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This paper presents a novel solution approach for a variant of the job shop scheduling problem with machine unavailability due to both condition-based preventive maintenance and corrective maintenance following random breakdowns. We first provide an exact mathematical formulation of the problem under simplifying assumptions, namely that the number of breakdowns for each job position on each machine is known, the degradation rates are fixed, and the preventive and corrective maintenance durations are deterministic parameters. Moreover, to handle the more realistic case of stochastic machine degradation, random breakdowns, and uncertain maintenance durations, a simulation-optimisation algorithm is proposed. The real makespan function is first approximated using multiple surrogate measures, which are optimised through independent genetic algorithms. Then, the fittest solutions obtained from these surrogate measures are simulated, and the best among them is added to an elite list, which is included in the genetic algorithms' populations for the next iteration. Schedule robustness is ensured by using an objective function that consists of the weighted average of the expected makespan and its 90th percentile. Furthermore, to reduce the likelihood of falling into a local optimum, a stopping criterion based on simulated annealing is implemented. Numerical experimentation on extended benchmark instances confirmed the validity of the mathematical formulation and the favourable performance of the proposed simulation-optimisation algorithm in terms of computational time and solution quality. [ABSTRACT FROM AUTHOR]

The confined aquifer dewatering for long-deep excavations usually encounters challenges due to complicated geotechnical conditions, large excavation sizes, and high hydraulic pressures. To propose the most efficient scheme of confined aquifer dewatering for long-deep excavations, dewatering optimizations were performed using the simulation–optimization method. An open cut tunnel of the Jiangyin-Jingjiang Yangtze River Tunnel Project was taken as an example. The methods of finite element and linear programming (LP) were combined to optimize the dewatering process. A three-dimensional finite element model was developed. After simulating the pumping tests, hydraulic conductivity was inverted. Then, necessary parameters in the LP method were determined by simulating dewatering with each pumping well, and various LP models were developed based on some important influence factors such as dewatering sequence, considered pumping wells, and pumping rate limitation. Finally, the optimal pumping rates were solved and applied to the numerical model, with induced drawdown and ground settlement computed for comparison. The results indicate that the optimization can significantly reduce the required wells in the original design. Dewatering in the deepest zone exhibits the highest efficiency for long-deep excavations with gradually varying depths. For the dewatering sequence from the shallowest to the deepest zone, more pumping wells are required but less energy is consumed. Higher quantity and more advantageous locations of pumping wells in the LP model usually result in lower total pumping rate, drawdown, and ground settlement. If more pumping wells are considered in the deepest zone, pumping rate limitation of single well will only slightly increase the total pumping rate, number of required pumping wells, drawdown, and ground settlement.

Blood supply chains (BSCs) face challenges in managing the production and inventory of blood components. We developed a simulation-optimisation approach to address inventory of blood products in blood supply chains. This approach can determine inventory size and replenishment points for blood products that balances supply and demand, while maintaining acceptable levels of wastage and shortage. A simulation methodology is used to represent the complexities in the inventory management of blood components. An optimisation module, coupled with the simulation, is used to address constraints related to waste, shortages, and inventory levels. The approach was verified and validated using a case study conducted in a blood bank in Brazil managing a supply of Red Blood Cells (RBCs). The approach proved to be suitable for the case analyzed and results suggest that 120 units is adequate to meet the demand for RBCs, while maintaining acceptable levels of shortages and wastages. [ABSTRACT FROM AUTHOR]

Exit‐choice behavior significantly influences the performance of egress operations in crowded spaces. Thus, strategically issuing active exit‐choice recommendations that provide clear and concise guidance to pedestrians can help improve evacuation safety and efficiency. This study investigates how to optimally regulate pedestrian density at the exits by generating time‐multiplexed exit‐choice recommendations. The study develops a simulated crowd evacuation scenario with multiple exits, where pedestrians receive periodic sequences of exit‐choice instructions. The optimal period for each instruction is obtained through simulation‐optimization processes with three minimization objectives: evacuation time, cumulative density, and cumulative density variance. First, the effect of collective behaviors on evacuation performance is studied, which justifies the need for active mechanisms to improve exit‐choice decision‐making coordination. Then, several optimal plans for the different objectives are obtained and evaluated regarding evacuation time and cumulative density performance. Finally, the influence of adoption rate and collective commitment to instructions are evaluated. This study suggests that static plans, defined as time‐multiplexed exit‐choice instructions obtained through simulation‐optimization, reasonably represent a first step in the real deployment of intelligent crowd management systems. [ABSTRACT FROM AUTHOR]

Abstract Conjunctive use management of water resources is of paramount importance in the backdrop of the growing demand for freshwater resources. With this viewpoint, this study is aimed to present a methodological approach utilizing the combined use of simulation and optimization models for evolving strategies or policies for the management of water resources in a well-field connected to a stream. The proposed methodology incorporates the coupling of the groundwater simulation model with the optimization model for decision-making through the response matrix method. In this study, a numerical groundwater flow model using MODFLOW was used to simulate the response of stream-aquifer interactions. A linear optimization model was formulated to account for the best optimal groundwater withdrawal taking into consideration the allowable drawdown, permissible stream flow depletion, and satisfaction of the water demands. Such management options permit the investigation of alternative strategies for groundwater abstraction in a well-field hydraulically connected to a stream for sustainable development of water resources. The model was applied in a hypothetical well field study area hydraulically connected to a stream and satisfactory results were obtained. The methodology was also very important to understand the tradeoff between groundwater withdrawal and the depletion of flow to the nearby stream. This approach was applied to the Aynalem well field stream-aquifer system which is located in Northern Ethiopia. Results show that groundwater extraction from the Aynalem well field triggers stream flow depletion and it is possible to decrease the stream flow depletion by 3.5–4.5% by adjusting current groundwater withdrawal schedules and configuration of existing wells.

Conjunctive use management of water resources is of paramount importance in the backdrop of the growing demand for freshwater resources. With this viewpoint, this study is aimed to present a methodological approach utilizing the combined use of simulation and optimization models for evolving strategies or policies for the management of water resources in a well-field connected to a stream. The proposed methodology incorporates the coupling of the groundwater simulation model with the optimization model for decision-making through the response matrix method. In this study, a numerical groundwater flow model using MODFLOW was used to simulate the response of stream-aquifer interactions. A linear optimization model was formulated to account for the best optimal groundwater withdrawal taking into consideration the allowable drawdown, permissible stream flow depletion, and satisfaction of the water demands. Such management options permit the investigation of alternative strategies for groundwater abstraction in a well-field hydraulically connected to a stream for sustainable development of water resources. The model was applied in a hypothetical well field study area hydraulically connected to a stream and satisfactory results were obtained. The methodology was also very important to understand the tradeoff between groundwater withdrawal and the depletion of flow to the nearby stream. This approach was applied to the Aynalem well field stream-aquifer system which is located in Northern Ethiopia. Results show that groundwater extraction from the Aynalem well field triggers stream flow depletion and it is possible to decrease the stream flow depletion by 3.5–4.5% by adjusting current groundwater withdrawal schedules and configuration of existing wells. [ABSTRACT FROM AUTHOR]

Capacity planning should be performed to balance investment costs and benefits of investing to meet the current and future demand in intensive care units. Having a high capacity to increase patient admission will lead to unutilized capacity in some periods, thereby increasing costs. On the other hand, patient admission requests from inborn and transported patients might be rejected due to lack of equipment. It should be considered in terms of cost-effectiveness and patient health; therefore, optimal equipment capacity must be determined. In this study, the optimal capacity planning problem has been considered for the neonatal intensive care unit of a hospital adopting the simulation-optimization approach. A discrete event simulation model is proposed for a neonatal intensive care unit in Adana, Turkey. Then, the optimization model identified the optimal numbers of incubators, ventilators, and nitric oxide devices to maximize equipment efficiency and minimize total inborn patient rejection and transport ratios. Three different resource allocations are presented, and the best is obtained from these three objectives as 72 incubators, 35 ventilators, and three nitric oxide devices. The application results obtained have revealed that the rejection and transport rate, which is found to be 1.12% in the current situation, can be reduced to 0.2% with different numbers of equipment and that equipment efficiency can be achieved with optimal quantities of each equipment. The results of the study can help the decision-makers when minimum transport and rejection ratios are critical which almost all intensive care units are required. Furthermore, the proposed simulation-optimization model can be adapted to different neonatal intensive care units having the same characteristics. [ABSTRACT FROM AUTHOR]

Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. The production machines have degradation levels from as-good-as-new to the breakdown state. The failures increase the production machine's degradation level, and maintenance activities change the status to the initial state. Also, the quality of the final product depends on the level of degradation of the machines and the correlation between the degradation level of the production machines and the product's quality in the case that high degradation of the previous production machines leads to a high probability to produce wastage by the following machines is considered. The production system studied in this research has been modeled using the agent-based simulation, and the Reinforcement Learning (RL) algorithm has obtained the optimal integrated policy. The goal is to find an integrated optimal policy that minimizes production costs, maintenance costs, inventory costs, lost orders, breakdown of production machines, and low-quality production. The meta-heuristic technique evaluates the joint policy obtained by the decision-maker agent. The results show that the acquired joint policy by the RL algorithm offers acceptable performance and can be applied to the autonomous real-time decision-making process in manufacturing systems. [ABSTRACT FROM AUTHOR]

The design of the water distribution network (WDN) is very difficult mainly due to the nonlinear relation between head and flow. The distribution network should also be cost-effective. In any simulation–optimization approach, the computational time requirement is very high for very complex problems. The optimization module plays a crucial role in reducing the computational time. This study aims to apply a simulation–optimization approach to designing a WDN. EPANET is selected as the simulation model and Real-Coded Genetic Algorithm (RCGA) is selected as the optimization module. To link the simulation module with the optimization module, a program is written in MATLAB. The developed simulation–optimization approach was applied to two benchmark network problems to check the suitability of the method. The parameters of the RCGA were optimized for the two networks. The computational efficiency of the developed simulation–optimization model is checked based on the number of function evaluations. For both networks, the number of function evaluations to get the optimum network design was less than the number of function evaluations required for other methods mentioned in the literature. HIGHLIGHTS Simulation–optimization approach is adopted for the design of WDNs.; Developed an interface in MATLAB to link simulation and optimization.; EPANET is used as the simulation module and RCGA as the optimization module.; The parameters of Real Coded Genetic Algorithm is optimized.; The developed simulation optimization model is applied to two benchmark problems.;

In the optimal design of groundwater pollution monitoring network (GPMN), the uncertainty of the simulation model always affects the reliability of the monitoring network design when applying simulation–optimization methods. To address this issue, in the present study, we focused on the uncertainty of the pollution source intensity and hydraulic conductivity. In particular, we utilized simulation–optimization and Monte Carlo methods to determine the optimal layout scheme for monitoring wells under these uncertainty conditions. However, there is often a substantial computational load incurred due to multiple calls to the simulation model. Hence, we employed a back-propagation neural network (BPNN) to develop a surrogate model, which could substantially reduce the computational load. We considered the dynamic pollution plume migration process in the optimal design of the GPMN. Consequently, we formulated a long-term GPMN optimization model under uncertainty conditions with the aim of maximizing the pollution monitoring accuracy for each yearly period. The spatial moment method was used to measure the approximation degree between the pollution plume interpolated for the monitoring network and the actual plume, which could effectively evaluate the superior monitoring accuracy. Traditional methods are easily trapped in local optima when solving the optimization model. To overcome this limitation, we used the grey wolf optimizer (GWO) algorithm. The GWO algorithm has been found to be effective in avoiding local optima and in exploring the search space more effectively, especially when dealing with complex optimization problems. A hypothetical example was designed for evaluating the effectiveness of our method. The results indicated that the BPNN surrogate model could effectively fit the input–output relationship from the simulation model, as well as significantly reduce the computational load. The GWO algorithm effectively solved the optimization model and improved the solution accuracy. The pollution plume distribution in each monitoring yearly period could be accurately characterized by the optimized monitoring network. Thus, combining the simulation–optimization method with the Monte Carlo method effectively addressed the optimal monitoring network design problem under uncertainty. [ABSTRACT FROM AUTHOR]

A Genetic Algorithm technique, linked with a numerical solver model (Finite Elements) (GA-FEM), has been evolved to compute the optimal hydraulic design for a safe hydraulic structure to handle seepage concerns. The minimum cost of construction was considered the objective function during the formulation of the optimization process. While the major constraints were represented by the uplift pressure beneath the base floor and the exit gradient of seepage flow. The GA-FEM approach proposed in this study addresses the criteria of optimal hydraulic design in two main steps. First, the seepage problem was analyzed using a verified numerical model coded in finite-element programming. Then the Genetic Algorithm technique is used to address the optimal solution model and obtain the optimum (minimum construction costs) location and depth for the cut-offs under hydraulic structures. As a simulation/optimization (S–O) model, MATLAB platform code was utilized to construct a connection between the FEM and the optimization technique. The results refer to the developed GA-FEM model that can yield efficient, economical, and safe hydraulic designs for the cut-offs. The assessment of the GA-FEM proposed model indicated a good agreement between the simulation and expected results for seepage characteristics under the hydraulic structure. [ABSTRACT FROM AUTHOR]

Stochastic, as well as fuzzy uncertainty, can be found in most real‐world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision‐makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best‐known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks. [ABSTRACT FROM AUTHOR]

This study focuses on the optimization of consequence management actions in the urban water distribution network. The EPANET simulation model is employed in combination with the multi-objective modified seagull optimization algorithm (MOMSOA) based on archives for a more efficient optimization process. Two objective functions are developed: minimizing reactive activities (cost reduction) and minimizing consumed pollution mass. The utilization of shut-off valves and hydrants for isolating the network and discharging pollution is explored. Without consequence management, 84.5 kg of pollution is consumed. With 18 reactive activities, pollution consumption was reduced to 59.8 kg. Also, to compare the proposed method with other algorithms, the interaction curve between reactive activities and the amount of pollutant mass consumed was obtained using other methods, including MOSOA, NSGA-II, MOPSO, and MOSMA. According to the obtained curve, the proposed method performed better in reducing the mass of consumed pollution. Extracting optimal activities using MOMSOA and a maximum of 18 activities takes about 80 min. The MOMSOA with archive technique significantly shortens this time for real-time consequence management. The proposed approach demonstrates that increasing the archive population decreases the extraction time of interaction curves between objectives by up to 60 %. A small archive capacity slightly increases the time required to extract optimal activities due to searching for similar solutions. However, utilizing the archive capacity enables real-time optimization and consequence management in the network.

The scientific layout design of the groundwater pollution monitoring network (GPMN) can provide high quality groundwater monitoring data, which is essential for the timely detection and remediation of groundwater pollution. The simulation optimization approach was effective in obtaining the optimal design of the GPMN. The ant colony optimization (ACO) algorithm is an effective method for solving optimization models. However, the parameters used in the conventional ACO algorithm are empirically adopted with fixed values, which may affect the global searchability and convergence speed. Therefore, a parameter-iterative updating strategy-based ant colony optimization (PIUSACO) algorithm was proposed to solve this problem. For the GPMN optimal design problem, a simulation–optimization framework using PIUSACO algorithm was applied in a municipal waste landfill in BaiCheng city in China. Moreover, to reduce the computational load of the design process while considering the uncertainty of aquifer parameters and pollution sources, a genetic algorithm-support vector regression (GA-SVR) method was proposed to develop the surrogate model for the numerical model. The results showed that the layout scheme obtained using the PIUSACO algorithm had a significantly higher detection rate than ACO algorithm and random layout schemes, indicating that the designed layout scheme based on the PIUSACO algorithm can detect the groundwater pollution occurrence timely. The comparison of the iteration processes of the PIUSACO and conventional ACO algorithms shows that the global searching ability is improved and the convergence speed is accelerated significantly using the iteration updating strategy of crucial parameters. This study demonstrates the feasibility of the PIUSACO algorithm for the optimal layout design of the GPMN for the timely detection of groundwater pollution. [ABSTRACT FROM AUTHOR]

Increasing uncertainties in the supply chains have caused more attentions to the supply chain risk management approaches. Because of the inherent turbulences in the international transactions, these uncertainties in the global context are more important. On the other hand, due to competitive pressures, businesses has been prepared themselves to operate in a global context to take advantage of the international markets. In addition, supplier selection is a challenge for purchasing managers by having more uncertainties in supply from the foreign supplier (exchange rate risk, extended lead times, regional risks). On the other hand, lower price procurement and having more diversified suppliers are the benefits that a company could obtain from global supply chains. In this paper a scenario based supply chain model for global purchasing of substitutable products is introduced and as a solution method a simulation-optimization approach is proposed. The model is applied on the modified data adopted from a case study and sensitivity analyzes (on the risk attitude of retailers, product substitutability and exchange rate) are presented for different amounts of parameters. [ABSTRACT FROM AUTHOR]

Using a discrete-event simulation (DES) model, the current disaster plan regarding the allocation of multiple injured patients from a mass casualty incident was evaluated for an acute specialty hospital in Vienna, Austria. With the current resources available, the results showed that the number of severely injured patients currently assigned might have to wait longer than the medically justifiable limit for lifesaving surgery. Furthermore, policy scenarios of increasing staff and/or equipment did not lead to a sufficient improvement of this outcome measure. However, the mean target waiting time for critical treatment of moderately injured patients could be met under all policy scenarios. Using simulation-optimization, an optimal staff-mix could be found for an illustrative policy scenario. In addition, a multiple regression model of simulated staff-mix policy scenarios identified staff categories (number of radiologists and rotation physicians) with the highest impact on waiting time and survival. In the short term, the current hospital disaster plan should consider reducing the number of severely injured patients to be treated. In the long term, we would recommend expanding hospital capacity—in terms of both structural and human resources as well as improving regional disaster planning. Policymakers should also consider the limitations of this study when applying these insights to different areas or circumstances. [ABSTRACT FROM AUTHOR]

Contaminants in groundwater may enter through various sources which are required to be identified for informed decision-making regarding remediation. In early stages of contamination of an aquifer, the sources of contamination are generally unknown. To estimate the unknown release histories at potential sources, the governing equations of contaminant transport are solved backwards in time which renders the inverse problem as ill-posed. Furthermore, due to measurement or operational errors, observed breakthrough curves can be noisy which can induce uncertainty. Simulation-optimization (SO) techniques are generally used for solving the inverse model. Nevertheless, swarm population-based optimization algorithms may get trapped in local optima in the nonlinear search space related to the problem. In this study, SO model based on meshless radial point collocation method (RPCM) and multiverse optimizer (MVO) is proposed. MVO implements gradual transformation from global exploration to local exploitation phase which helps in better estimation of unknowns. The performance of RPCM-MVO-SO model is compared with two other SO models developed by combining RPCM with grey wolf optimizer and particle swarm optimization in two case studies. The proposed model performs better compared to the other two SO models considered which suggests the efficacy of the model for release history estimation in groundwater. HIGHLIGHTS A novel RPCM-MVO-SO model is proposed for contaminant source identification.; The RPCM-MVO model performs better compared to RPCM-PSO and RPCM-GWO for the two case studies considered.; RPCM-MVO model estimated release intensities have low variations for noisy observations.;

This study develops a factorial Bayesian least-squares support vector machine-based energy–water–environment nexus system optimization (i.e., FBL–EWEO) model. FBL–EWEO can provide dependable predictions for electricity demand, quantify the interactions among different factors, and present optimal system planning strategies. The application to Fujian Province is driven by three global climate models (i.e., GCMs) under two SSPs, as well as two levels of economic and social factors' growth rates. Results revealed in the planning horizon: (1) Fujian would encounter rainy and warming trends (e.g., [2.17645, 4.51247] mm/year of precipitation and [0.0072, 0.0073] °C/year of mean temperature); (2) economic, social, and climatic factors contribute 62.30%, 35.50%, and 1.47% to electricity demand variations; (3) electricity demand would grow with time (increase by [64.21, 74.79]%); (4) the ratio of new energy power would rise to [70.84, 73.53]%; (5) authorities should focus on photovoltaic and wind power plants construction (their proportions increase from [0.81, 1.83]% to [9.14, 9.56]%, [1.33, 4.16]% to [11.44, 15.58]%, respectively); and (6) air pollutants/CO2 emissions would averagely decline [51.97, 53.90]%, and water consumption would decrease [41.77%, 42.25]%. Findings provide technical support to sustainable development. [ABSTRACT FROM AUTHOR]

Hydropower projects involve enormous investments that require an efficient cost–benefit framework and optimization model for proper development. Dams and hydropower plants have many impacts on the environment. These environmental impacts are often not included in the economic calculations and planning of the projects, which leads to the loss of natural resources. The primary purpose of this research is to incorporate environmental impacts into optimization and decision-making. A comprehensive simulation–optimization model is developed to optimize hydropower decisions. The positive and negative values of environmental impacts are incorporated into an economic objective function under different scenarios, and optimal design was done for each scenario. The results show that considering environmental economics affects the multipurpose hydropower project's NPV and decision outcomes. Considering environmental impacts compared to not considering them has reduced NPV of the project by 13.9%. The results emphasize the importance of including these impacts to achieve sustainable development and management. HIGHLIGHTS Environmental impacts (EIs) are included in the multipurpose hydropower project optimization problem.; Two different methods (hydrological method and habitat condition of indicator species) are employed for environmental flow assessment.; Considering EI is effective on decision variables and optimal energy production and agriculture development.;

Due to climate change, increased regulation of water resources and competition from other beneficial uses, the agricultural sector is under pressure to use water more efficiently. This paper reports the field evaluation of two model-based simulation-optimization approaches for irrigation scheduling: deterministic optimization and stochastic optimization. The field experiments were conducted at the University of California Davis research farm with a processing tomato crop. The crop growth simulation model used in the study was DSSAT-CROPGRO processing tomato. In order to mitigate the impact of weather forecasts inaccuracies, irrigation schedules were updated every 5 to 10 days, depending on operational constraints. These updates were performed via custom graphical user interfaces that enabled the user to visualize the expected outcomes of various decisions or scenarios before choosing which irrigation schedule to implement. An irrigation treatment based on manual monitoring of soil water content with a field-calibrated neutron probe served as benchmark. The model-based treatments achieved yields and water use efficiencies that were not significantly different from those obtained in the neutron probe-based treatment. These results demonstrate the high potential of model-based simulation-optimization approaches for in-season adaptive irrigation scheduling, especially since neutron probes, which are considered one of the most accurate indirect methods of measuring soil water content, are not commonly used by growers.

Reducing the quantity of water in recent years has increased the competition between development projects and the environment. Wetlands are increasingly under pressure due to human activities. The most serious threats to wetlands are excessive agriculture and the diversion of water for irrigation. In recent years, due to water shortage and drought, wetland dryness in Iran has caused many problems, including the dust crisis. Therefore, planning at the basin scale is necessary to achieve sustainable development, which emphasizes the employment of mathematical models. In this study, using a reliability-based simulation–optimization approach, development planning in the Karkheh basin with the following two objectives is investigated: (1) total area under cultivation of agricultural development sectors and (2) supply reliability of the environmental flow requirement. The Water Evaluation and Planning (WEAP) model is used for the simulation of water resources and the multi-objective particle swarm optimization (MOPSO) algorithm is employed for optimization. The results show that in addition to significantly improving the supply reliability of the wetland requirement (from 55 to 79%), the design of agricultural development projects has been optimized. The reliability-based model has prevented unsustainable developments in the basin. Also, the average supply reliability of agricultural demands has increased from 51% (in previous studies) to 72%. HIGHLIGHTS A long-term multi-objective optimization model is employed.; The model proposed in this study is reliability-based.; The optimal trade-off between the environment and agriculture goals is achieved.; The supply reliability of the wetland requirement is significantly increased.; The model leads to the sustainable development of agricultural projects.;

This paper addresses a parallel machine scheduling problem with non-anticipatory family setup times and batching, considering the task's stochastic processing times and release dates. The problem arises from a real-life ship scheduling problem in the oil and gas industry. We developed an Iterated Greedy simheuristic with built-in Monte Carlo Simulation to sample the stochastic parameters. We conducted experiments on a set of instances from the literature, considering two simheuristic variants and three uncertainty levels for the stochastic parameters. To highlight the advantages of using simulation to tackle the stochastic problem, the simheuristics are compared against a regular Iterated Greedy metaheuristic, yielding an improvement of up to 16.5% on the objective function's expected values, with a reduced impact on computational times. During a risk analysis, the Pareto set of solutions is generated to illustrate the trade-off between the expected objective value of the solutions and the conditional value at risk, providing decision-makers with a useful tool to select the schedules that better fit their risk profiles. We use an iterative mechanism to build confidence intervals within a certain confidence level during the method's simulation step, interrupting the procedure when it reaches the desired error. This strategy's advantage is highlighted in the computational experiments, which indicates that the number of replications of the simulation is instance and uncertainty level dependent. A periodic re-planning strategy is also used to evaluate the performance of the simheuristic, highlighting the advantages of using the proposed algorithm in a real-life usage situation. [ABSTRACT FROM AUTHOR]

The magnitude of the recent forest fires, the time required to extinguish them, and the damage they caused have attracted the attention of all humanity. If the current trend continues, it will cause great irreversible losses. There is a great need for scientific studies to prevent or reduce the damages of these fires. In this context, this paper proposes algorithms and mathematical models for generating the routes of unmanned aerial vehicles to detect forest fires that may occur especially in regions far from residential areas. A novel heuristic dispatching rule and a simulation-based optimization algorithm are proposed. The striking features of the proposed algorithms are that the routes are created with a focus on minimizing the fire probabilities. The uncertainties and dynamics of real-life are also considered. Various scenarios have experimented on a realistic case. Experimental results and findings are promising. [ABSTRACT FROM AUTHOR]

Hydropower projects involve enormous investments that require an efficient cost-benefit framework and optimization model for proper development. Dams and hydropower plants have many impacts on the environment. These environmental impacts are often not included in the economic calculations and planning of the projects, which leads to the loss of natural resources. The primary purpose of this research is to incorporate environmental impacts into optimization and decision-making. A comprehensive simulation-optimization model is developed to optimize hydropower decisions. The positive and negative values of environmental impacts are incorporated into an economic objective function under different scenarios, and optimal design was done for each scenario. The results show that considering environmental economics affects the multipurpose hydropower project's NPV and decision outcomes. Considering environmental impacts compared to not considering them has reduced NPV of the project by 13.9%. The results emphasize the importance of including these impacts to achieve sustainable development and management. [ABSTRACT FROM AUTHOR]

• A two-stage adaptive surrogate model improves the surrogate model accuracy. • Trust region GA improves the stability of the contamination source inversion results. • TSASM-TRGA framework is a robust tool to improve the accuracy and reliability of GCSI. The inversion of contamination source characteristics is of great importance to groundwater remediation and risk management. The surrogate model-based simulation-optimization method is the most commonly used groundwater contamination source inversion (GCSI) approach. However, the static surrogate model and the genetic algorithm (GA) suffer from inaccuracy and instability problems when the nonlinearity of the numerical model or the dimension of the variables to be identified increase. In this paper, a two-stage adaptive surrogate model-assisted trust region GA (TSASM-TRGA) framework was developed to improve the accuracy and stability of the complex high dimensional nonlinear GCSI. In this framework, the two-stage adaptive surrogate model was applied to balance the global and local accuracy of the surrogate model, and the trust region GA was adopted to accelerate the convergence and improve the stability of optimal solutions. By comparing the accuracy of the TSASM-TRGA, two-stage adaptive surrogate model-assisted GA (TSASM-GA), one-stage adaptive surrogate model-assisted GA (OSASM-GA), and static surrogate model-assisted GA (SSM-GA) frameworks, the results showed that the identified contamination source locations with TSASM-TRGA framework are more consistent with the actual locations, and the identified contamination source fluxes are closer to the actual ones. The mean relative error (MRE) between TSASM-TRGA framework and the actual values is 0.31%, which is much smaller than that for the other three frameworks. The inversion results with the TSASM-TRGA framework show a higher accuracy than those with the simulation-statistical method. The present study suggests that the proposed TSASM-TRGA framework provides a more effective approach to improve the accuracy and stability of GCSI. [ABSTRACT FROM AUTHOR]

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Using a discrete-event simulation (DES) model, the current disaster plan regarding the allocation of multiple injured patients from a mass casualty incident was evaluated for an acute specialty hospital in Vienna, Austria. With the current resources available, the results showed that the number of severely injured patients currently assigned might have to wait longer than the medically justifiable limit for lifesaving surgery. Furthermore, policy scenarios of increasing staff and/or equipment did not lead to a sufficient improvement of this outcome measure. However, the mean target waiting time for critical treatment of moderately injured patients could be met under all policy scenarios. Using simulation-optimization, an optimal staff-mix could be found for an illustrative policy scenario. In addition, a multiple regression model of simulated staff-mix policy scenarios identified staff categories (number of radiologists and rotation physicians) with the highest impact on waiting time and survival. In the short term, the current hospital disaster plan should consider reducing the number of severely injured patients to be treated. In the long term, we would recommend expanding hospital capacity—in terms of both structural and human resources as well as improving regional disaster planning. Policymakers should also consider the limitations of this study when applying these insights to different areas or circumstances.

To provide health services, hospitals consume electrical power and contribute to the CO2 emission. This paper aims to develop a modelling approach to optimize hospital services while reducing CO2 emissions. To capture treatment processes and the production of carbon dioxide, a hybrid method of data mining and simulation–optimization techniques is proposed. Different clustering algorithms are used to categorize patients. Using quality indicators, clustering methods are evaluated to find the best cluster sets, and then patients are categorized accordingly. Discrete-event simulation is applied to each patient category to estimate performance measures such as number of patients being served, waiting times, and length of stay, as well as the amount of CO2 emission. To optimize performance measures of patient flow, metaheuristic searches have been used. The dataset of Bushehr Heart Hospital is considered as a case study. Based on K-means, K-medoid, Hierarchical clustering, and Fuzzy C-means clustering methods, patients are categorized into two groups of high-risk and low-risk patients. The number of patients being served, total waiting time, length of stay, and CO2 emitted during care processes are improved for both groups. The proposed hybrid method is an effective method for hospitals to categorize patients based on care processes. The problems and the proposed solution approach reported in this study could be applicable to other hospitals, worldwide to help both optimize the patient flow and minimize the environmental consequences of care services. [ABSTRACT FROM AUTHOR]

Nowadays, with the ongoing advancement of social economy and water demands, the value of water resources has increased, and the crop pattern management, in terms of water consumptions and economic benefits has become significantly important. To achieve an optimal crop pattern on a river basin scale, a spatiotemporal agro-hydrologic simulation model, SWAT (Soil and Water Assessment Tool), was coupled to the multi-objective particle swarm optimization algorithm in a simulation-optimization (S-O) framework. The SWAT model of the Seymareh River Basin (SRB), in terms of hydrological and agronomic measures, was calibrated and validated from year 2003 to 2016. Two problems concerning determinations of I) crop area and II) crop type and area, were defined by focusing on increased economic benefit and decreased water consumption, which were constrained to available land and water resources. In Problem I, the crop types were predefined as the current situation in each sub-basin and in Problem II, the crop types were variable. The results indicated that in Problem I, the most water-efficient and economic solutions of Pareto front led to 51.94% and 39.7% lower water consumption, and 23% and 25.69% economic enhancement, respectively, compared to the current crop pattern scenario. In addition, in Problem II, the economic measure in the most water-efficient and economic alternatives increased by 35.7% and 38.7%, and the water saving measure was increased by 68% and 44%, respectively, compared to the existing crop pattern scenario. In this regard, the developed S-O framework would function as an efficient decision-making tool, particularly for river basin scale studies. [ABSTRACT FROM AUTHOR]

Airport ground operations are one of the main causes of late departures. Hence, reliable work plans for ground support resources can help mitigate these delays while limiting the under‐/over‐utilization of equipment. In this paper, we propose a stochastic approach for modeling the routing problems encountered during scheduling of each activity. We embed Monte Carlo simulation within an optimization approach to assess solutions during the search and guide the algorithm toward more robust schedules that account for resource cost. Actual flight arrival data, random service, and travel times are used in the simulation process. Results prove that our method clearly increases the robustness of the obtained schedules and the number of aircraft being handled within the planned time. To further identify potential causes of delay, we propose a reliability analysis based on the study of survival functions for each turnaround activity, combined with the dispersion of the resources' arrival to the parking stand. [ABSTRACT FROM AUTHOR]

With the growth of multinational companies, increasing international and domestic competition between companies, upgrading information technology, and increasing customer expectations, accurate supply chain (SC) planning is essential. In such an environment, pollution has become more severe in recent decades, and with the weakening of the environment and global warming, green SC management strategies have become significant issues in recent decades. In this research, we consider the integrated production and distribution planning problem of a multi-level green closed-loop SC system, which includes multiple recycling, manufacturing/ remanufacturing, and distribution centers. We present a three-level bi-objective programming model to maximize profit and minimize the amount of greenhouse gas emissions. A hierarchical iterative approach utilizing the LP-metric method and the non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the proposed model. The Taguchi approach is applied to find optimum control parameters of NSGA-II. Moreover, Monte Carlo (MC) simulation is applied to tackle uncertainty in demand, and the NSGA-II algorithm is integrated with MC simulation (MCNSGA-II). Through numerical experiments on randomly generated instances, we observe that the average of the relative gaps NSGA-II and LP-metric method are 7%, 7%, and 1% for the three levels, respectively. Furthermore, it is observed that the value of convergence (C) and spacing (S) metrics of the MCNSGA-II algorithm are 0 and 1.82E + 07, which are better than NSGA-II. [ABSTRACT FROM AUTHOR]

The greater flow of containers in global supply chains requires ever-increasing productivity at port terminals. The research found in the literature has focused on optimizing specific parts of port operations but has ignored important features, such as the stack-wise organization of containers in a container ship and port yard and the effects of interconnection among the operations in both places. The objective of this paper is to show the importance of designing an integrated plan of the container relocation problem at the port yard with the stowage planning problem for loading and unloading a ship through ports. Both individual problems are NP-Complete, and exact approaches for each problem are only able to find optimal or feasible solutions for small instances. We describe a simulation-optimization methodology that combines simulation, a genetic algorithm, and a new solution representation based on rules. The test results show that the solution from the integrated plan is mutually beneficial for port yards and ship owners. [ABSTRACT FROM AUTHOR]

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In the current study, a simulation-optimization model is presented using a new metaheuristic optimization algorithm called artificial Jellyfish Search (JS) to develop sustainable saltwater intrusion optimal management strategies for coastal aquifers. The simulation model is based on the finite element method (FEM). The effectiveness of the proposed model is tested by application to the real aquifer system of Miami Beach aquifer, Spain with management decisions of maximizing the total economic benefit and the total pumping rate. The model is also applied to the case study of El-Arish Rafah aquifer, Egypt to maximize the total pumping rate. The results of both cases are compared to that obtained by using genetic algorithm (GA), probabilistic global search Lausanne (PGSL), shuffled frog leaping (SFL) and particle swarm optimization (PSO). The efficiency rate metric and statistical indices are used to assess the performance of the proposed model. For Miami Beach aquifer, an optimal benefit of 24.93 ($/d) was obtained and which is equivalent to that by PSO and SFLA but higher than that of GA and PGSL and with the highest pumping rate of 5544.62 (m3/s). The model also had the highest efficiency rates compared to PSO, PGSL and SFLA. For El-Arish Rafah aquifer, a maximum optimal pumping rate of 55,619.43 (m3/s) is determined. The results show that the model can be considered as an effective and efficient management tool.

• A new approach is proposed for groundwater parameter structure identification. • Parameter structures are determined using the general form of the VT approach. • Minkowski distance is employed to assign the data points into the VT zones. • The heuristic SHALO approach is first applied to the solution of inverse problems. • Model identification performance has significantly enhanced. A new simulation–optimization approach is proposed for solving the inverse parameter structure identification problems in groundwater systems. In the simulation part of the proposed approach, the numerical groundwater flow process is simulated by using MODFLOW. Parameter structures of the aquifer system are identified by partitioning the flow domain into a finite number of zones employing the general form of the Voronoi tessellation (VT). This MODFLOW and VT-based simulation model is then integrated into an optimization model where the SHuffled Ant Lion Optimization approach (SHALO) is used. This work is the first application of SHALO by considering the general form of the VT zonation approach for solving the inverse groundwater parameter structure identification problems. The applicability of the proposed simulation–optimization approach is evaluated on a hypothetical aquifer model in the literature. This evaluation is conducted by solving the same problem using Ant Lion Optimization (ALO) and its improved version, the self-adaptive ALO (saALO) for the same conditions. Furthermore, the identified results are compared with those obtained using harmony search (HS) and hybrid genetic algorithm (hybrid GA) based solution approaches in the literature. The obtained results indicated that the proposed approach provided better identification results than ALO, saALO, HS, and hybrid GA in terms of different evaluation metrics. [ABSTRACT FROM AUTHOR]

This study aims to develop a multi-objective quantitative-qualitative reservoir operation model (MOQQROM) by a simulation-optimization approach. However, the main challenge of these models is their computational complexity. The simulation-optimization method used in this study consists of CE-QUAL-W2 as a hydrodynamic and water quality simulation model and a multi-objective firefly algorithm-k nearest neighbor (MOFA-KNN) as an optimization algorithm which is an efficient algorithm to overcome the computational burden in simulation-optimization approaches by decreasing simulation model calls. MOFA-KNN was expanded for this study, and its performance was evaluated in the MOQQROM. Three objectives were considered in this study, including (1) the sum of the squared mass of total dissolved solids (TDS), (2) the sum of the squared temperature difference between reservoir inflow and outflow as water quality objectives, and (3) the vulnerability index as a water quantity objective. Aidoghmoush reservoir was employed as a case study, and the model was investigated under three scenarios, including the normal, wet, and dry years. Results showed the expanded MOFA-KNN reduced the number of original simulation model calls compared to the total number of simulations in MOQQROM by more than 99%, indicating its efficacy in significantly reducing execution time. The three most desired operating policies for meeting each objective were selected for investigation. Results showed that the operation policy with the best value for the second objective could be chosen as a compromise policy to balance the two conflicting goals of improving quality and supplying the demand in normal and wet scenarios. In terms of contamination mass, this policy was, on average, 16% worse than the first policy and 40% better than the third policy in the normal scenario. In the wet scenario, it was, on average, 55% worse than the first policy and 16% better than the third policy. The outflow temperature of this policy was, on average, only 8.35% different from the inflow temperature in the normal scenario and 0.93% different in the wet scenario. The performance of the developed model is satisfactory. • The MOFA-KNN was expanded to optimize three objectives. • Developing the MOQQROM using the MOFA-KNN hybrid algorithm. • Development of an effective approach to enhance the speed of optimization. [ABSTRACT FROM AUTHOR]

Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper, we analyze the permutation flow shop problem (PFSP) with both stochastic and fuzzy processing times. The main goal is to find the solution (permutation of jobs) that minimizes the expected makespan. However, due to the existence of uncertainty, other characteristics of the solution are also taken into account. In particular, we illustrate how survival analysis can be employed to enrich the probabilistic information given to decision-makers. To solve the aforementioned optimization problem, we extend the concept of a simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated in the PFSP. In order to test our approach, classical PFSP instances have been adapted and extended, so that processing times become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. [ABSTRACT FROM AUTHOR]

As a new collaborative mining technology, the integration of mining-separating-backfilling (IMSB) enables the coal industry to realize safe, efficient, and green mining of coal resources at the expense of making the underground coal mine production more complex. In coal mining, the primary problem is to eliminate the production logistics system bottleneck to increase coal output. The key to synergetic mining is to realize that the mining capacity of the fully mechanized face should match the underground raw coal separating and gangue backfilling capacities. Considering a coal mine with IMSB in Henan Province, AnyLogic simulation software was used to simulate and optimize the production logistics system based on a generalized stochastic Petri net (GSPN). The main simulation results show that: (1) the raw coal separating capacity of this deep mine matches its mining capacity, and the gangue backfilling capacity can almost meet the demand for backfilling after raw coal mining; (2) belt conveyors 1 and 6 are the transportation bottlenecks of increasing production in the mine, and the lifting capacity of the main shaft is insufficient; (3) after optimization, the clean coal output of this coal mine increased by 3087 t monthly. This research promotes synergetic mining in the coal industry and can serve as a reference for the optimization of similar coal mine production logistics systems. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems can be solved through Simulation-Optimization (S-O) techniques where the pareto front gives the optimal solutions in the problem domains. During the selection of different modelling methods, optimization techniques and management scenarios, several pareto fronts can be generated. In the present work, an attempt has been made by performing intensive comparisons between different pareto fronts to compare the efficiency and convergence of different S-O models. In this process, groundwater models were developed to simulate the River-Aquifer (R-A) exchanges for the study area as groundwater pumping influences the rate of R-A exchanges and alters the flow dynamics. The developed models were coupled with optimization models and were executed to solve the multi-objective optimization problems based on the maximization of discharge through pumping wells and maximization of groundwater input into the river through R-A exchanges. The distinctive features of the paper include a pareto front comparison where fronts developed by different S-O models were compared and analysed based on various parameters. The results show the dominance of Multi-Objective Particle Swarm Optimization (MOPSO) over other optimization algorithms and concluded that the maximization of pumping rate significantly changes after considering the R-A exchanges-based objective functions. This study concludes that the model domain also alters the output of simulation–optimization. Therefore, model domain and corresponding boundary conditions should be selected carefully for the field application of management models. The artificial neural network (ANN) models have been also developed to deal with the computationally expensive simulation models by reducing the processing time and found efficient. [ABSTRACT FROM AUTHOR]