Simulation Optimization Method Research Articles

Design of a low carbon economy model by carbon cycle optimization in supply chain

IntroductionConcerning economic globalization, enterprises must work with the cooperative partner to obtain more profits and overall planning of the supply chain has become a new focus for enterprise development. This paper studies the joint emission reduction of the supply chain in green low-carbon economy development and achieve joint emission and economic cost reduction through the optimization of carbon emission and economic dispatch.MethodsThe paper firstly uses the multi-agent model to complete the fullcycle modeling of carbon emission and economic cost; Secondly, the simulated annealing-adaptive chaos-particle swarm optimization (SAACPSO) method is used to optimize various parameters in the model to achieve emission and cost reductionResultsThe results show that after the optimization, the economic cost is reduced by 0.07 and the carbon emission is also reduced by 0.16; Finally, the practical test of the model is conducted with the collected data from the local company. The results show that the multi-objective optimization model of a joint enterprise supply chain is significantly better than single optimization in terms of emission reduction.DiscussionIt provides new ideas for a green economy and technical support for the global planning of supply chain integration.

Simulation study on the interaction between the battery module and busbar under typical driving conditions of electric vehicles

Accurate simulation of the battery thermoelectric coupling characteristics is the key to the thermal design and thermal management. As the electrically connected component between the battery electrodes, the heat production and heat transfer of the busbars have a significant impact on the battery thermoelectric behavior. A small number of battery thermal management studies have begun to model busbars recently, but the thickness, length, and material of the busbars are diverse. There is a lack of perfect simulation analysis and optimization methods. There is an urgent need to conduct in-depth research on the influence of characteristic parameters of the busbars on the battery thermoelectric behavior. Based on the MSMD-NTGK model, this paper investigates the influence of the thickness, length, and material of the busbars on the battery thermoelectric behavior without considering the economic cost and differences in the battery structure design. In this paper, a simulation analysis method and an ideal improvement solution for the design of the busbars are proposed. This paper compares the changes in the battery thermal behavior before and after the improvement of the busbars under the constant-rate discharge process, FTP75 condition, NEDC condition and WLTC condition respectively. This study can improve the realism of the battery thermal behavior simulation, provide a reliable simulation analysis method and reference basis for the industrial design and optimization of the busbars, and further improve the reliability of the subsequent battery thermal management simulation.

Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity

The European Union (EU) set clear climate change mitigation targets to reach climate neutrality, accounting for forests and their woody biomass resources. We investigated the consequences of increased harvest demands resulting from EU climate targets. We analysed the impacts on national policy objectives for forest ecosystem services and biodiversity through empirical forest simulation and multi-objective optimization methods. We show that key European timber-producing countries – Finland, Sweden, Germany (Bavaria) – cannot fulfil the increased harvest demands linked to the ambitious 1.5°C target. Potentials for harvest increase only exists in the studied region Norway. However, focusing on EU climate targets conflicts with several national policies and causes adverse effects on multiple ecosystem services and biodiversity. We argue that the role of forests and their timber resources in achieving climate targets and societal decarbonization should not be overstated. Our study provides insight for other European countries challenged by conflicting policies and supports policymakers.

Post‐earthquake functionality assessment of subway stations considering the interdependency among sub‐systems

AbstractThis study proposes a framework for the post‐earthquake functionality assessment of subway stations, in which the effects of seismic damage of components and interdependency among sub‐systems are considered by combining the fault tree analysis and Bayesian network method, and the post‐earthquake functionality is quantified by combining multi‐agent simulation and simulation optimization methods. The effectiveness of this method is verified based on a numerical study of a typical subway station. The proposed framework can assist stakeholders of subway stations to systematically assess the functional performance of the system under different earthquake levels and provide support for the design and post‐earthquake emergency management of subway stations.

Joint pricing and inventory management in a competitive market using reinforcement learning: a combination of the agent-based and simulation-optimization approaches

ABSTRACT One of the most important tools is an appropriate pricing mechanism to attract more customers and increase profits. The retailers’ main question is how to set the prices and inventory policies to maximize profit in a competitive heterogeneous market in presence of non-zero lead time and lost sales. A reinforcement learning algorithm is proposed to create appropriate decision-making mechanisms for pricing. A coordinated inventory policy in a competitive environment reduces logistic costs and leads to a higher profit. We use a reinforcement learning algorithm to investigate the performance of a retailer in a competitive environment. An agent-based modeling experimental environment combined with a simulation-optimization method in which a virtual market has been reproduced is used. The market is not homogeneous with respect to customer behavior. It is assumed that the retailer uses (R, Q) policy where the lead time is a fixed amount (L), and the shortage is permissible. The quality, distance, service level, and price are factors that influence customers’ choices. The simulation results for some randomly generated examples show that the algorithm in the competitive environment can make more profit than other available methods and the combined utilization of simulation-optimization methods has been able to find better solutions for the hybrid model of pricing and inventory management considering customer behavior. The results of simulation for three different categories of customers (more sensitive to price, equally sensitive to price, quality and service level, and more sensitive to quality (indicate that the average profit for the proposed algorithm is higher than that of other examined algorithms.

Groundwater contamination source identification based on Sobol sequences-based sparrow search algorithm with a BiLSTM surrogate model.

In the traditional linked simulation-optimization method, solving the optimization model requires massive invoking of the groundwater numerical simulation model, which causes a huge computational load. In the present study, a surrogate model of the origin simulation model was developed using a bidirectional long and short-term memory neural network method (BiLSTM). Compared with the surrogate models built by shallow learning methods (BP neural network) and traditional LSTM methods, the surrogate model built by BiLSTM has higher accuracy and better generalization performance while reducing the computational load. The BiLSTM surrogate model had the highest R2 of the three with 0.9910 and the lowest RMSE with 3.7732g/d. The BiLSTM surrogate model was linked to the optimization model and solved using the sparrow search algorithm based on Sobol sequences (SSAS). SSAS enhances the diversity of the initial population of sparrows by introducing Sobol sequences and introduces nonlinear inertia weights to control the search range and search efficiency. Compared with SSA, SSAS has stronger global search ability and faster search efficiency. And SSAS identifies the contamination source location and release intensity stably and reliably. The average relative error of SSAS for the identification of source location is 9.4%, and the average relative error for the identification of source intensity is 1.83%, which are both lower than that of SSA at 11.12% and 3.03%. This study also applied the Cholesky decomposition method to establish a Gaussian field for hydraulic conductivity to evaluate the feasibility of the simulation-optimization method.

New coefficient for water quality modelling in meandering rivers: Fatigue Factor

The accuracy of water quality predictions is essential, especially in countries affected by climate change and ecological water diversity. Water quality modelling in rivers is a valuable tool for enabling decision-making in surface water management because water quality prediction using sampling methods is expensive and time-consuming. The collection of technical knowledge of river characteristics and information about the sources of pollution plays a vital role in this context. This research focused on the effects of river geometry and meandering on the one-dimensional pollutant transport process. Flow velocity magnitude and direction in meandering rivers are frequently variable, leading to uncertain dispersion coefficients and massive changes in pollution concentration even over short distances of these rivers. So, the geometry of meandering rivers has a significant effect on their ecological indicators. A new coefficient called Fatigue Factor was introduced and defined in this study to consider this effect. Colidale Beck (CB) and Tyne rivers were selected for water quality modelling and implementation of the Fatigue Factor. The simulation-optimization method was employed to calculate zinc concentrations along the CB river using measured data for performance assessment of the model. The genetic algorithm performed well in predicting measured zinc concentration with high accuracy. Results of the model demonstrated that the mean effect of the Fatigue Factor in reducing the peak concentration of zinc increases by 3.8% compared to ignoring the Fatigue Factor along the CB length. With the Fatigue Factor consideration, the Mean Percentage Error between model outputs and measured data is 4%, while without it is 18%. Also, the Fatigue Factor had a greater impact on river pollution transport than the dispersion coefficient. With a 50% increase in the Fatigue Factor, the zinc concentration decreased by 6.1% more than the same increase in the dispersion coefficient. Moreover, results indicated that a 100% increment in the Fatigue Factor increases the assimilation capacity up to 3.5 times in CB.

Simultaneous predictive maintenance and inventory policy in a continuously monitoring system using simulation optimization

Predictive maintenance (PdM) is a strategy which can help determine the appropriate timing for maintenance depending on the actual operational conditions of a manufacturing system. Many relevant studies have ignored the effects of resource dependency and only considered perfect maintenance to reduce the complexity and uncertainty of maintenance problems. In this study, we develop a novel and highly practical maintenance model to fill the gap. The proposed model simultaneously considers the predictive maintenance and inventory policies of spare parts. In light of the high degree of complexity in modern manufacturing systems and the profound stochasticity in component degradation, a metamodeling-based simulation optimization method is proposed to find the optimal component inventory policy and degradation level thresholds of each component. A numerical study is conducted to verify that the proposed model and solution method can find the optimal or nearly optimal maintenance cost effectively and efficiently. Furthermore, the impact of critical factors in the maintenance model on the optimal policy is analyzed and useful managerial insights are derived.

Grinding simulation and process optimization method of tungsten carbide alloy

Grinding simulation and process optimization method of tungsten carbide alloy

Numerical Simulation on Impeller Casting Defects and Optimization

The numerical simulation is similar to the experimental research, which uses the computer. 3D printing is an experimental study, which is compared with simulation. 3D printing and precision casting of impellers are frequently used in industrial scenarios. To address the problem of low casting yield of aluminium alloy impellers in gypsum‐type casting, three‐dimensional modelling by magics, casting simulation, and parameter optimization method by applying ProCAST software are used to improve the casting yield of impellers. The results show that the optimized structure of the casting system is more reasonable, so that the shrinkage and shrinkage defects are concentrated in the location of the riser; the maximum stress value of the casting and the casing during the solidification stage is 2.71 MPa, which is much smaller than the stress limit of the casing; the maximum horizontal displacement of the casting along the X and Y axes after solidification is completed at 0.025 mm and 0.022 mm, respectively, and the maximum displacement along the Z axis due to the presence of the riser is 0.009 mm, which greatly improves casting accuracy and molding quality.

Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning.

The simulation optimization method was used to the identification of light nonaqueous phase liquid (LNAPL) groundwater contamination source (GCS) with the help of a hypothetical case in this study. When applying the simulation optimization method to identify GCS, it was a common technical means to establish surrogate model for the simulation model to participate in the iterative calculation to reduce the calculation load and calculation time. However, it was difficult for a single modeling method to establish surrogate model with high accuracy for the LNAPL contamination multiphase flow simulation model (MFSM). To give full play to advantages of single surrogate model and improve the accuracy of the surrogate model to the MFSM, a combination of deep belief neural network (DBNN) and long short-term memory (LSTM) neural network was used to establish artificial intelligence ensemble surrogate model (AIESM) for the MFSM. At the same time, to reduce the influence of noise in observed concentrations on the accuracy of the identification results, empirical mode decomposition (EMD) and wavelet analysis methods were used to denoise the observed concentrations, and their noise reduction effects were compared. The observed concentrations with better noise reduction effect and the observed concentrations without denoising were used to construct the objective function, and constraints of the optimization model were determined meanwhile. Then, the objective function and the constraints were integrated to build the optimization model to identify GCS and simulation model parameters. Applying the AIESM instead of the MFSM to embed in the optimization model and participate in the iterative calculation. Finally, the genetic algorithm (GA) was used to solve the optimization model to obtain the identification results of GCS and simulation model parameters. The results showed that compared with the single DBNN and LSTM surrogate models, AIESM obtained the highest accuracy and could replace the MFSM to participate in the iterative calculation, thereby reducing the calculation load and calculation time by more than 99%. Comparing with the wavelet analysis, EMD could reduce the noise in the concentrations more effectively, improved the accuracy of the approximated concentrations to the actual values, and increased the accuracy of the GCSs identification results by 1.45%.

Optimal siting of rainwater harvesting systems for reducing combined sewer overflows at city scale

The installation of green infrastructure (GI) is an effective approach to manage urban stormwater and combined sewer overflow (CSO) by restoring pre-development conditions in urban areas. Research on simulation-optimization techniques to aid with GI planning decision-making is expanding. However, due to high computational expense, the simulation-optimization methods are often based on design storm events, and it is unclear how much different rainfall scenarios (i.e., design storm events vs. long-term historical rainfall data) impact the optimal siting of GI. The Parallel Pareto Archived Dynamically Dimensioned Search (ParaPADDS) algorithm in a novel simulation-optimization tool OSTRICH-SWMM was used to leverage distributed computing resources. A case study was conducted to optimally site rainwater harvesting cisterns within 897 potential subcatchments throughout the City of Buffalo, New York. Seven design storm events with different return periods and rainfall durations and a one-month historical rainfall time series were considered. The results showed that the optimal solutions of siting cisterns using event-based scenarios, though less computationally expensive, may not perform well under continuous rainfall scenarios, suggesting design rainfall scenarios should be carefully considered for optimizing GI planning. The impact of rainfall scenarios was particularly significant in the middle region of the Pareto front of multi-objective optimization. Utilizing high-performance parallel computing, OSTRICH-SWMM is a promising tool to optimize GI at large spatial and temporal scales.

Joint Optimization of Maintenance and Spare Parts Inventory for Multi-Unit Systems With a Generalized Structure

Abstract Joint optimization of maintenance operations and spare parts inventory is promising for ensuring the reliable and efficient operation of industrial systems. Extensive studies have been conducted on joint optimization emphasizing various aspects. However, those studies still suffer two limitations. (1) The considered system structures are restricted to particular cases, and thus are unable to fully address the joint optimization needs of actual industrial systems possessing various structures, and (2) imperfect maintenance, as has been commonly adopted in industries, is not addressed in existing works, which compromises the economic efficiency of the joint policy. Targeted at these limitations, this paper investigates the joint optimization for multi-unit systems with a generalized structure, i.e., the systems consist of multiple identical units, and each unit possesses a complex subsystem structure. First, a total cost rate model is formulated, in which maintenance actions include both perfect replacement and imperfect maintenance, and the spare parts inventory policy consists of an (s, S) policy and the option to place emergency orders. Based on the formulated model, a simulation optimization method is proposed. The simulation module evaluates the total cost rate iteratively, and the optimization module utilizes a variable neighborhood search-based algorithm to obtain a high-quality joint policy with tractable computation times. Two numerical examples are provided for demonstration, and sensitivity analyses are conducted to study the effects of parameters.

Using a Random Forest Model to Choose Optimized Group Structures

It is challenging to select an appropriate group structure for any given multigroup neutron transport problem. Many group structures were designed long ago, and the reasoning behind the creator’s choices may be unknown. In this work, we apply the simulated annealing optimization method to develop improved group structures for a set of test problems. We then use a random forest (a machine learning method) to identify which group structure will be the best for any new problem based on input characteristics, such as geometry and isotopics. Simulated annealing spans a large solution space before narrowing in on an optimal solution, avoiding local minima by jumping around. Our solution space, however, is large and inconsistent, making finding the optimal group structure infeasible. Instead, we find potentially optimal group structures, ones that yield more accurate solutions than our standard group structures, but are probably not the “best” possible. Group structures are obtained for six classes of problems, ranging from a fast 233U system to a thermal 239Pu system. These were chosen to encompass a series of critical assemblies from the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook. These optimized group structures were used in PARTISN for a large range of ICSBEP critical assemblies and compared to the traditional Los Alamos National Laboratory group structures. Our reference solution was from 618-group PARTISN runs. The results were used to train a random forest regressor model with bagging, which was then tested on similar benchmarks. The bagging regressor model chose the best group structure from 52% to 65% of the time, and a subjectively “good” group structure up to 91% of the time.

Integrated water-quality management indicators from river to sea: A case study of the Bohai Sea, China

Considering the interrelatedness of river and bay ecosystems, river and bay water quality management is shifting to integrated management across coastlines. Here, an integrated management indicator for the coordinated and efficient nitrogen abatement of the Bohai Sea and its basin was proposed. The terrigenous total nitrogen (TN) allocated load was optimized under the dual water quality constraints for both river and bay using a simulation–optimization method. The contributing jurisdictions were identified by their TN overload rates, and their responsibility apportionment rate for specific nitrogen-polluted segment was quantified. Integrated TN reduction scheme resulted in a 29 % greater reduction in bay and river nitrogen pollution than the equal proportion reduction approach. In 18 % of the watersheds in the Bohai basin, the water quality standards of the river were more restrictive than the standards of the bay. Integrated management scheme has higher coordination of river and sea management objectives.

A novel multi‐strategy self‐optimizing SAPSO algorithm for PMSM parameter identification

To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi-strategy self-optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full-rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition-based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy–Gaussian hybrid variation strategy based on similarity and density is devised to achieve self-learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine-exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed.

Optimum hydraulic design of cut-off under hydraulic structures using the simulation–optimization method

Optimum hydraulic design of cut-off under hydraulic structures using the simulation–optimization method

Fault Tolerance Optimization of a Lithium Battery Pack Having a Damaged Unit.

As a kind of green and sustainable technology, electric vehicles are continuously highlighted for solving the significant problems of energy and air pollution. In this paper, fault tolerance optimization of an air-cooled lithium battery pack having a damaged unit was considered to improve the heat dissipation performance. For constructing the relationship between the objective function and the design variables, a quadratic polynomial response function was set up based on the experimental points, which were selected by the Latin-hypercube design of experiment method. Then, a multi-island genetic algorithm-based optimization of the battery pack having a damaged unit was performed under the condition of adjusting the flow velocity of each inlet. The results show that the acceptable temperature differences were obtained under the condition of fixing the total mass flow rate of the inlets. The simulation and fault tolerance optimization methods recommended in this study can be widely used to improve the real safety level of the systems.

Integrating scheduling and routing decisions into home health care operation with skill requirements and uncertainties

ABSTRACT The rapid increase of aging population poses a great challenge on the public medical resources. Home health care (HHC), which provides the care services for the elderly and patients with mild disease, is an emerging approach to cope with such difficulty. This work formulates a stochastic HHC scheduling and routing problem with skill requirements to minimize total operation time. A stochastic programming model is given to mathematically define it. Then, a hybrid approach combining the genetic algorithm (GA) and simulation optimization with an approximated allocation (AA) rule is designed. The GA aims to search candidate solutions, while the simulation optimization method focuses on improving the efficiency of evaluating them. Numerical results show that the AA rule is more effective than equal allocation and proportional to variance rules in guiding the GA to find promising solutions. As a consequence, the designed approach acquires the better solutions for the problem under consideration.

Inversion of groundwater contamination source based on a two-stage adaptive surrogate model-assisted trust region genetic algorithm framework

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.