Simulation-optimization Approach Research Articles

A multi-objective simulation–optimization for a joint problem of strategic facility location, workforce planning, and capacity allocation: A case study in the Royal Australian Navy

The availability of assets crucially depends on the interplay between the facility locations, workforce planning, and capacity allocation. Therefore, in this paper, we model and solve a novel multi-objective optimization problem for strategic facility location, workforce planning, and capacity allocation in the context of the military. The developed model deviates from more common approaches and adapts a dynamic capacity scheme for both the workforce and facilities to capture the dynamic nature of future demand, such as maintenance requirements for assets. The capacity allocation and workforce planning problem focus on mainly strategic decisions involving from workforce and facilities. A system dynamics (SD) simulation model enables a decision-maker to analyze the effects of decision variables by modeling the system complexities, uncertainties, and interactions between facilities, workforce, and assets. However, the simulation model neither suggests nor seeks the best solution strategy or strategies. To overcome this shortcoming, we propose a simulation–optimization approach that uses a Non-dominated Sorting Genetic Algorithm-II (NSGA-II). NSGA-II generates feasible solution strategies (candidate solutions) such as time and amount of capacity expansion and downsizing for both the workforce and facilities as well as the amount of crew recruitment. These solution strategies are fed to the simulation model where it evaluates the fitness of candidate solutions. We test the applicability of the proposed method on a realistic case study from the Royal Australian Navy. Finally, we present scenario-based sensitivity analysis arising from the decision variables to support decision-makers.

Применение метода многокритериального синтеза в решении задач по обеспечению пожарной безопасности

Purpose. Increasing the efficiency of scientific research and solving applied problems in the field of fire safety by introducing a computational computer experiment – the method of multi-criteria synthesis to reduce the time, material and labor costs for the experiment and provide correct systemic interpretation of test results. Methods. To solve the problems of fire safety, the method of multi-criteria synthesis was applied. It makes it possi-ble to predict the behavior of the system, taking into account the stochasticity of its parameters and the choice of rational characteristics that provide the required output parameters. The search for the optimal set of parameters of a technical object involves the determination of the calculated values of the criteria by the values of the calculated points, which are located in the multidimensional space of variable parameters. In this case, the space of parameters is transformed into the space of criteria, taking into ac-count the accepted restrictions. Findings. The article provides examples of the application of the method of multi-criteria synthesis in solving problems of ensuring fire safety. The formulation of a fire retardant and fire extinguishing composition based on synthetic phosphates of bivalent and trivalent ammonium metals, being highly effective in impact on peat and wood, was determined. The using of an optimization-simulation approach made it possible to predict the time of extinguishing a fire in a tank and to select the optimal parameters of extinguishing by supply of low expansion foam directly into the fuel layer. On the basis of multi-criteria computer synthesis, the concentration of the modifying additive in the set of diamond segments and the cutting conditions for the tool during emergency rescue operations was determined. The way of assessing the aggregate territorial risk by the method of statistical modeling was developed which created a framework for risk management, including legal, economic regulation and compulsory insurance. Application field of research. The presented research results are aimed at improving the fire safety of various objects.

A fuzzy multi-objective multiple-pollutant model for rivers using an ant colony algorithm

A fuzzy multi-objective optimisation model was investigated for water quality management in a river under uncertain conditions. In this study, to deal with multiple pollutants simultaneously, the National Sanitation Foundation's water quality index was considered as one of the model's objective functions based on fuzzy set theory. This made it possible to investigate the overall effect of uncertainties on simultaneous changes. Another objective function was the total treatment costs for wastewater discharged into the river. A water quality simulation model and a non-dominated-archiving ant colony optimisation algorithm were used to determine the values of water quality parameters and the model's optimal solutions, respectively. Furthermore, a simulation–optimisation approach was adopted for facilitating the problem-solving process and applied to a hypothetical case study resembling a river system in Iran. The results show that the proposed model significantly reduced the total wastewater treatment costs compared with a similar single-objective model with a more cautious and a cost-effective approach. Although the treatment costs were increased compared with the similar deterministic model, more feasible approach was taken by considering the uncertainties associated with the objectives. With suitable modifications, the model could be easily adapted for other river systems.

A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Construction of ground control points for spacecraft using optimization-simulation model

The article deals with the application of simulation modeling and optimization-simulation approach to solving problems of synthesis of structures of automated spacecraft control systems, namely, the choice of spacecraft control points, which are multifunctional aggregates of stationary and mobile elements dispersed in space with developed technical means of receiving, transmitting and processing information. Various approaches to the joint use of optimization and simulation models in the synthesis of the structure of complex systems are analyzed. The main attention is paid to the possibility of joint use in the synthesis of optimization and simulation models, their rational interaction in optimization and simulation procedures that describe both the composition and interrelationships of the structural elements of the system, as well as dynamic and stochastic aspects of their functioning. An optimization-simulation approach based on the joint use of optimization and simulation models in the process of searching for optimal structure options is proposed. Rational mapping of a set of interrelated functions performed by a control system into a set of interconnected nodes with appropriate technical means, taking into account the costs of creating or reconstructing the system, the costs of operation and operation, the requirements of operational efficiency of management, the reliability of technical means, the survivability and globality of the control system and other characteristics. Consideration of the problem of optimizing the distribution of tasks included in the control loop by levels and control nodes of the system and determining a set of technical means that minimize the costs of equipping nodes with technical means and their operation when performing restrictions on efficiency, hardware reliability of performing control tasks, weight and energy consumption of on-board equipment, loading of nodes, etc.

Construction of ground control points for spacecraft using optimization-simulation model

The article deals with the application of simulation modeling and optimization-simulation approach to solving problems of synthesis of structures of automated spacecraft control systems, namely, the choice of spacecraft control points, which are multifunctional aggregates of stationary and mobile elements dispersed in space with developed technical means of receiving, transmitting and processing information. Various approaches to the joint use of optimization and simulation models in the synthesis of the structure of complex systems are analyzed. The main attention is paid to the possibility of joint use in the synthesis of optimization and simulation models, their rational interaction in optimization and simulation procedures that describe both the composition and interrelationships of the structural elements of the system, as well as dynamic and stochastic aspects of their functioning. An optimization-simulation approach based on the joint use of optimization and simulation models in the process of searching for optimal structure options is proposed. Rational mapping of a set of interrelated functions performed by a control system into a set of interconnected nodes with appropriate technical means, taking into account the costs of creating or reconstructing the system, the costs of operation and operation, the requirements of operational efficiency of management, the reliability of technical means, the survivability and globality of the control system and other characteristics. Consideration of the problem of optimizing the distribution of tasks included in the control loop by levels and control nodes of the system and determining a set of technical means that minimize the costs of equipping nodes with technical means and their operation when performing restrictions on efficiency, hardware reliability of performing control tasks, weight and energy consumption of on-board equipment, loading of nodes, etc.

Enhanced coverage by integrating site interdependencies in capacitated EMS location models

In order to allocate limited resources in emergency medical services (EMS) networks, mathematical models are used to select sites and their capacities. Many existing standard models are based on simplifying assumptions, including site independency and a similar system-wide busyness of ambulances. In practice, when a site is busy, a call is forwarded to another site. Thus, the busyness of each site depends not only on the rate of calls in the surrounding area, but also on interactions with other facilities. If the demand varies across the urban area, assuming an average system-wide server busy fraction may lead to an overestimation of the actual coverage. We show that site interdependencies can be integrated into the well-known Maximum Expected Covering Location Problem (MEXCLP) by introducing an upper bound for the busyness of each site. We apply our new mathematical formulation to the case of a local EMS provider. To evaluate the solution quality, we use a discrete event simulation based on anonymized real-world call data. Results of our simulation-optimization approach indicate that the coverage can be improved in most cases by taking site interdependencies into account, leading to an improved ambulance allocation and a faster emergency care.

Stochastic Techno-economic Analysis of CO2-circulated Geothermal Energy Production in a Closed Reservoir System

Many wells become abandoned with the depletion of oil and gas reservoirs, and low-enthalpy geothermal energy could be extracted from these abandoned wells. CO2 has been proposed and studied as the working fluid for mining such geothermal energy sources, owing to its favorable thermodynamic properties. The objective of this study is to develop an uncertainty-based simulation-optimization approach to analyze the techno-economic feasibility of CO2-circulated geothermal production in a closed reservoir system. In doing so, we address the particular geothermal reservoir conditions found in North Oman`s foreland basin where several large oil/gas fields are located. The geothermal reservoir conditions in this area can be characterized by inclined and thin layers, partially to fully fault-blocked, and relatively homogeneous and low permeable formations. Such conditions have not been investigated in the context of CO2-circulated geothermal production. Another key novelty of this work is to analyze a triplet horizontal well placement designed particularly for such thinly-bedded fully-closed reservoirs. The main building blocks of the proposed methodology are the application of a non-isothermal, multi-phase, multi-component numerical reservoir simulator, a neural network-based metamodel, Monte Carlo simulations, and genetic algorithm. The findings suggest the proposed well pattern is technically feasible and economically viable to harvest low-enthalpy geothermal energy from the type of depleted reservoirs found in North Oman region.

Simulation Optimization for the Multihoist Scheduling Problem

Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison.

State-dependent resource reallocation plan for health care systems: A simulation optimization approach

Health care services in any country influence a considerable part of the economic, social, and political systems; researchers are trying to improve the quality of health care to realize the patients' needs and service quality in various fields. This study is conducted to provide an optimal system to reduce waiting time and increase the level of services according to various system conditions and increase the output rate in a medical center according to a key performance indicator, i.e., the patient's average waiting time. Discrete-event simulation has been applied to analyze the process, and simulation optimization has been employed to achieve optimal values. The tools used include ARENA software to simulate and OptQuest to optimize the simulation model. The system has been simulated separately for two purposes: a) resource optimization and b) resource planning according to the system state, and the results indicate that the system state-dependent model is the ideal model to reduce the waiting time for people and increase the output rate without decreasing the quality of service. State-dependent modeling approach has an advantage that if the resources are employed in shifts in the more crowded wards, then the waiting time for patients can be significantly reduced in the health care center.

An Efficient Elite-Based Simulation–Optimization Approach for Stochastic Resource Allocation Problems in Manufacturing and Service Systems

Stochastic resource allocation problems (SRAPs) involve determining the optimal configuration of a limited resource to achieve an objective function under given constraints and random effects in manufacturing systems (MSs) and service systems (SSs). The problems are traditionally solved by determining the optimal solution. It is generally preferable to determine as many global optima as possible, or at least a small set of diverse but good candidates, to help the decision-maker rapidly adopt alternative solutions from the set if one solution is unsuitable. However, many local or global optima occur in SRAPs in MSs and SSs due to the interaction between random system factors, such as processing time uncertainty and machine failure rates. Thus, enhancing the searching efficiency of algorithms for SRAPs is a challenge. This study proposes an efficient simulation–optimization approach, called elite-based particle swarm optimization (EPSO), using an optimal replication allocation strategy (ORAS) (i.e., EPSO[Formula: see text], to address three types of SRAPs from the literature. Three simulation models were constructed to evaluate the system performance under random factors. We developed a novel EPSO to explore and exploit the solution space. We created an elite group (EG) that includes multiple solutions, and each solution of the EG has a statistically nonsignificant difference from the current optimal solution. The new feature of EPSO updates the velocity and position of the particles in the design space based on multiple global optima from the EG to enhance diversity and prevent premature convergence. We propose an ORAS to allocate a limited number of replications to each solution. Three numerical experiments were performed to verify the effectiveness and efficiency of EPSO[Formula: see text] compared with other simulation–optimization approaches, namely particle swarm optimization (PSO) and the genetic algorithm (GA) with both optimal computing budget allocation (OCBA) and the ORAS. The experimental results reveal that the solution quality of EPSO improved compared with that of PSO and GA, and the ORAS provides a more efficient allocation of the number of replications compared with the OCBA in the three experiments. Finally, the proposed approach also provides an elite set at the end of the algorithm, instead of a single optimal solution, to support decision-making.

City-scale optimal location planning of Green Infrastructure using piece-wise linear interpolation and exact optimization methods

Green Infrastructure (GI) location planning involves multi-objective spatial analysis considering simultaneous benefits. Fast optimization results are often required (e.g., collaborative modeling exercises and stakeholder involvement workshops), while the simulation-optimization approach is impractical for these cases if it relies on time-consuming simulations of complex drainage system models. This paper proposes a new method using a surrogated model based upon targeted executions of a more complex urban drainage model. This method uses a piece-wise linear interpolation to predict the hydrological impact of individual impervious-to-pervious area changes and the Mixed Integer Linear Optimization to find the optimal location of GI practices. We applied the methodology to the capital city of Colombia, Bogotá, with the objectives of reducing runoff, combined sewer overflows (CSOs), and separate sanitary overflows (SSOs). Two multi-objective optimization approaches (i.e., a lexicographic and a weighted-sum model) were explored, showing the high efficiency of this approach in building Pareto Fronts and rapidly suggesting alternative solutions for different budgets. It was found that by implementing GIs on one-third of the city’s GI-feasible public areas (2.2% of city area), under a 10-year 6-h rainfall event, the total reduction on runoff, CSO, and SSO are 0.9%, 1.8%, and 2.4%, respectively. These results show the importance of promoting GI among private-land owners, especially in cities with land predominantly private-owned. This new approach is particularly useful for applications under data scarcity and high-uncertainty scenarios.

A Sequential Optimization-Simulation Approach for Planning the Transition to the Low Carbon Freight System with Case Study in the North Island of New Zealand

Freight movement has always been, and always will be an essential activity. Freight transport is one of the most challenging sectors to transition to net-zero carbon. Traffic assignment, mode allocation, network planning, hub location, train scheduling and terminal design problem-solving have previously been used to address cost and operation efficiencies. In this study, the interdisciplinary transition innovation, management and engineering (InTIME) methodology was used for the conceptualization, redesign and redevelopment of the existing freight systems to achieve a downshift in fossil energy consumption. The fourth step of the InTIME methodology is the conceptualization of a long-term future intermodal transport system that can serve the current freight task. The novelty of our approach stands in considering the full range of freight supply chain factors as a whole, using an optimization-simulation approach as if we were designing the low-carbon system of 2121. For the optimization, ArcGIS software was used to set up a multimodal network model. Route and mode selection were delivered through the optimization of energy use within the network. Complementarily, Anylogic software was used to build a GIS-based discrete event simulation model and set up different experiments to enhance the solution offered by the network analysis. The results outline the resources needed (i.e., number of railway tracks, train speed, size of railyards, number of cranes and forklifts at terminals) to serve the freight task. The results can be backcast to reveal the most efficient investments in the near term. In the case of New Zealand’s North Island, the implementation of strategic terminals, with corresponding handling resources and railyards, could deliver 47% emissions reduction from the sector by 2030, ahead of longer lead-time upgrades like electrification of the railway infrastructure.

Production planning and control of unreliable hybrid manufacturing-remanufacturing systems with quality-based categorization of returns

Because of environmental and economic benefits, remanufacturing has become an integral part of a large number of manufacturing companies. This new reality forces them to focus on production planning and control (PPC) to minimize costs and reduce the environmental impact by decreasing waste and resource consumption. This paper deals with a PPC problem within a hybrid manufacturing-remanufacturing system (HMRS), which evolves in a constrained dynamic and stochastic context. The problem considers jointly three central decisions to optimize the long-term total cost. The main objective is to find both remanufacturing and manufacturing production rates as well as the policy used to switch between the remanufacturing modes of returns with different quality conditions. The optimal control policy characterization is performed combining optimal control theory and numerical techniques. Such policies consist of hedging point ones and stock-based switching decisions. Additional relevant control policies adapted from the literature are also considered and compared to the developed policies through a combined simulation-optimization approach, which optimize the policies control parameters. Both illustrative examples and a comparative analysis bring out the practical aspects of the proposed control policies and the interdependence between the quality conditions of returns and the production control settings. They also bring effective solutions to assist decision makers controlling simultaneously remanufacturing, manufacturing and switching operations for proper management of resource utilization, inventories and productivity.

Engineered Bioremediation of NAPL Polluted Sites: Experimental and Simulation-Optimization Approach under Heterogeneous Moisture and Temperature Conditions

An integrated experimental-numerical approach is used in this study to bioremediate a toluene, a nonaqueous phase liquid (NAPL), contaminated land site, having varying moisture and temperature levels. The rates of biodegradation in saturated and unsaturated zones under varying soil-moisture (100% to 20%) and temperature (30°C±2°C and 10°C±0.5°C) conditions are obtained by conducting a series of laboratory experiments first. Thereafter, an integrated approach for engineered bioremediation of a characteristic polluted subsurface site is developed considering a system of injection-extraction wells and a HYDRUS three-dimensional (3D) simulator. The injection-extraction wells system is optimally designed to enhance the natural bioremediation rate by having three injection wells and one extraction well to provide additional oxygen supply to the contaminated zone and to contain the NAPL plume in the treatment zone. The pumping rates for injection and extraction wells are optimized using an extreme learning machine-particle swarm optimization-based simulation-optimization approach (ELM-PSO). The results show that the biodegradation rates are high at 30°C for the polluted site having soil moisture content around field capacity. The degradation rate is reduced significantly at a lower temperature of 10°C, particularly when the soil moisture content is kept in the low (40%–20%) range. The designed injection-extraction well system shows that almost similar costs of remediation are required when the soil moisture content is maintained in the range of 60%–80% of the saturation level at a high (30°C) temperature. No substantial change in the time of remediation is observed by changing the soil moisture content between 60% and 80% at this high temperature. However, an elongated time period of treatment observed at a 60% moisture content as compared to the 80% level at a low (10°C) temperature indicates the dominant role of temperature stress as compared with the soil moisture availability. Further, this combination takes about 66% less time in remediating the pollutant concentration to an acceptable level than the time required at the low temperature and moisture content levels. The findings of this study are of direct use in planning remediation strategies for hydrocarbon contaminated sites.

A multi-objective simulation–optimization approach for water resource planning of reservoir–river systems based on a coupled quantity–quality model

A multi-objective simulation–optimization approach for water resource planning of reservoir–river systems based on a coupled quantity–quality model

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.

Design and optimization of energy-efficient single mixed refrigerant LNG liquefaction process

The optimal design of single mixed refrigerant (SMR) natural gas liquefaction process is decisive for upgrading the liquefied natural gas (LNG) value chain and can be achieved using process simulators and derivative-free optimization techniques in simulation–optimization frameworks. The process work consumption can be reduced by modifying process flowsheet and manipulating refrigeration cycle thermodynamic conditions and refrigerant composition. The goal of this paper is the design of energy-efficient SMR liquefaction process for LNG production using simulation–optimization approach, in Aspen HYSYS® and MATLAB®, that considers more decision variables to be optimized with a Nelder–Mead simplex algorithm and small flowsheet modifications, such as hydraulic turbines and flash separators in between compression stages. The present approach resulted in liquefaction process with network duty of 750.2 kJ per kg of natural gas, which is considerably smaller than those reported in the literature.

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.

A simulation-optimization algorithm for return strategies in emergency medical systems

In an emergency medical system, the locations of ambulance stations has a direct impact on response time. In this paper, two location models are presented in combination with the hypercube queuing model to maximize coverage probability. In the first model, the locations of free and busy ambulances are considered in the system states, and the hypercube model can be analyzed accurately. The model contains a large number of states, and cannot be used for large-sized problems. For this reason, the second model is presented with the same assumptions as in the first model, except that the locations of busy ambulances are not included in the system state, but approximated based on the arrival rates. Both models are offline and dynamic, in which an ambulance does not necessarily return to the station from which it has been dispatched. Two strategies are defined for returning ambulances to the stations from the customer’s location. In the first strategy, the ambulance is returned to the nearest station after completion of its mission, and in the second strategy, it returns to the empty station that covers the highest demand rate. For evaluation of the performance of the proposed models, small-sized examples are solved for both return strategies using the GAMS software. A simulation-optimization approach combined with a simulated annealing algorithm and a discrete-event simulation are used for solving large-sized problems. Moreover, real data from a case study are used to demonstrate the performance of the models in the real world.