Simulation Optimization Algorithm Research Articles

A robust simulation optimization algorithm using kriging and particle swarm optimization: Application to surgery room optimization

Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process) metamodel. The particle swarm optimization algorithm is applied in consideration of the uncertainty of the problem parameters to solve for a robust optimal answer. Finally, the performance of the proposed algorithm is highlighted through examples of numerical simulation and comparisons with other methods in this area, and it is applied to the surgery room optimization problem as a case study.

Cavitation optimization of a throttle orifice plate based on three-dimensional genetic algorithm and topology optimization

A throttle orifice plate is a mechanical device that is used to throttle and depressurize a fluid piping system. In the pipeline, the throttle orifice plate serves as a hindrance to the flow of liquid, leading to the phenomenon of cavitation. An optimization method based on 3D geometric model simulation, genetic algorithm, and topology optimization algorithm is proposed to optimize the structure of the throttle orifice plate. The flow path optimization method is applied to the genetic algorithm through the simulation results, while three-dimensional flow path model is achieved with minimum cavitation region under the condition of flow constraint. Then, on the basis of the flow path model, the optimal solution of the structure is achieved through the topology optimization model. To reduce the computational resources required for topology optimization, an adaptive parameter function of bi-directional evolutionary structural optimization(BESO) method is proposed, which reduces the iterations for simulation convergence. Our results show that the method is effectively applied to the design of the throttle orifice plate under flow requirements and structure limits.

TD-OCBA: Optimal computing budget allocation and time dilation for simulation optimization of manufacturing systems

Discrete event simulation has been widely applied to study the behavior of stochastic manufacturing systems. This is due to the fact that manufacturing systems are usually too complex to obtain a closed-form analytical model that can accurately predict their performance. This becomes particularly critical when the optimization of these systems is of concern. In fact, Simulation optimization techniques are employed to identify the manufacturing system configuration which can maximize the expected system performance when this can only be estimated by running a simulator. In this article, we look into simulation-based optimization when a finite number of solutions are available and we have to identify the best. In particular, we propose, for the first time, the integration of Optimal Computing Budget Allocation (OCBA), which is based on independent measures from each simulation experiment, and Time Dilation (TD), which is a single-run simulation optimization algorithm. As a result, the optimization problem is solved when only one experiment of the system is performed by changing the “speed” of the simulation at each configuration in order to control the computational effort. The challenge is how to iteratively select such a speed. We solve this problem by proposing TD-OCBA, which integrates TD and OCBA while relying on standardized time series variance estimators. Numerical experiments have been conducted to study the performance of the algorithm when the response is generated from a time series. This provides the possibility to test the robustness of TD-OCBA. Comparison between TD-OCBA and the original TD method was performed by simulating a job shop system reported in the literature. Finally, an application involving semiconductors remote diagnostics is used to compare the TD-OCBA method and what is known as the equal allocation method.

Risk averse sourcing in a stochastic supply chain: A simulation-optimization approach

A growing need for global sourcing has forced firms to manage more complex supply chains with increasing risks of supply disruptions. Multi sourcing is a common method to hedge against these risks. In the presence of demand uncertainties and supply disruptions, minimizing the downside risk is necessary. Hence, in this paper a new multi-period and scenario based supply chain model consists of a number of unreliable suppliers and a number of retailers is developed in the form of a multi-period newsvendor problem with a risk averse objective function. In the model, there are two types of retailers both faced uncertain demands: risk sensitive and risk neutral. Retailers have three choices to respond to the customer demand: a forward contract, and two option contracts include reserving a certain capacity in the secondary supplier and buying from the spot market. The problem has also developed as an agent-based system. As a solution approach in the large scale problem instances, a simulation-optimization algorithm is developed. Two kinds of heuristics are compared in order to optimize the simulation procedure: genetic algorithm and q-learning. Results showed the efficiency of the q-learning algorithm.

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

Dual-mode inventory management under a chance credit constraint

We study a dual-mode inventory management problem of a high-value component where the customer demand and the regular transportation lead time are stochastic, and the review periods of the two modes are different. The manufacturer is subject to a chance credit constraint that bounds the working capital. To solve the resulting chance-constrained stochastic optimization problem, we develop a hybrid simulation optimization algorithm that combines the modified nested partitions method as the global search framework, a feasibility detection procedure for chance constraint verification, and a $$\hbox {KN}{++}$$ procedure as the final “cleanup” procedure to ensure solution quality. We are then able to analyze the impact of the chance credit constraint on the inventory policies and operational cost. Our numerical study shows that the effects of the reduction in mean or variance of the regular transportation lead time depend on whether the chance credit constraint is loose or tight. We show in this way that this tightness may lead to different mechanisms dominating the observed behavior. Further, we show that substantially extending the deterministic credit limit is less effective than having a slight increase in the probability parameter of the chance credit constraint.

Minimizing Expected Cycle Time of Stochastic Customer Orders Through Bounded Multi-Fidelity Simulations

This paper considers the scheduling of stochastic customer orders to minimize expected cycle time. Customer orders dynamically arrive at a machine station, and each order consists of multiple product types. Random workloads are required by each product type, and the workloads are assigned to a set of unrelated parallel machines in the station to be processed. The objective is to obtain the minimal long-run expected order cycle time through proper workload assignments. In view of the difficulty in evaluating the objective function, this paper models the targeted problem as a simulation optimization problem and proposes to solve it under the multifidelity model framework. To improve the efficiency in evaluating candidate solutions, a low-fidelity model is constructed to select solutions with better performances for high-fidelity simulations. The effectiveness of this low-fidelity model is demonstrated through a series of theoretical evidences. A simulation optimization algorithm, named Bound-Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling Bound-(MO2TOS), is developed by taking advantage of the properties of the low-fidelity model. Numerical experiments are conducted to evaluate the performance of the proposed algorithm against three other well-known simulation optimization algorithms in the literature. Results indicate that the Bound-MO2TOS outperforms all the other tested algorithms, and its performance is robust against changes in problem scale. Note to Practitioners —Customer order scheduling models have wide applications in industries where every single order may contain multiple product types and the entire order requires one shipment delivery. In many applications, such as pigments dyes manufacturing, auto repair, and grocery, consideration of customer orders rather than individual jobs is often preferred. Given the stochastic nature and the synchronization constraints of the problem, a multifidelity simulation algorithm named Bound-Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling Bound-(MO2TOS) is proposed. Experimental results suggest that the proposed algorithm outperforms three other popular simulation optimization algorithms under a variety of scenarios. It is admitted that further improvement can be made in refining the computation resource distribution process of Bound-MO2TOS. In the future research, such an improvement will be more thoroughly elaborated so that practitioners can utilize it to coordinate their productions better.

Redundancy Optimization of an Uncertain Parallel-Series System with Warm Standby Elements

The redundancy optimization problem is formulated for an uncertain parallel-series system with warm standby elements. The lifetimes and costs of elements are considered uncertain variables, and the weights and volumes of elements are random variables. The uncertain measure optimization model (UMOM), the uncertain optimistic value optimization model (UOVOM), and the uncertain cost optimization model (UCOM) are developed through reliability maximization, lifetime maximization, and cost minimization, respectively. An efficient simulation optimization algorithm is provided to calculate the objective values and optimal solutions of the UMOM, UOVOM, and UCOM. A numerical example is presented to illustrate the rationality of the models and the feasibility of the optimization algorithm.

A simulation-based vessel-truck coordination strategy for lighterage terminals

Lighterage contributes significantly to the maritime industry since thousand tons of necessary supplies have to be delivered to mother vessels anchored near sea every day. A single lighter waits to receive cargoes from multiple suppliers at the berth, while the trucks wait for their turns to enter the terminal. Due to the limited space, the terminal operation becomes vulnerable to the lack of coordination of their arrivals. In this study, despite a traditional industry, we are interested in applying the emerging technologies to improve the operation efficiency. We develop a simulation-based coordination strategy to construct coordinated schedules for both the lighters and suppliers to reduce congestions in the lighterage terminal The discrete event simulation model is developed based on understanding the real-world terminal in Singapore, and the controlled arrival method, determining coordinated arrival schedules of trucks, is introduced and embedded to the simulation model. The simulation model is tuned up with a benchmark setting of 6-month historical data. To find the optimal strategy, an advanced bi-objective simulation optimization algorithm is employed. According to our findings, the proposed strategy could significantly improve the efficiency of both lighters and trucks in various indicators. At the end, a mobile application prototype is proposed to deliver the coordinated schedules to different parties, and improve the communication between parties.

A novel optimal replication allocation strategy for particle swarm optimization algorithms applied to simulation optimization problem

This study proposes a simulation optimization algorithm, called optimal replication allocation strategy (ORAS), for particle swarm optimization (PSO) algorithms in simulation models with high computing cost in large design spaces. In this study, the PSO is employed to explore and exploit near-optimal or optimal solution in the design space. Given the uncertainties in real-word applications, a simulation model is constructed to evaluate the performance of each design alternative. Optimal computing budget allocation (OCBA), a state-of-the-art resampling method, is combined with metaheuristic principles to improve the accuracy of estimating best solutions and enhancing efficiency by intelligently allocating the number of replications. However, the solution space is nonlinear or multimodal; that is, various local or global solutions exist. In OCBA, the probability of correct selection (P(CS)) in the currect best solution serves as an important measurement. P(CS) refers to the probability that the “best” of k populations is selected according to some specified criteria of best. OCBA can halt allocation when P(CS) reaches higher than the desired value, i.e., P(CS)*. The multimodal solution space features a high probability of reaching a low P(CS) as many solutions perform closely to the current best of each generation. This situation indicates that P(CS) cannot achieve P(CS)*; OCBA cannot stop allocation, and additional computational cost may be wasted. In this study, we redefine and modify P(CS). The new version is P(CSE), which considers calculation of global best, called the super individual, instead of current best solution within a confidence level. The proposed ORAS can provide an asymptotically optimal allocation rule for combining with population metaheuristics based on P(CSE). We apply ORAS using an original particle swarm optimization (PSO) and two variants of PSO to address stochastic buffer allocation problem and stochastic function optimization problem compared with several state-of-the-art technologies from literature. The resulting ORAS with three PSOs is an effective procedure as it intelligently utilizes limited computing resources. Numerical tests indicate that ORAS increases P(CSE) in each generation and subsequently enhances the efficiency of PSO algorithms.

Optimally configuring a measurement system to detect diversions from a nuclear fuel cycle

The civilian nuclear fuel cycle is an industrial process that produces electrical power from the nuclear fission of uranium. Using a measurement system to accurately account for possibly dangerous nuclear material, such as uranium, in a fuel cycle is important because of its possible loss or diversion. A measurement system is defined by a set of measurement methods, or “devices,” used to account for material flows and inventory values at specific locations at facilities in the fuel cycle. We develop a simulation-optimization algorithm and an integer-programming model to find the best, or near-best, resource-limited measurement system with a high degree of confidence. The simulation-optimization algorithm minimizes a weighted sum of false positive and false negative diversion-detection probabilities while accounting for material quantities and measurement errors across a finite, discrete time horizon in hypothetical non-diversion and diversion contexts. In each time period, the estimated cumulative material unaccounted for is compared to a fixed or an optimized threshold value to assess if a “significant amount of material” is lost from a measurement system. The integer-programming model minimizes the population variance of the estimated material loss, i.e., material unaccounted for, in a measurement system. We analyze three potential problems in nuclear fuel cycle measurement systems: (i) given location-dependent device precisions, find the configuration of n devices at n locations ( $$n=3$$ ) that provides the lowest corresponding objective values using the simulation-optimization algorithm and integer-programming model, (ii) find the location at which improving device precision reduces objective values the most using the simulation-optimization algorithm (given the device accuracy is 100%), and (iii) determine the effect of measurement frequency on measurement system configurations and objective values using the simulation-optimization algorithm. We obtain comparable results for each problem at least an order of magnitude faster than existing methods do. Using an optimized, rather than a fixed, detection threshold in the simulation-optimization algorithm reduces the weighted sum of false positive and false negative probabilities.

Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle

The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the [Formula: see text]-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

A variable neighborhood search simheuristic for project portfolio selection under uncertainty

With limited financial resources, decision-makers in firms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash flows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases.

Multiobjective generalized extremal optimization algorithm for simulation of daylight illuminants

Daylight illuminants are widely used as references for color quality testing and optical vision testing applications. Presently used daylight simulators make use of fluorescent bulbs that are not tunable and occupy more space inside the quality testing chambers. By designing a spectrally tunable LED light source with an optimal number of LEDs, cost, space, and energy can be saved. This paper describes an application of the generalized extremal optimization (GEO) algorithm for selection of the appropriate quantity and quality of LEDs that compose the light source. The multiobjective approach of this algorithm tries to get the best spectral simulation with minimum fitness error toward the target spectrum, correlated color temperature (CCT) the same as the target spectrum, high color rendering index (CRI), and luminous flux as required for testing applications. GEO is a global search algorithm based on phenomena of natural evolution and is especially designed to be used in complex optimization problems. Several simulations have been conducted to validate the performance of the algorithm. The methodology applied to model the LEDs, together with the theoretical basis for CCT and CRI calculation, is presented in this paper. A comparative result analysis of M-GEO evolutionary algorithm with the Levenberg– Marquardt conventional deterministic algorithm is also presented

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes.

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

Optimal inventory control policy of a hybrid manufacturing – remanufacturing system using a hybrid simulation optimisation algorithm

Remanufacturing is the process of bringing used products back to like-new products. In this study, inventory control policies in remanufacturing with different prioritisations (remanufacturing vs. manufacturing) and coordination (non-coordinating vs. coordinating) are investigated. A proposed hybrid simulation optimisation algorithm, where outputs are exchanging between Mixed-Integer Linear Programming and simulation models, is presented to search for optimality. Obtained results are then compared with the results obtained from the pure analytical model and simulation-based optimisation where the proposed Hybrid Algorithm outperforms other solving methods by obtaining a statistically higher profit, using less number of iterations to find the optimal result. Regarding the inventory control policy, it is found that the returned component ratio (proportion of returned components as compared to the actual customer demand) has an effect on the inventory control policy. The outcome of the study can recommend the best operating solution at each level of the returned component ratios under an uncertain environment.

Fixture Layout Design of Sheet Metal Parts Based on Global Optimization Algorithms

Fixture layout can affect deformation and dimensional variation of sheet metal assemblies. Conventionally, the assembly dimensions are simulated with a quantity of finite element (FE) analyses, and fixture layout optimization needs significant user intervention and unaffordable iterations of finite element analyses. This paper therefore proposes a fully automated and efficient method of fixture layout optimization based on the combination of 3dcs simulation (for dimensional analyses) and global optimization algorithms. In this paper, two global algorithms are proposed to optimize fixture locator points, which are social radiation algorithm (SRA) and GAOT, a genetic algorithm (GA) in optimization toolbox in matlab. The flowchart of fixture design includes the following steps: (1) The locating points, the key elements of a fixture layout, are selected from a much smaller candidate pool thanks to our proposed manufacturing constraints based filtering methods and thus the computational efficiency is greatly improved. (2) The two global optimization algorithms are edited to be used to optimize fixture schemes based on matlab. (3) Since matlab macrocommands of 3dcs have been developed to calculate assembly dimensions, the optimization process is fully automated. A case study of inner hood is applied to demonstrate the proposed method. The results show that the GAOT algorithm is more suitable than SRA for generating the optimal fixture layout with excellent efficiency for engineering applications.

Fleet size optimization in the discarded tire collection process

Due to rapid growth in urban populations, waste collection in cities has become a topic of great concern, due to it impacts on environmental pollution. Regarding used tires, the effect on human health and the environment is a matter of great concern for urban authorities. Therefore, in this paper, we analyze the recovery process for discarded tires. In particular, we study a Reverse Supply Chain Network Design (RSCND), which is designed according to three fundamental echelons (i.e. waste collection, processing and customer zones). To this end, the simultaneous application of simulation techniques and optimization algorithms is proposed. Both approaches are framed within a general methodology, which has been taken from the Operations Research literature. We present a case study from a Colombian city. The combined algorithmic approaches, which include discrete simulation and multiobjective optimization, provide sufficient evidence regarding ways to optimally manage fleet size in the RSCND. Experimental results indicate that the discarded tire recovery process can be designed to be more profitable, as well as less environmentally harmful.

Optimization of Hedging Rules for Hydropower Reservoir Operation

Reservoir operation plays an important role in economic development of a region. Hedging operations were used for municipal, industrial and irrigation water supplies from reservoirs in the past. However, hedging operation for hydropower reservoir operation is very rare. Practically simple and useful new form of Standard Operation Policy and new form of hedging rules for hydropower production are introduced in this paper and demonstrated with a case study for hydropower reservoir operation of Indirasagar reservoir system in India. The performances of optimal hedging rules were compared with that of a new standard operation policies and the superiority (reliability increases by about 10%) of the hedging rules are presented. When the number of decision variables is increased from 5 to 15, the energy production is increased by 0.7 %, the spill is reduced by 16.8 % while the reliablity is decreasing slightly by 2.1 %. A bi-level simulation-optimization algorithm was used for optimizing the hedging rules. For optimization, Genetic Algorithm, artificial bee colony algorithm and imperialistic competitive algorithms were attempted. The results indicate that all the three algorithms are competitive and artificial bee colony algorithm is marginally better than the other two.

Adaptive System Optimization Using Random Directions Stochastic Approximation

We present new algorithms for simulation optimization using random directions stochastic approximation (RDSA). These include first-order (gradient) as well as second-order (Newton) schemes. We incorporate both continuous-valued as well as discrete-valued perturbations into both types of algorithms. The former are chosen to be independent and identically distributed (i.i.d.) symmetric uniformly distributed random variables (r.v.), while the latter are i.i.d. asymmetric Bernoulli r.v.s. Our Newton algorithm, with a novel Hessian estimation scheme, requires $N$ -dimensional perturbations and three loss measurements per iteration, whereas the simultaneous perturbation Newton search algorithm of [1] requires $2N$ -dimensional perturbations and four loss measurements per iteration. We prove the asymptotic unbiasedness of both gradient and Hessian estimates and asymptotic (strong) convergence for both first-order and second-order schemes. We also provide asymptotic normality results, which in particular establish that the asymmetric Bernoulli variant of Newton RDSA method is better than 2SPSA of [1] . Numerical experiments are used to validate the theoretical results.