Simulation-based Optimization Algorithm Research Articles

Simulation-based integrated optimization of nesting policy and booking limits for revenue management

Nesting control is one of the most prevalent quantity-based controls for revenue management (RM) problem. This type of control sets nested booking limits to avoid the situation in which high-fare bookings are rejected in favor of low-fare class. There are two nesting policies that are commonly adopted in practical application, namely standard nesting policy (SNP) and theft nesting policy (TNP). Existing research usually chooses one of them and then studies how to optimize the booking limits under the selected policy. In this paper, we newly introduce a generalized nesting policy (GNP) that can enrich the family of nesting policies. It is certified that the traditional SNP and TNP are special cases of GNP. A mathematical model for the nesting control under GNP is proposed, in which the nesting policy and booking limits are both taken as the decision variables. Followed, a simulation-based optimization algorithm integrated of simulated annealing (SA) algorithm and finite difference (FD) algorithm is designed to explore an improved solution to the proposed model. In the integrated algorithm, SA is used to search a better nesting policy and FD is to further optimize the booking limits under the current policy. Numerical experiments are conducted on a three-leg airline network with customer choice behavior and they mainly show three aspects of findings. First, the solutions obtained by simulation-based algorithms make significant improvements of revenues compared to the popular EMSR heuristics. Second, on average, GNP outperforms traditional nesting policies SNP and TNP. Third, in most of arrival patterns, GNP has no clear advantage over SNP and TNP, but in about 18% of arrival patterns, some new nesting policies embedded in GNP make significant improvements.

Simulation-Based Coyote Optimization Algorithm to Determine Gains of PI Controller for Enhancing the Performance of Solar PV Water-Pumping System

In this study, a simulation-based coyote optimization algorithm (COA) to identify the gains of PI to ameliorate the water-pumping system performance fed from the photovoltaic system is presented. The aim is to develop a stand-alone water-pumping system powered by solar energy, i.e., without the need of electric power from the utility grid. The voltage of the DC bus was adopted as a good candidate to guarantee the extraction of the maximum power under partial shading conditions. In such a system, two proportional-integral (PI) controllers, at least, are necessary. The adjustment of (Proportional-Integral) controllers are always carried out by classical and tiresome trials and errors techniques which becomes a hard task and time-consuming. In order to overcome this problem, an optimization problem was reformulated and modeled under functional time-domain constraints, aiming at tuning these decision variables. For achieving the desired operational characteristics of the PV water-pumping system for both rotor speed and DC-link voltage, simultaneously, the proposed COA algorithm is adopted. It is carried out through resolving a multiobjective optimization problem employing the weighted-sum technique. Inspired on the Canis latrans species, the COA algorithm is successfully investigated to resolve such a problem by taking into account some constraints in terms of time-domain performance as well as producing the maximum power from the photovoltaic generation system. To assess the efficiency of the suggested COA method, the classical Ziegler–Nichols and trial–error tuning methods for the DC-link voltage and rotor speed dynamics, were compared. The main outcomes ensured the effectiveness and superiority of the COA algorithm. Compared to the other reported techniques, it is superior in terms of convergence rapidity and solution qualities.

Solving patient referral problems by using bat algorithm.

BACKGROUND:A two-hospital patient referral problem intends to calculate an optimal value of referral patients between two hospitals and to evaluate whether or not the current number of referral patients is too low.OBJECTIVE: The goal of this study is to develop a simulation-based optimization algorithm to find the optimal referral between two hospitals with the unfixed daily patient referral policy.METHODS: This study applied system simulation and a bat algorithm (BA) to build a simulation model in accordance with the status of the two hospitals case and to calculate an optimal value of daily referral patients.RESULTS:Based on the 20 test instances, we verified the stability of this algorithm. The results show that the average magnetic resonance imaging (MRI) patient wait time reduced from 16 days to eight days. The hospital should increase the average total monthly MRI referral patients to 370 under the limitation of the daily referral patients to 25.CONCLUSIONS: This research investigated the two-hospital patient referral problems. We conducted and analyzed a simulation model and improved the case hospital’s conditions, enhancing the quality of its medical care. The findings of this study can extend to other departments or hospitals.

Aggregated GP-based Optimization for Contaminant Source Localization

Recently a new simulation-based optimization benchmark of groundwater contaminant source localization problems has been introduced to the hydrogeological science community. Given information on contaminant concentration levels at each monitoring well and each time step, its objective is to identify the location of contaminant source. In this work, we analyze and look at the problem from different angles to gain more insights on this class of groundwater problems. To tackle the problem, a novel simulation-based optimization algorithm relying on an aggregated Gaussian process model, and the expected improvement criterion is introduced. Results from this study show that the proposed algorithm, though relying on an approximated Gaussian process model, demonstrates superior efficiency and reliability than a traditional expected improvement-based algorithm. The location of the monitoring wells was confirmed to play a crucial role in assisting the optimization algorithm to accurately localize the contaminant source. Additional monitoring wells, while adding more knowledge of the space-time mapping of concentration levels, could nevertheless slow down convergence of the algorithm due to the increase in problem complexity.

A simulation-based optimization approach to design optimal layouts for block stacking warehouses

Storing pallets of products on top of one another on the floor of a warehouse is called block stacking. The arrangement of lanes, aisles, and cross-aisles in this storage system affects both utilization of the storage space and material handling costs; however, the existing literature focuses exclusively on lane depths and their impact on space utilization. This paper fills this gap and studies the optimal layout design for block stacking, which includes determining the numbers of aisles and cross-aisles, bay depths, and cross-aisle types. We show that lane depths affect material handling cost in addition to space utilization and develop a simulation-based optimization algorithm to find optimal layouts with respect to both of these objectives. We also propose a closed-form solution to the optimal number of aisles in a layout. The results of a case study in the beverage industry show that the resulting layout can save up to ten percent of the operational costs of a warehouse. We present the computational efficiency of the algorithm and some insights into the problem through an exhaustive experimental analysis based on various test problems that cover small- to industrial-sized warehouses.

A method for optimizing installation capacity and operation strategy of a hybrid renewable energy system with offshore wind energy for a green container terminal

The contribution of this paper is to provide a method for optimizing installation capacity and operation strategy of a hybrid renewable energy system (HRES) with offshore wind energy for container terminals. A mixed integer optimization model of the HRES is proposed, in which maximum environmental and economic benefits are considered as two objectives, and energy supply and demand balances, energy storage balances, technical constraints and quantity limitations are considered as constraints. However, container terminals typically execute many complicated and stochastic operation processes, and thus represent highly dynamic energy demands. Therefore, a simulation-based optimization algorithm is developed to resolve the optimization model, which consists of a simulation model and an optimization module. The simulation model is constructed to overcome the stochastic characteristics and obtain the hourly energy demands in container terminals, which are fed into the optimization module. While, the optimization module is used to search the solution space and provide the final decisions under three evaluation strategies: cost-saving, low-carbon, and trade-off strategy. Finally, taking a container terminal in Northeast China as an example, optimal results are obtained by the proposed algorithm, which can be used to provide references for power department policy making and green container terminal construction.

Simulation-Based Travel Time Reliable Signal Control

This paper addresses a travel time reliable signal control problem. Travel time distributional estimates are obtained from a stochastic microscopic traffic simulator. The estimates are embedded within a simulation-based optimization algorithm. Analytical approximations of the simulated metrics are combined with the simulated data in order to enhance the computational efficiency of the algorithm. The signal control problems are formulated based on the expectation and the standard deviation of travel time metrics. The proposed approach goes beyond the traditional use of first-order simulated information, it addresses a problem that embeds higher-order distributional information. It is used to solve a large-scale signal control problem. The approach addresses these challenging simulation-based optimization problems in a computationally efficient manner. Its performance is compared to that of a traditional simulation-based optimization approach. The proposed method systematically outperforms the traditional approach. Such an approach can be used to inform the design and operations of transportation systems by, for instance, addressing reliable and/or robust formulations of traditional transportation problems. The online appendix is available at https://doi.org/10.1287/trsc.2017.0812 .

An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse Disaster

With the gradual expansion of high buildings and underground spaces, deep foundation pits have been widely used in these engineering projects, but if they are not well-designed, safety problems occur. Proper deep foundation pit design requires proper exit distribution. However, calculating an adequate number of exit distributions for evaluation is difficult due to the numerous influential factors existing in the deep foundation pit environment. To this end, this paper presents a prototype of a decision-making system that uses agent-based modeling to simulate deep foundation pit evacuation in the presence of collapse disaster. By modeling the collapse occurrence process and agent escape process, an agent-based evacuation model is built, and a modified simulation-based particle swarm optimization algorithm is used to solve the optimization problem of exit distribution. Extensive experiments are conducted to verify the system, and the results show that the system provides a feasible framework for deep foundation pit evacuation.

Model-based Optimization for Architectural Design: Optimizing Daylight and Glare in Grasshopper

Model-based optimization is an innovative optimization strategy and particularly appropriate for time-intensive performance simulations. To demonstrate this appropriateness, this paper reviews simulation-based optimization algorithms and benchmarks several (single- and multi-objective) optimization tools on two problems involving annual daylight and glare simulations. The benchmarks demonstrate that model-based optimization outperforms other (single- and multi-objective) approaches on time-intensive, simulation-based optimization problems and thus puts new applications within reach. In this way, model-based optimization aids architectural designers and consultants to develop more resource- and energy-efficient buildings.

A computational approach to economic production quantity model for perishable products with backordering shortage and stock-dependent demand

This paper deals with an Economic Production Quantity (EPQ) model to determine production-inventory policies for perishable products. Shortage is permitted and fully backordered. The demand rate is stochastic- and stock-dependent. Since the problem is mathematically challenging and intractable via analytical approaches, this paper designs a simulation-based optimization algorithm by combining a grid search and a simulation model to solve the problem. The grid search plays the role of optimizer to determine the model variables, and the simulation model is utilized to evaluate the quality of solutions obtained by the optimizer through an iterative procedure. Eventually, a numerical example is discussed to illustrate how the solution procedure works, and a comparison study is carried out to demonstrate the superiority of suggested approach.Moreover, a comprehensive sensitivity analysis with respect to the problem parameters is performed.

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

This paper addresses large-scale urban transportation optimization problems with time-dependent continuous decision variables, a stochastic simulation-based objective function, and general analytical differentiable constraints. We propose a metamodel approach to address, in a computationally efficient way, these large-scale dynamic simulation-based optimization problems. We formulate an analytical dynamic network model that is used as part of the metamodel. The network model formulation combines ideas from transient queueing theory and traffic flow theory. The model is formulated as a system of equations. The model complexity is linear in the number of road links and is independent of the link space capacities. This makes it a scalable model suitable for the analysis of large-scale problems. The proposed dynamic metamodel approach is used to address a time-dependent large-scale traffic signal control problem for the city of Lausanne. Its performance is compared to that of a stationary metamodel approach. The proposed approach outperforms the stationary approach. This comparison illustrates the added value of providing the algorithm with analytical dynamic problem-specific structural information. The performance of a signal plan derived by the proposed approach is also compared to that of an existing signal plan for the city of Lausanne, and to that of a signal plan derived by a mainstream commercial signal control software. The proposed method can systematically identify signal plans with good performance. The online appendix is available at https://doi.org/10.1287/trsc.2016.0717 .

Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO2-eq vs. Discounted Payback Time

Building energy design is a multi-objective optimization problem where collective and private perspectives conflict each other. For instance, whereas the collectivity pursues the minimization of environmental impact, the private pursues the maximization of financial viability. Solving such trade-off design problems usually involves a big computational cost for exploring a huge solution domain including a large number of design options. To reduce that computational cost, a bi-objective simulation-based optimization algorithm, developed in a previous study, is applied in the present investigation. The algorithm is implemented for minimizing the CO2-eq emissions and the discounted payback time (DPB) of a single-family house in cold climate, where 13,456 design solutions including building envelope and heating system options are explored and compared to a predefined reference case. The whole building life is considered by assuming a calculation period of 30 years. The results show that the type of heating system significantly affects energy performance; notably, the ground source heat pump leads to the highest reduction in CO2-eq emissions, around 1300 kgCO2-eq/m2, with 17 year DPB; the oil fire boiler can provide the lowest DPB, equal to 8.5 years, with 850 kgCO2-eq/m2 reduction. In addition, it is shown that using too high levels of thermal insulation is not an effective solution as it causes unacceptable levels of summertime overheating. Finally a multi-objective decision making approach is proposed in order to enable the stakeholders to choice among the optimal solutions according to the weight given to each objective, and thus to each perspective.

Capacity planning for mega container terminals with multi-objective and multi-fidelity simulation optimization

ABSTRACTContainer terminals play a significant role as representative logistics facilities for contemporary trades by handling outbound, inbound, and transshipment containers to and from the sea (shipping liners) and the hinterland (consignees). Capacity planning is a fundamental decision process when constructing, expanding, or renovating a container terminal to meet demand, and the outcome of this planning is typically represented in terms of configurations of resources (e.g., the numbers of quay cranes, yard cranes, and vehicles), which enables the container flows to satisfy a high service level for vessels (e.g., berth-on-arrivals). This study presents a decision-making process that optimizes the capacity planning of large-scale container terminals. Advanced simulation-based optimization algorithms, such as Multi-Objective Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO-MO2TOS), Multi-Objective Optimal Computing Budget Allocation (MOCBA), and Multi-Objective Convergent Optimization via Most-Promising-Area Stochastic Search (MO-COMPASS), were employed to formulate and optimally solve the large-scale multi-objective problem with multi-fidelity simulation models. Various simulation results are compared with one another in terms of the capacities over different resource configurations to understand the effect of various parameter settings on optimal capacity across the algorithms.

Simulation-Based Optimization: Achieving Computational Efficiency Through the Use of Multiple Simulators

Transportation agencies often resort to the use of traffic simulation models to evaluate the impacts of changes in network design or network operations. They often have multiple traffic simulation tools that cover the network area where changes are to be made. These multiple simulators may differ in their modeling assumptions (e.g., macroscopic versus microscopic), in their reliability (e.g., quality of their calibration), as well as in their modeling scale (e.g., city-scale versus regional-scale). The choice of which simulation model to rely on, let alone of how to combine their use, is intricate. A larger-scale model may, for instance, capture more accurately the local-global interactions; yet may do so at a greater computational cost. This paper proposes an optimization framework that enables multiple simulation models to be jointly and efficiently used to address continuous urban transportation optimization problems. We propose a simulation-based optimization algorithm that embeds information from both a high-accuracy low-efficiency simulator and a low-accuracy high-efficiency simulator. At every iteration, the algorithm decides which simulator to evaluate. This decision is based on an analytical approximation of the accuracy loss as a result of running the lower-accuracy model. We formulate an analytical expression that is based on a differentiable and computationally efficient to evaluate traffic assignment model. We evaluate the performance of the algorithm with a traffic signal control problem on both a small network and a city network. We show that the proposed algorithm identifies signal plans with excellent performance, and can do so at a significantly lower computational cost than when systematically running the high-accuracy simulator. The proposed methodology contributes to enable large-scale high-resolution traffic simulation models to be used efficiently for simulation-based optimization. More broadly, it enables the use of multiple simulation models that may differ, for instance, in their scale, their resolution, or their computational costs, to be used jointly for optimization.

Multi-period portfolio selection with dynamic risk/expected-return level under fuzzy random uncertainty

In this study, we discuss multi-period portfolio selection problems when security returns are described as fuzzy random variables. The main concern of this work is to apply dynamic risk tolerance and expected return levels in mathematical modeling; i.e., these two indices of each period are influenced by the investment result of the previous period as well as human risk attitudes instead of static values over the entire investment horizon. Essentially, this assumption is based on the reality that investors tend to update targets when their wealth changes. In addition, fuzzy random variables are employed here to incorporate historical data with expert knowledge when estimating security future returns. Based on the above considerations, two multi-period portfolio selection models are built in light of the different risk attitudes. We then provide property analysis on complicated nonlinear optimization problems and derive several equivalents of the models, which can be solved by the existing dynamic programming. In general situations, a fuzzy random simulation-based particle swarm optimization algorithm is developed to search for approximate optima. The performance of this research is exemplified by a real market data-based case study in which the superiority of the dynamic strategy is demonstrated by a comparison with conventional approaches.

Finding Optimal Observation-Based Policies for Constrained POMDPs Under the Expected Average Reward Criterion

In this technical note, constrained partially observable Markov decision processes with discrete state and action spaces under the average reward criterion are studied from a sensitivity point of view. By analyzing the derivatives of performance criteria, we develop a simulation-based optimization algorithm to find the optimal observation-based policy on the basis of a single sample path. This algorithm does not need any overly strict assumption and can be applied to the general ergodic Markov systems with the imperfect state information. The performance is proved to converge to the optimum with probability 1. One numerical example is provided to illustrate the applicability of the algorithm.

Simulation-Based Gradient-Descent Optimization of Modular Multilevel Converter Controller Parameters

One of the main technical challenges associated with the modular multilevel converter (MMC) for any application is the optimal control of its states. Optimal control of the MMC requires determination of the following: 1) an accurate MMC model, 2) the MMC states to be controlled and their corresponding reference trajectories, and 3) an appropriate controller type/form and its parameters. The aforementioned tasks lead to the formulation of a multistate control optimization problem, which is tackled in this paper. This paper enhances the accuracy of the existing MMC state-space model by including the following: 1) a piecewise affine insulated-gate bipolar transistor/diode model and 2) the impact of dead time. This paper also proposes a simulation-based gradient-descent optimization algorithm (cosimulation) to optimize the controller gains, given the controller type/form and the reference state trajectory. The theoretical proofs of the gradients required in the optimization algorithm are also provided. The aforementioned optimization algorithm is applied to a variable-frequency MMC-based drive system. Experimental results on a scaled-down prototype and simulation results on a real-world application are provided to validate the accuracy of the proposed MMC model and to show the effectiveness of the proposed optimization algorithm.

A Computationally Efficient Simulation-Based Optimization Algorithm for Large-Scale Urban Transportation Problems

This paper proposes a computationally efficient simulation-based optimization (SO) algorithm suitable to address large-scale generally constrained urban transportation problems. The algorithm is based on a novel metamodel formulation. We embed the metamodel within a derivative-free trust region algorithm and evaluate the performance of this SO approach considering tight computational budgets. We address a network-wide traffic signal control problem using a calibrated microscopic simulation model of evening peak period traffic of the full city of Lausanne, Switzerland, which consists of more than 600 links and 200 intersections. We control 99 signal phases of 17 intersections distributed throughout the entire network. This SO problem is a high-dimensional nonlinear constrained problem. It is considered large-scale and complex in the fields of derivative-free optimization, traffic signal optimization, and simulation-based optimization. We compare the performance of the proposed metamodel method to that of a traditional metamodel method and that of a widely used commercial signal control software. The proposed method systematically and efficiently identifies signal plans with improved average city-wide travel times.

Energy-Efficient Urban Traffic Management: A Microscopic Simulation-Based Approach

Microscopic urban traffic simulators embed the most detailed traveler behavior and network supply models. These simulators represent individual vehicles and can therefore account for vehicle-specific technologies. They can be coupled with instantaneous fuel consumption models to yield detailed network-wide fuel consumption estimates. Nonetheless, there is currently a lack of computationally efficient optimization techniques that enable the use of these complex integrated models to design sustainable transportation strategies. This paper proposes a methodology that combines a stochastic microscopic traffic simulation model with an instantaneous vehicular fuel consumption model. The combined models are embedded within a simulation-based optimization algorithm and used to address a signal control problem that accounts for both travel times and fuel consumption. The proposed technique couples detailed, stochastic, and computationally inefficient models, yet is an efficient optimization technique. Efficiency is achieved by combining simulated observations with analytical approximations of both travel time and fuel consumption. This methodology is applied to a network in the Swiss city of Lausanne. Within a tight computational budget, the proposed method identifies signal plans with improved travel time and fuel consumption metrics. It outperforms traditional methodologies, which use only simulated information or only analytical information. The case study illustrates the added value of combining simulated and analytical information when performance metrics with high variance, such as fuel consumption, are used. This method enables the use of disaggregate instantaneous vehicle-specific information to inform and improve traffic operations at the network-scale.

Solving Large-scale Urban Transportation Problems by Combining the Use of Multiple Traffic Simulation Models

Transportation agencies often resort to the use of traffic simulation models to evaluate the impacts of changes in network design or network operations. They often have multiple traffic simulation tools that cover the network area where changes are to be made. Nonetheless, these multiple simulators may differ in their modeling assumptions (e.g., macroscopic versus microscopic), in their reliability (e.g., quality of their calibration) as well as in their modeling scale (e.g., city-scale model versus regional-scale model). The choice of which simulation model to rely on, let alone of how to combine their use, is intricate. A larger-scale model may, for instance, capture more accurately the local-global interactions; yet may do so at a greater computational cost. This paper proposes a methodology that enables the simultaneous use of multiple traffic simulation models.We propose a simulation-based optimization algorithm that embeds information from simulation models with different levels of accuracy and with different levels of computational efficiency. The algorithm combines the use of high-accuracy low-efficiency models with low-accuracy high-efficiency models. This combination leads to an algorithm that can identify points with good performance at a reduced computational cost.We evaluate the performance of the algorithm with a traffic signal control problem on a small network. We show that the proposed algorithm identifies signal plans with excellent performance, i.e., with reduced average trip travel times, while doing so with a reduction in the computational cost.