Simulation-optimization Approach Research Articles

Close-to-nature management of tropical timber plantations is economically viable and provides biodiversity benefits

Abstract Reforestation of tropical forests is crucial to mitigate the climate crisis and restore ecosystems. However, past efforts have been criticized for establishing monoculture timber plantations with exotic tree species. Close-to-nature (CTN) practices aim to minimize negative forest management impacts on forests ecosystems by mimicking natural dynamics. So far, CTN management practices are rarely applied in tropical plantation forestry. This study evaluates the economic, carbon sequestration, and biodiversity potential of CTN management in tropical mixed-species plantations in Central America using a simulation-optimization approach. To our knowledge, this study is the first to assess the potential of tropical CTN-managed plantations on the basis of detailed process-based forest growth simulations. CTN practices such as selective harvesting, retention forestry, and shelterwood cutting of mixed-species stands were compared to even-aged mixtures and conventional monoculture practices. Results showed that CTN management was economically viable for certain species mixtures and management practices at an 8 % discount rate and had the potential to increase carbon storage and biodiversity in the modeled plantations. At current carbon prices, CTN-managed plantations may only become financially competitive with monocultures, if monocultures are excluded from carbon certification schemes that increasingly aim at co-producing non-carbon benefits like biodiversity conservation. If carbon prices increase, the sale of carbon credits could finance the transformation of monocultures to CTN-managed mixed-species stands. The competitiveness of CTN management could also be improved through performance-based biodiversity payments, such as the sale of biodiversity credits.

Optimum identification of aquifer parameter zone structures using the SHuffled Ant Lion Optimization approach considering general form of the Voronoi tessellation

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.

Hybrid simulation-optimization approach for planning relief-aid distribution with a real-world case study

Purpose Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates. Design/methodology/approach A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study. Findings The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used. Originality/value The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

Spatial allocation of bioretention cells considering interaction with shallow groundwater: A simulation-optimization approach

Green infrastructure (GI), as one type of ecological stormwater management practices, can potentially alleviate water problems and deliver a wide range of environmental benefits in urban areas. GIs are often planned and designed to reduce runoff and mitigate pollution. However, the influence of GI on groundwater hydrology and that of shallow groundwater on GI performance was seldom considered. This study utilized a calibrated surface-subsurface hydrological model, i.e., Storm Water Management Model coupled with USGS's modular hydrologic model (SWMM-MODFLOW) to consider the interaction between GI and groundwater into the process of GI planning. The optimal implementation ratio, aggregation level and upstream-downstream location of bioretention cells (BC, one type of GI) under different planning objectives and hydrogeologic conditions was explored. The consideration of groundwater management exerted a significant impact on the optimal spatial allocation of BCs. The results showed that when groundwater management was more concerned than runoff control, BCs were recommended to be allocated more apart from each other and more upstream in the catchment because more-distributed and upstream BCs can result in lower groundwater table rise which is beneficial. BCs were overall recommended to be allocated in areas of deeper groundwater tables, coarser soils, and flatter topographies. However, the spatial features of BCs are related to each other, the choice of them are affected by various hydrogeologic factors simultaneously. The exact location of BCs should be determined by considering the trade-off between runoff control efficiency and groundwater impact. The findings obtained in this study can provide guidance on GI planning in shallow groundwater areas.

Stochastic ambulance dispatching and routing in mass casualty incident under road vulnerability

This paper investigates the stochastic ambulance dispatching and routing (SADR) problem with multiple casualty collection points in the mass casualty incidents (MCI) under uncertainty of road vulnerability and traffic congestion caused by an earthquake. A two-stage stochastic mixed-integer nonlinear programming model is formulated to derive a reliable and high-quality ambulance scheduling, dispatching and routing solution for maximizing expected number of survivors of all casualties. An integrated simulation optimization approach combining a data-driven travel time scenario generation algorithm, sample average approximation, and a column-generation-based heuristic method is developed to efficiently solve this complicated two-stage SADR model with a nonlinear objective function and continuous travel time distributions. Collaborating with the National Science and Technology Center for Disaster Reduction (NCDR), an empirical study using a potential real-world earthquake scenario occurring in Taiwan is conducted to demonstrate the usefulness and effectiveness of our proposed model and solution approach compared to the deterministic model and two current-practice heuristics.

Ternary Organic Solar Cells—Simulation–Optimization Approach

Ternary Organic Solar Cells—Simulation–Optimization Approach

A discrete event simulation model assessing the impact of using new packaging in an agri-food supply chain

Fresh product loss is a global issue having negative impact on both the economy and the environment. The perishability of fruits and vegetables can lead to post-harvest losses in the supply chain as well as at consumer’s homes. Packaging plays an important role in preventing such losses. This paper focuses on the impact of new packaging on reducing product waste and other logistics costs. The objective is to evaluate the performance of an inventory control policy that uses new packaging to extend the shelf life of strawberries while maintaining their quality. We have developed a discrete event simulation model, that represents store inventory, by considering random consumer demand and random product shelf life, to assess various packaging types leading to different post-harvest lives of strawberries. We then use a simulation-optimisation approach to determine optimal inventory control parameters and conduct extensive numerical experimentations to evaluate the impact of the new packaging, under three distribution strategies (FIFO, FEFO, and LEFO). Results indicate that the new packaging has a significant impact: extending the shelf life of strawberries can reduce the total logistics cost by 0.66% (when using LEFO strategy) to 6.37% (FIFO strategy) and improve sales by 0.67% (FEFO) to 10.07% (FIFO).

Sustainable and integrated rail operations planning for grain exports

ABSTRACT The grain export in Brazil is operated by several players and regulated by a few governmental agencies. Although players have particular interests, sustainability is an increasing concern that poses a common challenge to rail operations: reducing CO2 emissions. This work proposes a simulation-optimisation approach for assigning trains to routes in a complex queuing system, minimising CO2 emissions of the country-wide rail operations without compromising the delivery of planned volume exports. The model considers a multi-modal logistic system, i.e., terminals, railways, and ports integrated into a closed circuit. The computational experiments using real-world data of 2021 reveal that the CO2 emissions could be reduced by 5.32 kilo tons and still increase the grain export by 2.20% per year.

Part transformation-based spare parts inventory control model for the high-tech industries

Timely and cost-effective supply of spare parts is the main purpose of spare parts inventory management and substitution is an effective way to fulfill demand on time. However, direct substitution of spare parts is not suitable for the high-tech industries due to the ever-changing nature of the product structures. Hence, parts should be transformed to be used as substitutes. This paper provides a novel spare parts inventory control model for the high-tech industries. In the proposed model, part transformation-based substitution is considered and the near-optimal values of spare part inventory levels (s, S) that minimize total cost are determined by using a simulated annealing-based simulation optimization approach. Computational analyses are performed for a hypothetical inventory system by considering transformation and no-transformation cases. The results reveal that transformation is very useful for the companies who endure long production lead times and high penalty costs associated with backorders.

Novel Simulation Optimization Approach for Supply Chain Coordination and Management

The importance of supply chain management is paramount nowadays and this research seeks ways for better supply chain coordination. We present a roadmap for implementing true digital replicas of physical supply chains based on a disruptive simulation modeling approach. These digital replicas can enable the comparative evaluation of existing Just-In-Time (JIT) control policies for the first time without the unrealistic assumptions of existing studies in the literature. These realistic supply chain simulation models have the prospect of giving us better understanding of their behavior and of their complex dynamics. We provide guidelines for using the digital replicas and advanced optimization approaches to derive novel supply chain coordination policies pushing the field of JIT control beyond the state-of-the-art. An equally important contribution of this research is that it advances the field of hybrid discrete event-system dynamics (DES-SD) simulation with the prospect of pushing Systems Engineering to the next level. This research discusses theoretical foundations for mixing DES with SD simulation, and explores technical aspects and implementation details. The implications of this extent well beyond the supply chain field since both DES and SD have dozens of application areas including manufacturing, service systems and economics, social sciences, respectively. The paper is concluded with a discussion on the potential impact of the proposed approach including insights for practitioners, applicability of scientific results in industry and prerequisites for business process re-engineering.

River water quality management using an integrated multi-objective optimization-simulation approach based on bankruptcy rules.

The aim of this research is to allocate the river's self-purification (acceptance capacity of pollution) fairly between the beneficiaries (pollutant sources) using bankruptcy theory. For this purpose, four bankruptcy rules (CAE, CEL, P, and TAL) were called using the link of the water quality simulation model (QULA2Kw) to an evolutionary optimization algorithm (multi-objective imperialist competition algorithm (MOICA)). The objective functions were reducing polluters' wastewater treatment costs and preventing biochemical oxygen demand (BOD) violations of the standard level along the river. The applicability of the approach is demonstrated by the case study that was carried out on the Dez River in Iran. According to the results, the CEL scenario is the most effective method for the Dez River when taking into account the most optimal state for both objective functions (selecting the best compromise solution from the Pareto front). This is because it has the lowest violation value of the standard level for BOD along the river when compared to other scenarios. Alternatively, when considering Solution 20, which focuses on the maximum cost of treating the polluters while staying within the acceptable level of pollution in the river, the results indicated that the CEA rule emerged as the most favorable option. This is due to its lower treatment cost (156.9 (1000$)) and higher pollution discharge to the river (681.91 g/s).

Simulation–optimisation approach for sustainable planning of intermodal logistics in the Brazilian grain export industry

Planning nationwide sustainable logistics for grain exports at a tactical level assumes flexibility in handling uncertainties. Despite the dynamic operations, the solution’s approach focuses mainly on optimisation, leaving out the queuing requirements. This paper presents a simulation–optimisation approach for planning the Brazilian intermodal grain export chain by integrating trucks, railways, and port terminal operations. We evaluate a base scenario, aiming to minimise CO2 emissions in rail transport, meeting demand requirements, and three alternative planning scenarios concerning demand retraction, trend, and expansion. The sustainable grain transportation plan of the base scenario shows a 66.8 kilo tons CO2/year emission reduction while increasing the exports by 2.51% (approximately 146 kilo tons grains/year) and a contribution to profit of $43.82 million in the short term. Scenario analyses open discussions about sustainable investments in intermodal transport for the medium and long term. The proposed approach is suitable for other commodity chains, representing a step towards consistently applying environmentally oriented supply chain planning.

Simulation- optimisation approach to support management of blood components inventory

ABSTRACT 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.

Assessing Impacts of Flood Diversion on the Ecosystem of Brackish-Water Lakes through Simulation-Optimization Model

This study develops and evaluates a simulation-optimization approach to mitigate the environmental impacts of freshwater pulses in brackish-water lakes whilst maximizing flood diversion benefits. Lake Pontchartrain, located downstream of the Mississippi River, Mississippi, United States, is a brackish-water ecosystem threatened by reducing salinity concentrations due to freshwater pulses from the flood diversion project on the Mississippi River. An adaptive neuro-fuzzy-inference-system-based model was developed as a data-driven model for simulating salinity distribution at a representative station of Lake Pontchartrain. Then, the data-driven model was used as the simulator in the optimization system. Both single-objective and multi-objective particle swarm optimizations were used to find the optimal solutions. Results show that the data-driven model is robust at simulating the salinity time series in the brackish-water ecosystem of Lake Pontchartrain. The Nash–Sutcliffe efficiency index of the data-driven model between measured and modelled salinity is 0.85, which means the model is reliable for applying in further simulations. The proposed optimal solutions for the environmental management of the lake indicate that because of the magnitude of the volume of freshwater released, environmental impacts at this location cannot be optimized through varying the timing and volume of the releases. This work presents a novel contribution to science through developing an optimization framework for mitigating the impacts of flood management on changes in salinity in brackish-water systems.

Selection of Real-Coded Genetic Algorithm parameters in solving simulation–optimization problems for the design of water distribution networks

Abstract 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.

Optimal layout design of groundwater pollution monitoring network using parameter iterative updating strategy-based ant colony optimization algorithm.

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.

A simulation-optimization approach based on the compound eutrophication index to identify multi-nutrient allocated load

Eutrophication with nutrient enrichment is a global marine ecosystem concern that threatens human health, economic activities, and ecosystem functions. Therefore, a nutrient load optimization method is required to help control marine eutrophication. However, eutrophication-based nutrient allocated load optimization is a multi-objective project due to a series of eutrophication pressures, such as cross-regional land-based nutrient loads and multi-nutrient regimes and ratios. In this study, a synergistic multi-nutrient control method was developed for the Bohai Sea (BS), China, which links multi-nutrient pressures with eutrophication states. Based on the eutrophication control standard, which is the second level of compound eutrophication index (CEI), the total maximum allocated loads (CEI-based TMALs) of total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), and the chemical oxygen demand (COD) were calculated by a simulation-optimization approach. Using the end year of China's 13th Five-Year Plan (2020) as the reference year, 154 high load pressure jurisdictions (HLPJs) that contribute to eutrophication response segments in the BS were identified. Accordingly, practiced the optimized annual reduction rates of TDN, TDP, and COD in the HLPJs at 15 %, 11 %, and 2 % according to CEIII, respectively, the proportion of eutrophicated areas gradually decreased from 32 % in 2020 to 15 % in 2025 and might be 0 % in 2035 with ecosystem resilience in 2035. In particular, under the annual reduction rates of TDN and TDP optimized based on CEIII, the DIN/DIP molar ratio in the BS decreased to 16:1 by 2035. The simulation–optimization approach associated with the CEI-based TMALs for multi-nutrient control in this study might make implementing land-sea coordination more efficiency and marine nutrient regime stably. This can provide scientific and technological support for improving the health of coastal ecosystems.

An assets maintenance-workforce planning problem under uncertainty: A chance constraints assisted simulation-optimization approach

Preventive and corrective maintenance-workforce planning for assets is a growing area of research, given the presence of uncertainty. We model a Simulation-Optimization framework to solve this challenging large-scale maintenance-workforce planning problem while considering its stochastic nature inside both the simulation and optimization models. A holistic simulation model is proposed through multiple modules to imitate different aspects of the maintenance process, i.e., spare facilities/storage and workforce. The workforce module represents technicians’ types, skills and levels, and employment life-cycle, including recruitment, promotion, and separation/retirement. The present uncertainty in the simulation model demands its corresponding optimization model to cope with the nature of the problem and guarantee a degree of confidence level of the optimal decisions. Therefore, a Chance Constraints Programming (CCP) method is integrated with an evolutionary optimization algorithm to limit the risk level of violating the random constraints while minimizing the total maintenance cost. Discrete Event Simulation (DES) and Self-Adaptive Differential Evolution assisted with Chance Constraints (SA-DE-CC) algorithm are coupled to solve the current problem. The optimization model is proposed to optimize the preventive maintenance frequency and workforce planning problem while implicitly considering corrective maintenance in the simulation model. The Taguchi method performs scenario and risk analysis for multiple CC probability distributions (i.e., Weibull and Normal), confidence levels, and critical parameter combinations. The model’s sensitivity analysis and managerial insights are also discussed.

Simulation and optimization framework for evaluating the robustness of low-impact development placement solutions under climate change in a small urban catchment

ABSTRACT The lack of acceptable temporal resolution of climate projections hinders proper assessment of the future performance of low-impact development (LID) systems in small catchments when continuous simulations are required (e.g. to evaluate infiltration). This study applied a simulation optimization approach to maximize infiltration with LIDs at minimum costs in a small urban catchment (0.67 km2). We coupled the Storm Water Management Model (SWMM) with the Nondominated Sorting Genetic Algorithm II (NSGA-II) to determine near-optimal locations of bioretentions, green roofs, and permeable pavements. The temporal resolution of rainfall projections was disaggregated from 24 hours to 15 minutes using the Bartlett-Lewis rectangular method to evaluate the performance and robustness of the optimized solutions under different budgets and climate scenarios. Results suggest that LIDs can mitigate climate change impacts with relatively inexpensive solutions. However, the robustness analysis showed that climate change could compromise the expected performance of LIDs sized with historical rainfall.

A Simulation Optimization Approach for Wetland Conservation and Management in an Agricultural Basin

Decreasing water quantity and growth in water demand have increased the competition between satisfying societal water needs and protecting ecosystem requirements. Wetlands are some of the most productive ecosystems on Earth. They provide various services to people’s livelihoods, in addition to being suitable habitats for many plant and wildlife species. However, wetlands are under threat of loss and degradation due to anthropogenic activities, particularly the diversion of water for irrigation. The flow regime is usually considered the most crucial ecological factor and a key component of wetland management. So, determining the allocation of environmental requirements is a main factor for managing, restoring, and protecting wetlands, and it is crucial to reach a compromise for optimal water allocation between different sectors. For this purpose, in this research, a new approach is developed to achieve the optimal environmental flow of the wetland in an agricultural-dominated basin using a combination of remote sensing and the simulation optimization method. Waterbirds and vegetation are used as bioindicators of wetland ecosystems. First, using remote sensing data and analyses, we obtained the interrelation between the wetland water regime, vegetation, and waterbird characteristics using different time series of Landsat spectral indices. Then, by employing the long-term simulation optimization (WEAP-MOPSO) model, the optimal e-flow of the wetland is evaluated in such a way that the suitable ecological condition of the wetland is achieved and the wetland is able to provide its functions and services.