Simulation-optimization Framework Research Articles

Optimization of selective withdrawal systems in hydropower reservoir considering water quality and quantity aspects

The selective withdrawal system (SWS) has often been used to alleviate downstream temperature pollutions and in-reservoir unacceptable oxygen concentrations whilst increasing hydropower energy generation. The aim of this study is to a) design the optimal locations of intakes in SWS, and, b) derive optimal parameterized selective withdrawal operation rules. The integration of water quality and quantity aspects to meet long-term environmental services and economic benefits is challenging. The main challenge lies in coupling search-based optimization algorithms with a numerical hydrodynamic and water quality simulation model (i.e., CE-QUAL-W2). To cope with the computational burdens of CE-QUAL-W2, surrogate models have been developed to represent the dynamics of temperature and water quality according to various SWSs. The surrogate models and the hydropower energy computation module were coupled with a multi-objective particle swarm optimization algorithm in an adaptive surrogate-based simulation–optimization framework (ASBSOF). The ASBSOF is applied in Karkheh reservoir, Andimeshk, Khuzestan, Iran, to address problems a and b. The results indicate that the optimal SWSs provide the advantage of sustainable management to balance the energy generation whilst minimizing environmental damages.

A simulation-optimization study of the inventory of a bike-sharing system: The case of Mexico City Ecobici’s system

Bike-sharing systems (BSS) are a healthy and environmentally friendly alternative for public transportation in many large urban areas around the world. In 2010, Mexico City started its BSS, named Ecobici, with 84 stations and reached more than 840,000 trips that year. Ecobici rapidly became a preferred option for many users. By 2019, the number of stations in Ecobici had grown almost 6 times since the first year. In addition, the number of registered trips on 2019 was more than 8.4 million and the number of trips in a weekday were more than 30,000. In order to incentivize the adoption of bike as transportation mode, Ecobici and other BSS need to handle the bike inventory at their stations throughout the day. There are many challenges for optimizing bike inventory in BSS given the complex interdependencies, the non-homogeneous demand flows, and the large-scale scenarios. In this paper, we propose and study a simulation–optimization framework to minimize the number of shortage events (failed bike withdrawals and returns) by setting an adequate bike inventory at each station throughout the day. We propose an efficient modeling approach to simulate large-scale BSS and a simulation–optimization heuristic to optimize bike inventory. The experiments show that these algorithms obtain results with similar quality than other search methods, but in a significant shorter amount of time.

Groundwater sustainability: Developing a non-cooperative optimal management scenario in shared groundwater resources under water bankruptcy conditions

Groundwater level drawdown changes the hydrological cycle and poses challenges such as land subsidence and reduction of the groundwater quality. In this study, a new approach using a simulation-optimization framework was developed for shared groundwater management under water bankruptcy conditions (where water demand is greater than the allowable discharge capacity of water resources). The novelty of this study lies in using bankruptcy rules and a game model to manage a bankrupted shared groundwater resource considering aquifer sustainability. Accordingly, groundwater flow in the aquifer was numerically simulated by a finite-differences model (MODFLOW). Then, the repeated performance code of the finite-differences model was run for different discharge scenarios, and the results were applied to develop an MLP-ANN meta-model. By coupling the meta-model with a non-dominated sorting genetic algorithm II (NSGA–II)–based multi-objective optimization model, an optimized cultivation pattern under water bankruptcy conditions was achieved. Then, six different bankruptcy methods were utilized to specify groundwater allocation between three stakeholders. To manage the water bankruptcy conditions, different scenarios considering various groundwater extraction rates and cultivation areas were defined, and the optimization model was recoded for each scenario to find the corresponding optimized cultivation pattern. To consider the competition between stakeholders for groundwater extraction, a non-cooperative 3-player game was applied to achieve a compromise for different combinations of management strategies, which maximizes the profit and yields the best cultivation scenario. Applicability of the proposed methodology was investigated in an aquifer system located in Golestan Province, Iran, including three regions (Minudasht, Azadshahr, and Gonbade-kavus). Results show that the proposed method is capable of managing the bankruptcy conditions by generating greater agricultural profit and reducing groundwater drawdowns.

A simulation–optimization framework for enhancing robustness in bulk berth scheduling

The service time of the vessels is one of the main indicators of ports’ competitiveness. This, together with the increasing volume of bulk transportation, make the efficient management of scarce resources such as berths a crucial option for enhancing the productivity of the overall terminal. In real scenarios, the information available to port operators may vary once the planning has been elaborated. Unforeseen events, errors, or modifications in the available information can lead to inefficient terminal management and the initial scheduling might become unfeasible. This implies that the use of deterministic approaches may not be enough to maximize productivity. Therefore, in this work, proactive simulation–optimization approaches that utilize the information collected during the simulation for guiding the optimization search to provide robust solutions are proposed. Moreover, a multi-objective approach based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for jointly tackling the problem objective as well as the deviations because of stochastic changes is developed. Finally, we also investigate the contribution of time management strategies such as buffers to absorb stochastic modifications and hence increase solutions’ robustness. The computational results indicate, on the one hand, the benefit of integrating both types of objectives (i.e., deterministic and stochastic) to guide the simulation–optimization process, and on the other hand, the benefit of using the multi-objective approaches like NSGA-II. Finally, the incorporation of buffers leads to better performance in terms of reducing penalty costs due to disruptions, shortening the planning risks related to only considering deterministic planning.

Balancing irrigation planning and risk preference for sustainable irrigated agriculture: A fuzzy credibility-based optimization model with the Hurwicz criterion under uncertainty

The challenge of irrigation planning is that it involves various system components, which is further exacerbated by uncertainties and risk preference in making decisions. This study presents a simulation-optimization framework under uncertainty by incorporating fuzzy credibility-based optimization model with the Hurwicz criterion and daily water balance simulation for irrigation planning. The developed methodology can effectively handle fuzzy uncertainty in the objective function and constraints and readily analyze tradeoffs between optimal solutions of irrigation planning and risks associated with the decision-makers’ preferences. It can also derive several physical parameters expressed as water depth from the water balance of root zone for determining optimal irrigation water amount. The Hurwicz criterion, fuzzy credibility constraints and other irrigation-related constraints are imposed on the study system through the developed framework. Then, its applicability is illustrated with a real case study in the Jiefangzha irrigation subarea in the Hetao Irrigation District, northwest China, which is an arid area. Optimal results using three risk-related parameters, i.e., nine preference weights (λ = 0.1, 0.2, … and 0.9) of objective function, and three credibility levels (α = 0.7, 0.8 and 0.9, β = 0.7, 0.8 and 0.9) of fuzzy constraints, have been generated for supporting irrigation planning. Based on these outcomes, system benefits resulting from agricultural production have a slight growth with preference weights increase from 0.1 to 0.9, showing that optimistic criterion plays a decisive role in optimal irrigation planning solutions, especially when preference weights are greater than 0.7. Daily groundwater depth is presented in the given 81 scenarios for comparison, showing the usefulness of the developed framework in practical applications. Therefore, these findings are helpful for supporting irrigation planning and balancing tradeoffs between irrigation planning and risk preference for sustainable irrigated agriculture.

Operational Planning of Energy for Non-Interconnected Zones: A Simulation-Optimization Approach and a Case Study to Tackle Energy Poverty in Colombia

There is a need to look for alternative sources of renewable energy, especially in zones where people continue to live under energy poverty conditions. Consequently, to enhance the performance of energy systems, algorithms to support planning decisions are required. This article proposes a simulation-optimization framework to solve the stochastic version of the integrated energy dispatch and unit commitment problem for a solar radiation system operating in non-interconnected zones. Our study was motivated by challenges faced by a rural school located in Cundinamarca, Colombia. Particularly, a simulation with optimization-based iterations approach is used, modeling solar radiation as a random variable. The optimization phase uses a heuristic procedure that enables good solutions to be found in short computational times. To test our method, computational experiments were conducted using a set of randomly generated cases. The results suggest that our approach is useful and able to handle the random nature of the process for the school “Volcanes”. Additionally, we were able to quantify the impact that using a deterministic approach has on service levels for such systems. The novelty of the article lies in the proposed method and its application to a rural school with a low-budget system.

Optimizing Energy Savings for a Fleet of Commercial Autonomous Trucks

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically.

Optimal reservoir operation – A climate change adaptation strategy for Narmada basin in central India

The potential impacts of climate change on the water resources of the Narmada basin in central India has been investigated using the Soil and Water Assessment Tool (SWAT). The existing dams in the river basin have been incorporated in the model setups, calibration and validation. The COordinated Regional climate Downscaling EXperiment datasets for South-Asia (CORDEX-SA) at 0.5° × 0.5° resolution for four-time horizons, viz., 1970–05 (historical), 2006–40 (near-term), 2041–70 (mid-term) and 2071–99 (end-term) under Representative Concentration Pathways (RCP) scenarios, RCP4.5 and RCP8.5 has been used to investigate the changes in the future climate and simulation of future streamflow. The proposed dams have also been incorporated for modeling the future developmental scenarios. The scenario analysis based on the projected climate variables has led to the inference that the change in the precipitation pattern coupled with the warming trends, maybe contributing towards higher variability in water availability. A future scenario of lower water availability and higher water demands thus calls for optimal utilization of available water resources in the future, so that the higher water demands can be satisfied with lower anticipated future flows. Various alternatives were explored for devising adaptation strategies using the engineering/technical solutions in which the optimal water resources management approaches were explored using the simulation-only and the genetic algorithm based simulation–optimization approaches. The simulation–optimization framework based integrated reservoir operation of four reservoirs has led to better reservoir performance and the number of irrigation failures has decreased substantially from 92 to 12 during 2006–40, 86 to 22 during 2041–70 and 89 to 10 during 2071–99. The hydropower failures have also decreased considerably from 202 to 96 during 2006–40, 192 to 28 during 2041–70 and 179 to 67 during 2071–99 under the RCP8.5 scenario. There were no failures in meeting the domestic water supply and environment flow demands. This may be an important adaptation measure to address the issues of climate change impacts on the water resources in the future in the Narmada basin.

Flood evacuation during pandemic: a multi-objective framework to handle compound hazard

The evacuation of the population from flood-affected regions is a non-structural measure to mitigate flood hazards. Shelters used for this purpose usually accommodate a large number of flood evacuees for a temporary period. Floods during a pandemic result in a compound hazard. Evacuations under such situations are difficult to plan as social distancing is nearly impossible in the highly crowded shelters. This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter’s stay. To the best of our knowledge, such a problem is yet to be explored in literature. Here we develop a simulation-optimization framework, where multiple objectives are handled with a max–min approach. The simulation model consists of an extended Susceptible—Exposed—Infectious—Recovered—Susceptible model. We apply the proposed model to the flood-prone Jagatsinghpur district in the state of Odisha, India. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. At the same time, this does not result in an uncontrolled number of new infections. The proposed approach can generalize to different regions and can provide a framework to stakeholders to manage conflicting objectives in disaster management planning and to handle compound hazards.

Stochastic optimization using grey wolf optimization with optimal computing budget allocation

Stochastic optimization problems exist widely in many manufacturing and service systems. Due to the stochastic nature, these problems usually have no analytical solutions and are difficult to solve. This research proposes a hybrid approach that integrates the grey wolf optimization algorithm and the simulation optimization framework. In this hybrid approach, the grey wolf optimization algorithm is used to search for candidate solutions from the solution space, while the simulation helps the algorithm to identify the desired solutions such that the search is guided to more promising regions. To enhance the efficiency of simulation, this work designs a computing budget allocation rule that helps the grey wolf optimization algorithm to select the elite candidate solutions in each iteration. The proposed computing budget allocation rule is then integrated with the grey wolf optimization algorithm to solve stochastic optimization problems. Numerical experiments confirm that the proposed computing budget allocation rule performs better than extant allocation rules, and can find the better solution for stochastic optimization problems using fewer iterations by integration with the grey wolf optimization algorithm.

Integrated Simulation-Optimization Framework for Water Allocation Based on Sustainability of Surface Water and Groundwater Resources

The aim of this study is to develop a framework for the optimal conjunctive use of surface and groundwater in a case study of the Zayandehrud river basin in Iran. The basis of the framework is an integrated simulation-optimization model that can be used to optimize a number of decisions, including (1) the operation of a reservoir, (2) allocations of water to irrigation canals, and (3) withdrawals from the river and groundwater sources. A water evaluation and planning system (WEAP) model is coupled with the nondominated sorting genetic algorithm II (NSGA-II) optimization algorithm to optimize the following two criteria: (1) a fuzzy sustainability criterion, and (2) the normalized shortfall of supply compared to demand. Each point on the generated Pareto front corresponds to a set of releases from the reservoir and allocations from surface and groundwater resources to the various agricultural regions. A compromise solution is selected from the Pareto front using an established method. The total sustainability of the compromise solution is 26% higher than that obtained previously by using the simulation model alone. It also represents an increase in the sustainability of the reservoir by 37% and the sustainability of the aquifers by 16%.

GA-based implicit stochastic optimization and RNN-based simulation for deriving multi-objective reservoir hedging rules.

Management of reservoir systems is a complicated process involving many uncertainties regarding future events and the diversity of purposes these reservoirs serve; therefore, an effective management of these systems could help improve resource utilization and avoid stakeholder disputes. The aim of this paper was to build an optimization-simulation framework based on implicit stochastic optimization (ISO), genetic algorithms (GA), and recurrent neural network (RNN) for addressing the issue of reservoir operation. Inflow scenarios were generated synthetically based on a monthly scale to be used as an input to a multi-objective genetic programming model to construct an optimal operating rules database. Such database was subsequently used simultaneously with the output of the inflow forecasting model to simulate monthly reservoir hedging rules using RNN. Our results demonstrate the effectiveness of the GA-ISO-RNN model for simulating and predicting optimal reservoir release with consistent accuracy. Results from both the training and testing phases clearly proved the usefulness of RNN in predicting optimal reservoir release with relatively higher values of the Nash-Sutcliffe model efficiency coefficient, correlation coefficient, and lower values of root mean squared error and mean absolute deviation. Furthermore, by comparing the historical releases and the output of the proposed model, the results show that the proposed model was less vulnerable than standard operating rules. The proposed methodology was applied to the Bigge reservoir in Germany, as it features an extensive management infrastructure, but this methodology can also be easily adopted in other similar cases.

A simulation–optimization framework for optimizing response strategies to epidemics

Epidemics require dynamic response strategies that encompass a multitude of policy alternatives and that balance health, economic and societal considerations. We propose a simulation–optimization framework to aid policymakers select closure, protection and travel policies to minimize the total number of infections under a limited budget. The proposed framework combines a modified, age-stratified SEIR compartmental model to evaluate the health impact of response strategies and a Genetic Algorithm to effectively search for better strategies. We implemented our framework on a real case study in Nova Scotia to devise optimized response strategies to COVID-19 under different budget scenarios and found a clear trade-off between health and economic considerations. Closure policies seem to be the most sensitive to policy restrictions, followed by travel policies. On the other hand, results suggest that practising social distancing and wearing masks are necessary whenever their economic impacts are bearable. The framework is generic and can be extended to encompass vaccination policies and to use different epidemiological models and optimization methods.

An Optimization-Simulation Framework for Integrated Inventory and Cash Replenishment Problem of Automated Teller Machines in India

An Optimization-Simulation Framework for Integrated Inventory and Cash Replenishment Problem of Automated Teller Machines in India

Metamodel-based calibration of large-scale multimodal microscopic traffic simulation

Agent-based microscopic traffic simulation models are gaining popularity over traditional trip-based traffic models due to their superiority in modeling household member interaction, car-sharing, vehicle interaction, etc. Calibrating these models is challenging due to the intrinsic complexity, e.g., nonlinearity, high simulation time, and lack of closed-form expressions. With multimodal large-scale networks, this problem becomes even more severe. This paper proposes a metamodel-based simulation optimization framework for calibrating an agent-based multimodal traffic microsimulator. The metamodel serves as a surrogate of the simulation model for faster and more tractable calibration optimization. In particular, a simplified multimodal traffic model is developed and incorporated in the metamodel. This paper also develops and compares three gradient-based metamodel schemes, taking account of the derivatives of link flows with respect to the calibration parameters in the metamodel fitting process. Numerical results on a toy network show that the proposed metamodel incorporating an analytical traffic model is more efficient and highly adaptable compared to the conventional pure polynomial metamodel schemes. Moreover, taking into account gradient information in the metamodels improves the performance of convergence and solution quality. The proposed metamodel-based method is further applied to calibrate the simulation of an actual network in Hong Kong. The results confirm the applicability of the proposed calibration method for large-scale networks. The proposed framework can be applied to other simulation-based optimization problems as well.

Simulation-optimization framework for train rescheduling in rapid rail transit

One of the primary challenges of re-planning in high-speed urban railways is the randomness of disruptive events. In this study, an integrated disturbance recovery model presented in which short-turn and stop-skip service operations are optimized together to minimize the average of passengers’ waiting times. This study develops a discrete-event simulation model that employs a variable neighborhood search algorithm to maintain the service level under infrastructure elements’ unavailability. Due to the unpredictable nature of the incidents, the uncertainty associated with obstruction duration is experimentally analyzed through probabilistic scenarios. The computational experiments are conducted on some test cases of the Tehran Metropolitan Network, and the benefits of the combined control strategy are justified. The outcomes validate the superior performance of the proposed simulation-optimization method over existing state-of-the-art methods. The optimal solutions provide urban rail companies with robust decision options where the maximum recoverability resulting from rescheduled services are expected. The integrated control policy result can also support the analysis of secondary train delay and timetable deviations. The computational results afford practical insights by showing the strong potential to improve the system's responsiveness by minimizing the random disturbances’ cascading effects.

Identifying remedial solutions through optimal bioremediation design under real-world field conditions

Over more than a century of intense industrial production and associated accidental release, petroleum products (e.g., gasoline, diesel, fuel oil) have contaminated a significant portion of the world's groundwater resources. Groundwater remediation is generally a complex task, especially where aquifers and the associated contaminant distribution are highly heterogeneous. The ability to predict the efficiency of such remediation is of crucial importance, as the costs are strongly linked to the treatment design and duration. In this study, a coupled simulation-optimization (S/O) framework, consisting of a process-based reactive transport simulation model linked with particle swarm optimization (PSO) was developed. It was subsequently applied for the design of a real-world in situ bio-treatment of a BTEX contaminated aquifer in France. In the application, the optimization framework was used to simultaneously determine optimal well locations and their optimal injection rates, both constituting key elements of the enhanced biodegradation design problem. The optimization of the treatment efficiency was examined in terms of three different regulatory objectives, (1) minimization of the residual NAPL mass of the key contaminant, i.e., benzene, in the source zone, (2) reduction of the maximum concentration of benzene in groundwater, and (3) minimization of the time required to reduce the benzene concentration in groundwater to below a threshold value. Our analysis of potential, optimal remediation strategies showed that: (i) the complexity of the biodegradation behavior at real sites may favor very different remediation options as a result of varying remediation targets, (ii) the long term behavior of the contaminants after the end of the active treatment period, which is often neglected, showed to have a significant influence on remediation design that requires increased attention, (iii) PSO has shown to be a very efficient algorithm in the context of the present study. The insights that can be gained from such a framework will provide decision support to select the most suitable remediation strategy while facing different regulatory objectives.

Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network

Here we study a short-sea feeder network design problem based on mother and daughter vessels. The main feature of the studied system is performing transshipment of cargo between mother and daughter vessels at appropriate locations at sea. This operation requires synchronization between both types of vessels as they have to meet at the same location at the same time. This paper studies the problem of designing a synchronized feeder network, explicitly accounting for the effect of uncertain travel times caused by harsh weather conditions. We propose an optimization-simulation framework to find robust solutions for the transportation system. The optimization model finds optimal routes that are then evaluated by a discrete-even simulation model to measure their robustness under uncertain weather conditions. This process of optimization simulation is repeated until a satisfactory condition is reached. To find even better solutions, we include different performance-improving strategies by adding robustness during route generation or exploiting flexibility in sailing speed to recover from delays. We apply the solution method to a case based on realistic data from a Norwegian shipping company. The results show that the method finds near-optimal solutions that offer robustness against schedule perturbations due to harsh weather. They also highlight the importance of considering uncertainty when designing a short-sea feeder network with transshipment at sea.

Implications of uncertainty in inflow forecasting on reservoir operation for irrigation

Accurate and reliable forecasting of reservoir inflows is crucial for efficient reservoir operation to decide the quantity of the water to be released for various purposes. In this paper, an artificial neural network (ANN) model has been developed to forecast the weekly reservoir inflows along with its uncertainty, which was quantified through accounting the model’s input and parameter uncertainties. Further, to investigate how the effect of uncertainty is translated in the process of decision making, an integrated simulation–optimization framework that consists of (i) inflow forecasting model; (ii) reservoir operation model; and (iii) crop simulation model was developed to assess the impacts of uncertainty in forecasted inflow on the irrigation scheduling and total crop yield from the irrigation system. A genetic algorithm was used to derive the optimal reservoir releases for irrigation and the area of irrigation. The proposed modeling framework has been demonstrated through a case example, Chittar river basin, India. The upper, lower, and mean of forecasted inflow from the ANN model were used to arrive at the prediction interval of the depth of irrigation, total crop yield, and area of irrigation. From the analysis, the ANN model forecast error of ± 69% to the mean inflow was estimated. However, the error to mean value of simulation for total irrigation, total yield, and area of irrigation was ± 13.3%, ± 6.5%, and ± 4.6%, respectively. The optimizer mainly contributed to the reduction in the errors (i.e., maximizing the total production with the optimal water releases from the reservoir irrespective of inflow to the reservoir). The results from this study suggested that the information on the uncertainty quantification helps in better understanding the reliability of the systems and for effective decision making.

A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong

Overcrowding at emergency departments in Hong Kong has been a critical issue for hospital managers recently. In this study, we focus on optimizing the medical staff configuration to alleviate overcrowding. According to the service requirements proposed by the Hong Kong government, 90% of urgent patients should receive treatment within 30 min. However, this condition is rarely satisfied in the practical situation. Therefore, we formulate the problem as minimizing the proportion of urgent patients that violate the service requirements while satisfying the service requirements of the other categories and cost constraints, thereby resulting in an optimization problem with a stochastic objective and several stochastic constraints. To solve this problem efficiently, we proposed a multi-fidelity simulation optimization framework containing a low- and a high-fidelity process. We utilize an evolutionary algorithm with violation-constrained handling assisted by a surrogate model as a low-fidelity process to shrink the solution space and generate an elite population. In the high-fidelity process, we exploit the optimal computing budget allocation method to identify the best solution in the elite population based on a data-driven simulation model. A case study is also discussed, and the results demonstrated that with a limited labor cost, there is a 52.05% reduction on average in the waiting time of urgent patients. Meanwhile, our proposed multi-fidelity simulation optimization framework proves to save 98.4% of the simulation time.