment of renewable generation and incentivize grid-scale energy storage deployment, a concept of combining stationary and mobile applications of battery energy storage systems built within renewable energy farms is proposed. A simulation-based optimization model is developed to obtain the optimal design parameters such as battery capacity and power ratings by solving a multi-objective optimization problem that aims to maximize the economic profitability, the energy provided for transportation electrification, the demand peak shaving, and the renewable energy utilized. Two applications considered for the stationary energy storage systems are the end-consumer arbitrage and frequency regulation, while the mobile application envisions a scenario of a grid-independent battery-powered electric vehicle charging station network. The charging stations receive supplies from the energy storage system that absorbs renewable energy, contributing to a sustained DC demand that helps with revenues. Representative results are presented for two operation modes and different sets of weights assigned to the objectives. Substantial improvement in the profitability of combined applications over single stationary applications is shown. Pareto frontier of a reduced dimensional problem is obtained to show the trade-off between design objectives. This work could pave the road for future implementations of the new form of energy storage systems.

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm for replacing non-linear process simulation models integrated in multi-level optimization of a process and energy system superstructure with surrogate models, applying an active learning strategy to continuously enrich the database on which the surrogate models are trained and evaluated. Surrogate models are generated and trained on an initial data set, each featuring the ability to quantify the uncertainty with which a prediction is made. Until a defined prediction quality is met, new data points are continuously labeled and added to the training set. They are selected from a pool of unlabeled data points based on the predicted uncertainty, ensuring a rapid improvement of surrogate quality. When applied in the optimization superstructure, the surrogates can only be used when the prediction quality for the given data point reaches a specified threshold, otherwise the original simulation model is called for evaluating the process performance and the newly obtained data points are used to improve the surrogates. The method is tested on three simulation models, ranging in size and complexity. The proposed approach yields mean squared errors of the test prediction below 2% for all cases. Applying the active learning approach leads to better predictions compared to random sampling for the same size of database. When integrated in the optimization framework, simpler surrogates are favored in over 60% of cases, while the more complex ones are enabled by using simulation results generated during optimization for improving the surrogates after the initial generation. Significant time savings are recorded when using complex process simulations, though the advantage gained for simpler processes is marginal. Overall, we show that the proposed method saves time and adds flexibility to complex superstructure optimization problems that involve optimizing process operating conditions. Computational time can be greatly reduced without penalizing result quality, while the continuous improvement of surrogates when simulation is used in the optimization leads to a natural refinement of the model.

A simulation-based optimization model to determine optimal electricity prices under various scenarios considering stakeholders’ objectives

Electricity subsidy is allocated by many governments to help access electricity, support domestic industries and achieve social objectives. However, by distorting price signals, problems such as over consumption, low efficiency and investment shortage are attributed to this policy. Thus, many countries want to reduce or remove subsidies. A simulation-based optimization model using system dynamics approach is presented to determine optimal prices for electricity demand sectors and power plants' fuels under demand scenarios considering objectives of Iran's power sector stakeholders, namely government, electricity consumers and electricity producers. The model uses the prices of energy carriers determined based on economic, social and environmental criteria to simulate their impact on supply and demand of electricity, environmental emissions, water consumption and allocated subsidies. Simulation results indicate the household sector should have considerable higher prices in all scenarios. Moreover, with 57% decline in allotted funds, a complete removal of subsidies occurs in case 4. It is shown electricity export reaches 11% of total consumption. Also, CO2 emission and water use respectively decreases by 17.2% and 18.1%. The electricity supply reaches 61% more than the current value and average efficiency of thermal plants reaches 58.31% by 2040, compared respectively with −12% and 42.7%, in case 1.

Optimizing energy efficiency and thermal comfort in building green retrofit

Building green retrofit offers significant opportunities for enhancing energy efficiency and achieving green development goals. However, a conflicting criterion exists between energy conservation and thermal comfort improvement when making optimal design solutions for building retrofit. This study presents a simulation-based energy-comfort optimization model to facilitate evaluating various design alternatives and balancing multiple objectives in building green retrofit. A building simulation model is first established to measure energy consumption and comfort level. Then, a multi-objective optimization method (response surface method) is employed to identify critical building parameters and generates a set of alternative plans for building retrofit based on green building standards. After that, optimal design solutions with trade-offs between thermal comfort and energy demand are obtained. A school building in Wuhan city of China is chosen as a case to validate the developed model, and ten building parameters pertaining to energy demand and environmental comfort are considered in the optimization process. The results show that four parameters are significantly sensitive to energy efficiency and thermal comfort, including insulation thickness of the external wall, the heat transmission coefficient of the roof, solar heat gain coefficient of the external window, and window to wall ratio. The optimal combination of four parameters approximately produces 4 % of energy savings, as well as an improving level of environmental comfort. The study benefits designers and construction managers to determine optimal solutions for building retrofit to achieve better energy efficiency and comfort in green building development.

Optimal design and operation of an Organic Rankine Cycle (ORC) system driven by solar energy with sensible thermal energy storage

In this study, the optimal design and operation of an Organic Rankine Cycle (ORC) system driven by solar energy is investigated. A two-tank sensible thermal energy storage system is configured to overcome the intermittency of solar energy. A circulating fluid, also termed as heat transfer fluid (HTF) that connects the solar collector and the ORC system plays a critical role in this system. The mass flowrate of the HTF determines both the temperature of the HTF and the amount of heat absorbed from the solar collector. A simulation-based optimization model is developed in this work. Process simulation of the ORC is performed in Aspen HYSYS, and the mathematical models of the energy storage system and the parabolic trough collector are developed in Matlab. The optimal design of the system including the hot tank temperature, cold tank temperature, mass flowrate of the HTF, and operating conditions of the ORC are determined simultaneously based on the simulation-based optimization framework. The control strategy of the solar collector can be determined as well. The system efficiency of the solar energy driven ORC system is maximized with the proposed optimal operation strategy. With the simulation-based optimization framework, the system efficiency of the recuperative ORC power plant with toluene as the working fluid is increased from 17.9% to 24.8% compared with a previous study in the literature. The recuperative ORC performs much better than the basic ORC. Toluene performs best among all the investigated working fluids ignoring the problem with vacuum condensation. The cycle type (subcritical vs. supercritical) exerts great influence on the system performance. The supercritical ORC can improve the thermal efficiency by 11.3% and the overall system efficiency by 10.8% compared with the subcritical ORC with n-pentane as the working fluid.

Simulation-based optimization for spatiotemporal allocation of irrigation water in arid region

Optimal allocation of irrigation water both spatially and temporally is a great challenge in arid and semi-arid regions with limited water available for irrigation. In this study, a simulation-based optimization model for spatiotemporal allocation of irrigation water in arid region was developed, which integrated a distributed farmland water balance model for simulating hydrological processes in farmland and an optimization model to maximize irrigation and precipitation use efficiency (IPUE). A typical arid region, the middle reach of the Heihe River basin in Northwest China with a total farmland area of 2624 km2, was selected as the study area. Field experiment data in 2012 and 2013 were used to calibrate and validate the simulation model. Then, the simulation-based optimization model was used to optimize the spatial and temporal allocation of irrigation water under various scenarios. Results indicate that the model is applicable for reflecting the complexities of water consumption and irrigation water allocation in the study area. The optimal irrigation water depths averaged for different farmland units are 326 mm, 327 mm, 471 mm, and 495 mm for spring wheat, barley, vegetable, and maize, respectively. After optimal allocation of irrigation water, IPUEs can exceed 0.8 for all climate conditions, and IPUE in the wet year was lower than those in other circumstances because of higher precipitation in the wet year. Higher IPUE can be achieved when the gross irrigation water are 1.72, 1.90 and 1.97 billion m3 for the representative wet, normal, and dry years, respectively. The results can be used to guide irrigation water management by providing the information on when and how much to irrigate for each crop in different farmland units.