Simulation Optimization Algorithm Research Articles

Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise

In this article we investigate the unconstrained optimization (minimization) of the performance of a system that is modeled through a discrete-event simulation. In recent years, several algorithms have been proposed which extend the traditional Kriging-based simulation optimization algorithms (assuming deterministic outputs) to problems with noise. Our objective in this paper is to compare the relative performance of a number of these algorithms on a set of well-known analytical test functions, assuming different patterns of heterogeneous noise. We also apply the algorithms to a popular inventory test problem. The conclusions and insights obtained may serve as a useful guideline for researchers aiming to apply Kriging-based algorithms to solve engineering and/or business problems, and may be useful in the development of future algorithms.

Use of Optimization Algorithm for Simulation of Spark Plasma Sintering Process in Si3N4-SiO2 Composite

Use of Optimization Algorithm for Simulation of Spark Plasma Sintering Process in Si3N4-SiO2 Composite

Optimal design of aerodynamic force supplementary devices for the improvement of fuel consumption and emissions

One of the primary concerns in the automotive industry is energy saving, protecting the global environment and fuel consumption reduction. The main purpose of this study is to develop optimal supplementary parts for reduction of aerodynamic force of highway tractor and trailer combinations to reduce aerodynamic drag, without negatively affecting the usefulness or profitability of the vehicles. In this article, the possibility to improve the aerodynamic performance for boosting fuel economy of trucks is studied by optimal designing of supplementary devices. That will be carried out by using integrated computational fluid dynamic and genetic algorithm for simulation and geometry optimization of applied devices. Also, simulation results are verified by experimental results in wind tunnel. For this purpose, effects of various supplementary devices and configuration are added to space between cabin and cargo compartment to stabilize the vortex and decrease the drag resistance force. Finally, the geometry of appended device with considering the installation and packing conditions is optimized by using a genetic algorithm. Through the analysis of airflow contours and optimization procedure, results indicate that using two plates at the sidewalls of the gap with optimized length and installation angle can reach the maximum reduction of drag force and fuel consumption by 20 and 10%, respectively.

Providing personalized system optimum traveler information in a congested traffic network with mixed users

ABSTRACTThe advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time traffic management in a congested network. This article proposes a personalized system optimum traveler information (PSOI) system under ubiquitous communication, which allows traffic system operators to fully optimize and coordinate individuals' trip plans according to the personal attributes, such as real-time location, value of time, allowable budgets for congestion tolling, and willingness to take detours. We also developed an efficient queue-based evaluation and solution heuristic algorithm using mesoscopic simulation models to solve for near-optimal PSOI strategies—route suggestions for each individual traveler. The simulation optimization algorithm can account for different information users and provide predictive information that robustly accounts for potential decisions of other travelers in real time. Case studies were carried out on a test network and a real-world network, and the proposed heuristic algorithm is proven effective. Also, sensitivity analyses show that PSOI not only is an effective traffic management method in reducing average system travel time, but also potentially provides travelers with reasonable or even shorter travel times compared with other information users. Further, simulation results showed that even in mixed traffic, PSOI is able to shorten travel times for both users without information and users of other information types. Thus, PSOI is recommended by this article as an advantageous way for next-generation advanced information systems and dynamic traffic management.

Multi-objective simulation optimization for medical capacity allocation in emergency department

This study aims to investigate medical resource allocation problems by multi-objective simulation optimization to address the long-term overcrowding situations experienced in hospital emergency departments (EDs). With resource constraints considered, decision makers at EDs must determine the number of doctors, nurses, lab technicians, and other medical equipment to allocate efficiently these medical resources and simultaneously minimize the average patient length of stay in the system and the medical resource wasted cost. Therefore, this study first proposes a multi-objective stochastic optimization model to identify the optimal number of all medical resources at the EDs. In addition, a multi-objective simulation optimization algorithm by integrating non-dominated sorting particle swarm optimization (NSPSO) with multi-objective computing budget allocation (MOCBA) and an ED simulation model is developed and constructed to address this problem, respectively. Specifically, NSPSO searches for potential solutions to medical resource allocation problems. MOCBA identifies effective sets of feasible Pareto medical resource allocation solutions and effective allocation of simulation replications. An ED simulation model based on the operation flows of EDs in Taiwan was constructed to estimate the expected performance value of each resource allocation solution generated by NSPSO. The effectiveness and performance of integrated NSPSO and MOCBA was verified by computational experiments.

Matlab enhanced multi-threaded tomography optimization sequence (MEMTOS)

One challenge associated with spent nuclear fuel assemblies is the lack of non-destructive analysis techniques to determine if fuel pins have been removed or replaced or if there are significant defects associated with fuel pins deep within a fuel assembly. Neutron computed tomography is a promising technique for addressing these qualitative issues. Monte Carlo simulation of spent nuclear fuel neutron computed tomography allows inexpensive process investigation and optimization. The main purpose of this work is to provide a fully automated advanced simulation framework for the analysis of spent nuclear fuel inspection using neutron computed tomography. The simulation framework, called Matlab Enhanced Multi-Threaded Tomography Optimization Sequence (MEMTOS) not only automates the simulation process, but also generates superior tomography image results. MEMTOS is written in the MATLAB scripting language and addresses file management, parallel Monte Carlo execution, results extraction, and tomography image generation. This paper describes the mathematical basis for neutron computed tomography, the Monte Carlo technique used to simulate neutron computed tomography, and the overall tomography simulation optimization algorithm. Sequence results presented include overall simulation speed enhancement, tomography and image results obtained for Experimental Breeder Reactor II spent fuel assemblies and light water reactor fuel assemblies. Optimization using a projection difference technique are also described.

Environmental decision-making under uncertainty using a biologically-inspired simulation-optimisation algorithm for generating alternative perspectives

In solving many environmental policy formulation applications, it is generally preferable to formulate several quantifiably good alternatives that provide multiple, disparate approaches to the problem. This is because environmental decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult - if not impossible - to quantify and capture at the time when supporting decision models must be constructed. By generating a set of maximally different solutions, it is hoped that some of the dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This maximally different solution creation approach is referred to as modelling to generate-alternatives (MGA). This paper provides a biologically-inspired metaheuristic simulation-optimisation MGA method that can efficiently create multiple solution alternatives to environmental problems containing significant stochastic uncertainties that satisfy required system performance criteria and yet are maximally different in their decision spaces. The efficacy of this stochastic MGA approach for environmental policy formulation is demonstrated using a municipal solid waste case study. It is shown that this new computationally efficient algorithmic approach can simultaneously produce the desired number of maximally different solution alternatives in a single computational run of the procedure.

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

An efficient simulation-optimization coupling for management of coastal aquifers

Seawater intrusion in coastal aquifers is a major environmental problem and efficient tools are needed to assist decision making. Decision tools are often simulation models (which evaluate probable actions) combined with optimization algorithms (which search for optimum management decisions). A coupling between simulation models and optimization algorithms for management of coastal aquifers is presented. The simulation models are based on the sharp-interface approximation where the decision variables do not affect the discretized system matrix. For such problems, a transformation of the system matrix prior to optimization is proposed which supports rapid solution of the linear system of equations during the optimization stage, for different values of decision variables. The method is applied to a hypothetical simulation of a coastal aquifer on the Greek island of Santorini, where the proposed simulation-optimization coupling method is employed to maximize pumping rates subject to environmental constraints that protect the aquifer from seawater intrusion. Various packages were tested in order to investigate their efficiency in solving the linear system pertinent to the case study. The proposed method, based on coupling of equations, is found to be very efficient in terms of computational cost. In particular, for the problem examined, it is at least 50 times faster than standard methods, depending on the grid size.

Iterative optimization algorithm with parameter estimation for the ambulance location problem.

The emergency vehicle location problem to determine the number of ambulance vehicles and their locations satisfying a required reliability level is investigated in this study. This is a complex nonlinear issue involving critical decision making that has inherent stochastic characteristics. This paper studies an iterative optimization algorithm with parameter estimation to solve the emergency vehicle location problem. In the suggested algorithm, a linear model determines the locations of ambulances, while a hypercube simulation is used to estimate and provide parameters regarding ambulance locations. First, we suggest an iterative hypercube optimization algorithm in which interaction parameters and rules for the hypercube and optimization are identified. The interaction rules employed in this study enable our algorithm to always find the locations of ambulances satisfying the reliability requirement. We also propose an iterative simulation optimization algorithm in which the hypercube method is replaced by a simulation, to achieve computational efficiency. The computational experiments show that the iterative simulation optimization algorithm performs equivalently to the iterative hypercube optimization. The suggested algorithms are found to outperform existing algorithms suggested in the literature.

<b>Development of an adaptive genetic algorithm for simulation optimization

Optimization methods in discrete-event simulation have become widespread in numerous applications. However, the methods´ performance falls sharply in terms of computational time when more than one decision variable is handled. Current assay develops an adaptive genetic algorithm for the simulation optimization capable of achieving satisfactory results in time efficiency and response quality when compared to optimization software packages on the market. A series of experiments was elaborated to define the algorithm’s most significant parameters and to propose adaptations. According to the results, the most significant parameters are population size and number of generations. Further, adaptive strategies were proposed for these parameters which enabled the algorithm to obtain good results in response quality and time necessary to converge when compared to a commercial software package.

A decision making framework with MODFLOW-FMP2 via optimization: Determining trade-offs in crop selection

Farmers in regions experiencing water stress or drought conditions can struggle to balance their crop portfolios. Periods of low precipitation often lead to increased, unsustainable reliance on groundwater-supplied irrigation. As a result, regional water management agencies place limits on the amount of water which can be obtained from groundwater, requiring farmers to reduce acreage for more water-intensive crops or remove them from the portfolio entirely. Real-time decisions must be made by the farmer to ensure viability of their farming operation and reduce the impacts associated with limited water resources. Evolutionary algorithms, coupled with accurate, flexible, realistic simulation tools, are ideal mechanisms to allow farmers to assess scenarios with regard to multiple, competing objectives. In order to effective, however, one must be able to select among a variety of simulation tools and optimization algorithms. Many simulation tools allow no access to the source code, and many optimization algorithms are now packaged as part of a suite of tools available to a user. In this work, we describe a framework for integrating these different software components using only their associated input and output streams. We analyze our strategy by coupling a multi-objective genetic algorithm available in the DAKOTA optimization suite (developed and distributed by Sandia National Laboratory) with the MODFLOW-FMP2 simulation tool (developed and distributed by the United States Geological Survey). MODFLOW-FMP2 has been used extensively to model hydrological and farming processes in agriculture-dominated regions, allowing us to represent both farming and conservation interests. We evaluate our integration by considering a case study related to planting decisions facing farmers experiencing water stress. We present numerical results for three competing objectives associated with stakeholders in a given region (i.e., profitability, meeting demand targets, and water conservation). The data obtained from the optimization are robust with respect to algorithmic parameter choices, validating the ability of the associated evolutionary algorithm to perform well without expert guidance. This is integral to our approach, as a motivation for this work is providing decision-making tools. In addition, the results from this study demonstrate that output from the chosen evolutionary algorithm provides a suite of feasible planting scenarios, giving farmers and policy makers the ability to compromise solutions based on realistic simulation data.

Integrating simulation with simulated annealing for scheduling physicians in an understaffed emergency department

In this paper, a physician scheduling problem in an emergency department (ED) is studied. A simulation-optimisation approach, which integrates simulation into simulated annealing is proposed to solve the problem. The algorithm explores solutions iteratively, where simulations are carried out to evaluate the solution quality at each step. With the proposed simulation-optimisation algorithm, a case study of an understaffed ED in Hong Kong is conducted. The historical data provided by the ED are used to draw some managerial insights into emergency-physician scheduling. It is found that (i) the best staffing level has a similar profile to the patient arrival rate, but shifts some period behind; (ii) the use of staggered shifts is helpful in reducing patient waiting time; (iii) physician scheduling decisions have a greater impact particularly when the ED is small; and (iv) ED pooling seems more beneficial than having a good physician schedule.

Research of Design Synthesis for MEMS Device of Serpentine Spring

The design synthesis of MEMS device is an inverse problem of the MEMS analysis. The performance analysis equations are transformed to the performance design equations according to the predetermined performance. The Direct Solution Algorithm derived by the Newton iterative method is used to solve the nonlinear performance design equations, and then the MEMS device will possess predetermined properties. The serpentine spring was synthesized with this method. The computational efficiency and accuracy is greatly improved by comparison with the foreign natural simulation optimization algorithms of the genetic algorithms. The genetic algorithms needs tens of thousands of times analyses, the relative error of the calculation is 1%, but this method only needs dozens of analyses and the relative error of the calculation is 0.01%.

Framework for the optimization of online computable models

Purpose – The purpose of this paper is to make easily accessible models to test and compare the optimization algorithms we develop. Design/methodology/approach – For this, the paper proposes an optimization framework based on software component, web service, and plugin to exploit these models in different environments. Findings – The paper illustrates the discussion with optimizations in Matlab™ and R (www.r-project.org) of a transformer described and exploitable from the internet. Originality/value – The originality is to make easy implementation of simulation model and optimization algorithm coupling using software component, web service, and plugin.

Logic hybrid simulation-optimization algorithm for distillation design

In this paper, we propose a novel algorithm for the rigorous design of distillation columns that integrates a process simulator in a generalized disjunctive programming formulation. The optimal distillation column, or column sequence, is obtained by selecting, for each column section, among a set of column sections with different number of theoretical trays. The selection of thermodynamic models, properties estimation, etc. is all in the simulation environment. All the numerical issues related to the convergence of distillation columns (or column sections) are also maintained in the simulation environment. The model is formulated as a Generalized Disjunctive Programming (GDP) problem and solved using the logic based outer approximation algorithm without MINLP reformulation. Some examples involving from a single column to thermally coupled sequence or extractive distillation shows the performance of the new algorithm.

Automated and Optimal Actuation Voltage Waveform Design for Contact Bouncing Mitigation of MEMS Switches

This paper presents an efficient methodology for automated optimal tailoring actuation voltage waveform of MEMS switches aiming at eliminating the detrimental contact bouncing effect to speed up the switching process and improve the mechanical reliability. This is a simulation-based approach where genetic algorithm (GA) is used in combination with a dedicated mechanical model of MEMS switch to derive optimal actuation waveform. The proposed technique has been implemented in SystemC-A, which is extremely well suited for complex modeling, implementation of post-processing of simulation results and optimization algorithms. Effectiveness of proposed approach is corroborated by a practical case study of automated actuation waveform design for a prefabricated DC-contact MEMS switch. The experimental results show that the switching time of the switch by employing optimized actuation voltage waveform is dramatically reduced to 60μs from 95μs, while the bouncing effect is successfully eliminated.

A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times

This paper describes a simulation–optimization algorithm for the Permutation Flow shop Problem with Stochastic processing Times (PFSPST). The proposed algorithm combines Monte Carlo simulation with an Iterated Local Search metaheuristic in order to deal with the stochastic behavior of the problem. Using the expected makespan as initial minimization criterion, our simheuristic approach is based on the assumption that high-quality solutions (permutations of jobs) for the deterministic version of the problem are likely to be high-quality solutions for the stochastic version – i.e., a correlation will exist between both sets of solutions, at least for moderate levels of variability in the stochastic processing times. No particular assumption is made on the probability distributions modeling each job-machine processing times. Our approach is able to solve, in just a few minutes or even less, PFSPST instances with hundreds of jobs and dozens of machines. Also, the paper proposes the use of reliability analysis techniques to analyze simulation outcomes or historical observations on the random variable representing the makespan associated with a given solution. This way, criteria other than the expected makespan can be considered by the decision maker when comparing different alternative solutions. A set of classical benchmarks for the deterministic version of the problem are adapted and tested under several scenarios, each of them characterized by a different level of uncertainty – variance level of job-machine processing times.

Discrete Optimum Design for Truss Structures by Subset Simulation Algorithm

AbstractThis article deals with the design optimization of truss structures with discrete design variables, which remains quite a challenging task in structural design. A new discrete search strategy based on the recently developed subset simulation optimization algorithm is proposed in detail for this type of structural optimization. The discrete design variables are transformed into standard normal variable space to implement the sampling procedure in subset simulation optimization, while the optimization is processed in the discrete design space in the mean time. The performance of the proposed method is illustrated by four representative benchmark optimization problems. Comparisons are made with other well known stochastic optimization algorithms. It is found that the proposed method can produce optimum designs as good as or better than those of other stochastic optimization algorithms.

Simultaneous Perturbation Newton Algorithms for Simulation Optimization

We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman---Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work.