Simulation-based Optimization Approach Research Articles

A Stochastic Optimization Algorithm for Optimizing Flood Risk Management Measures Including Rainfall Uncertainties and Nonphysical Flood Damages

AbstractOne of the basic assumptions usually made by researchers for obtaining optimal designs of flood mitigation measures that take into account the associated uncertainties is consideration of flood damage probability directly correlated with flood event probability. The present work demonstrates that this assumption leads to 42% underestimation of expected damages for the case study of the Kan River basin near the capital city of Iran, Tehran. To eliminate this limitation, in this paper, inherent rainfall uncertainties are included through random sampling techniques and simulation-based optimization approaches. A Monte Carlo simulation method is employed to generate multivariate synthetic rainfalls, which are then imported in a rainfall-runoff model. This model gives the flood hydrographs of subbasins for hydraulic routing in waterways of the watershed by a hydraulic model, considering different flood mitigation measures. These models are then coupled with the NSGA-II optimization algorithm to provide...

Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations

AbstractOptimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP’s practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa ...

Hybrid approach using simulation-based optimisation for job shop scheduling problems

In this paper, we present a hybrid modelling approach and formulation using simulation-based optimisation (SbO) for solving complex problems, viz., job shop scheduling. The classical job shop scheduling problem is NP-Hard. Traditionally, the problem is modelled as a Mixed-Integer Programming (MIP) model and solved using exact algorithms (branch-and-bound, branch-and-cut, etc) or using meta-heuristics (Genetic Algorithm, Particle Swarm Optimisation, etc). In our hybrid SbO approach, we propose a modified formulation of the scheduling problem where the operational aspects of the job shop are captured only in the simulation model. Two new decision variables, controller delays and queue priorities, are introduced. The performances of the MIP-based approach and the proposed hybrid approach are compared through the number of decision variables, run time and the objective values for select deterministic benchmark problem instances. The results clearly indicate that the hybrid approach outperforms the traditional MIP for all large-scale problems, resulting in solutions closer to optimum in a much lesser computational time. Interestingly, it is also observed that the introduction of an ‘error’ term in the objective of the deterministic problem improves performance. Finally, the performance of the proposed SbO approach is analysed for stochastic job shops.

Simulation-based optimization for a capacitated multi-echelon production-inventory system

One of the most important aspects affecting the performance of a supply chain is the management of inventories. Managing inventory in complex supply chains is typically difficult, and may have a significant impact on the customer service level and system-wide costs. The main challenge of inventory management is that almost every inventory problem involves multiple and conflicting objectives that need to be optimized simultaneously. In this paper, we present an efficient way using simulation-based optimization approach to determine the optimal inventory control parameters of a multi-echelon production-inventory system under a stochastic environment. The Pareto dominance concept is implemented to find a set of near optimal solutions for determining the best trade-off between objectives. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm is used to determine the appropriate inventory control parameters to minimize the total inventory cost and maximize the service level. To evaluate the control parameters generated by the MOPSO, an object-oriented framework for developing the simulation model is presented. Finally, we provide a real-world case study of a major food product supply chain to demonstrate the use of proposed approach and enable decision making at inventory management. The proposed algorithm is compared with existing multi-objective genetic algorithm (NSGA-II).

Risk based optimal design of detention dams considering uncertain inflows

This study deals with the optimal design of detention dams under flood discharge uncertainties using a simulation-based optimization approach. An extended methodology is represented by integrating the ant colony optimization (ACO), artificial neural networks, and various risk measures including: expected flood discharge, value at risk, and conditional value at risk (CVaR). For this purpose, first, the neural network is trained by the results of a hydrodynamic model and then it is used to measure different risk indices under flood uncertainties. The proposed approach is then applied to a real case and optimal designs are determined by the search algorithm-i.e. ACO. Different optimal designs are obtained for the storage detention dams when different risk concepts are implemented as the objective function in the system modeling. Particularly, when the expected value measure is combined with the CVaR, the cost of optimal design is nearly two times smaller than those obtained by the formulation with independent objective functions whereas the obtained solution could efficiently minimize both E(Q d ) and CVaR. The optimal solutions have especial capabilities in terms of performance and cost levels and this gives the stakeholders and decision makers to construct a framework to choose the final design when there are different attitudes and interests for flood risk management.

A simulation-based optimization approach to integrated inventory management of a sawlog supply chain with demand uncertainty

This paper develops a simulation-based optimization supply chain model for supplying sawlogs to a sawmill from a forest management unit. The simulation model integrates the two-way flow of information and materials under the stochastic demand of the sawmill production unit. The dynamic optimization model finds the optimum inventory policy (s, S) that minimizes the total inventory cost for the three supply chain agents — sawmill storage, merchandizing yard, and forest management unit. The model is used to analyze a real sawmill case study in northwestern Ontario, Canada. It was found that the merchandizing yard absorbs shocks of uncertain demand from the sawmill production unit and reduces idle time, but it increases the total cost of the supply chain by $11 802 (about 42%). The optimized model predicts that only 3.5 days of inventory is required at the sawmill storage. The simulation-based optimization supplier model will help in decision-making at the tactical and operational level in the forest products industry supply chain through a two-way flow of information and materials.

Optimal Design of Detention Rockfill Dams Using a Simulation-Based Optimization Approach with Mixed Sediment in the Flow

Detention rockfill dams are generally known as economic and efficient structural methods for flood mitigation and watershed management. Despite the existing studies in the context of rockfill dam design, designers still rely on time-consuming and costly experimental tests which can only lead into practical guidelines. Moreover, investigation of various rockfill dam design alternatives in terms of hydraulic performance needs an exhaustive framework to obtain the optimal design values. With respect to these, this research study aims to investigate the optimal dam thickness and mean diameter of coarse aggregates in detention rockfill dams in order to improve the hydraulic characteristics, e.g., reduction in flood peak discharge and increase in flood duration while the construction cost is minimized. Here, a methodology based on data-driven simulation model, multi-objective optimization model, multi-criterion decision making model, and social choice method is suggested to find the optimal design parameters of detention rockfill dam. This methodology adapts data to model and model to optimum design variables. Experimental results are converted to data-driven simulation model based on multilayer perceptron neural network, which can model the relationship between design variables of detention rockfill dam and parameters of flood hydrograph. The simulation model is used in a robust multi-objective optimization algorithm, non-dominated sorting genetic algorithm-ΙΙ (NSGA-ΙΙ), to establish a trade-off between the conflicting objectives. Eventually, PROMETHEE decision making model and Borda count social choice method are used to find the best agreed-upon design optimal point on the trade-off curve. Results indicate that the best non-dominated solution can be considered as 10.01 and 5 cm for the optimum thickness of detention rockfill dam and optimum mean diameter of coarse aggregates in the porous media, respectively. The similar optimum characteristics of detention rockfill dam using PROMETHEE and Borda count social choice method depict the stable performance of the proposed methodology.

Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraint

This paper considers the problem of integrated production, preventive maintenance and quality control for a stochastic production system subject to both reliability and quality deteriorations. A make-to-stock production strategy is used to provide protection to the serviceable stock against uncertainties. The quality control is performed using a single acceptance sampling plan by attributes. The preventive maintenance strategy consists in carrying out an imperfect maintenance as a part of the setup activity at the beginning of each lot production, while a major maintenance (overhaul) is undertaken once the proportion of defectives in a rejected lot reaches or exceeds a given threshold. The main objective of this study is to jointly optimize the production lot size, the inventory threshold, the sampling plan parameters and the overhaul threshold by minimizing the total incurred cost. To meet customer requirements, the optimization problem is subject to a specified constraint on the average outgoing quality limit (AOQL). A stochastic mathematical model is developed and solved using a simulation-based optimization approach. Numerical examples and thorough sensitivity analyses are provided to illustrate the efficiency of the proposed integrated model. Compared with the 100% inspection policy which is widely used in the literature on integrated production, maintenance and quality control, the results obtained show that an economic design of acceptance sampling in such an integrated context can lead to important cost savings of more than 20%.

A multi-aid optimization scheme for large-scale investigation of cost-optimality and energy performance of buildings

According to the European Energy Performance of Buildings Directive (EPBD-2010/31/EU), all EU-Member states are obliged to continuously apply analysis on cost-optimal levels of minimum energy performance requirements towards nearly/net zero energy buildings. To perform such techno-economic analysis, a large number of technical/financial assumptions should be covered and possibly billions of design/operation options should be explored. This is computationally expensive. This study introduces a novel multi-aid optimization scheme (MAOS) for supporting robust cost-optimal decisions on energy-performance levels of buildings. The scheme's feature is reduction of the computational cost by avoiding time-consuming simulations through the use of post-processing and/or simplified models (when possible), while holistic optimization is adopted for considering multivariate interactions between possible design/operation options and financial/technical assumptions. The effectiveness of MAOS is demonstrated by optimizing a single-family house under 108-financial scenarios, where more than 1.610 solutions would be possible. The results show significant (∼95%) time reduction compared with those of the usual simulation-based optimization approach.

Optimising design parameters of continuous mining transport systems using discrete event simulation

The ground articulating pipeline (GAP) system was developed to transport oil sand using a continuous transport system. This paper proposes a simulation-based optimisation approach to improve the efficiency of oil sand continuous transport systems at the operational level. Based on the analysis of shovel capacity, the authors recommend that this GAP transfer system should operate with a 70-ton capacity shovel. They also recommend that a surge hopper should be introduced on the mobile slurry system to maximise productivity of the GAP system. This simulation approach can be applied to crushers and conveyor belt systems.

A simulation-based optimization approach for passenger train timetabling with periodic track maintenance and stops for praying

This paper presents two optimization methods for solving the passenger train timetabling problem to minimize the total delay time in the single track railway networks. The goal of the train timetable problem is to determine departure and arrival times to or from each station in order to prevent collisions between trains and effective utilization of resources. The two proposed methods are based on integration of a simulation and an optimization method to simulate train traffic flow and generate near optimal train timetable under realistic constraints including stops for track maintenance and praying. The first proposed method integrates a cellular automata (CA) simulation model with genetic algorithm optimization method. In the second proposed approach, a CA simulation model combines with dynamically dimensioned search optimization method. The proposed models are applied to hypothetical case study to demonstrate the merit of them. The Islamic Republic of Iran Railways (IRIR) data and regulations have been used to optimize train timetable. The results show the first method is more efficient than the second method to obtain near optimal train timetabling.

A derivative-free approach for a simulation-based optimization problem in healthcare

Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs, developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital’s ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation-based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints. In this work, we develop a simulation-based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete-event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative-free optimization algorithm to modify the given setting. We report results for a real-world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach.

A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

One of the main limitation of the application of evolutionary algorithms (EA) is the tendency to converge prematurely to a local optimum. The EAs suffer with the disadvantage of premature convergence and hence the study on convergence of EAs is always one of the most important research fields. Due to outstanding capability of chaos to avoid being trapped in local optimum, it can be considered as an efficient search tool. Therefore, in current paper, in order to taking properties of chaos, eight chaotic maps are employed within a differential evolution (DE) algorithm for solving a stochastic job scheduling problem. To speedup searching and avoid local optimum traps, the random sequences produced from chaotic maps are utilized instead of random variables in DE. Furthermore, to address the uncertainties arising in scheduling environments, Monte Carlo simulation is used. However, simulation is not an optimization approach. Therefore, we design the simulation-based optimization approach where a simulator is combined with chaotic DE. The simulation experiments are used to evaluate the quality of candidate solutions and the chaotic DE is utilized to find best-compromised solutions and then guide the search direction. The performance of simulation-based chaotic DE algorithm is investigated in a computational study, and the results show the outperformance of suggested method with respect to the traditional methods.

Integrated quality strategy in production and raw material replenishment in a manufacturing-oriented supply chain

This paper deals with the coordination of production, replenishment and inspection decisions for a manufacturing-oriented supply chain with a failure-prone transformation stage, random lead time and imperfect delivered lots. Upon reception of the lot, the manufacturer executes an acceptance sampling plan with a zero non-conforming criterion. If the sample does not contain non-conforming items, the lot is accepted; otherwise, it is rejected. In this work, two strategies regarding the refused sampled lot are studied. The first one involves a return of the lot to the supplier, who commits to improving the quality of the lot, while the second assumes that the manufacturer performs a 100 % inspection and rectification operation. This work presents two main objectives. The first one is to jointly optimize, in a stochastic and dynamic context, the ordering point of raw material, the lot size of raw material, the final product inventory threshold and the severity of the sampling plan using a simulation-based optimization approach. The second one is to determine the best of the two quality control strategies. The in-depth study has shown that no strategy could be preferred in all the cases. For this reasons, we present an easy decision-making tool (indifference curves) to help the manager select the best quality control strategy when considering the entire supply chain.

Urban transportation emissions mitigation: Coupling high-resolution vehicular emissions and traffic models for traffic signal optimization

This paper proposes a methodology that allows high-resolution traffic and emissions models, known as microscopic simulation models, to be efficiently used to address transportation optimization problems that account for complex environmental metrics. The methodology consists of a metamodel simulation-based optimization (SO) approach. The metamodel combines traffic and emissions information from high-resolution microscopic simulators with information from lower-resolution analytical macroscopic models. This paper formulates and uses an analytical and differentiable macroscopic approximation of the non-differentiable simulation-based microscopic emissions model. A differentiable macroscopic traffic model is also used.This paper shows that the analytical structural information provided by macroscopic analytical emissions models can contribute, despite their lower-resolution, to enhance the computational efficiency of algorithms that embed higher-resolution inefficient emissions models. The proposed algorithm is computationally efficient, i.e., it can identify points with improved performance within few simulation runs. More generally, the results of this paper highlight the added value of embedding analytical structural information within SO algorithms to address complex SO problems.A traffic signal control case study is carried out. The proposed metamodel identifies signal plans that improve travel time and various emissions metrics. We present the corresponding monetary savings that can be achieved.This optimization framework enables practitioners to use high-resolution microscopic traffic and emissions models to systematically design transportation schemes that account directly, in the design process, for environmental metrics. Hence, the use of such high-resolution models is no longer limited to the environmental evaluation of a small set of predetermined schemes. The tight computational budgets used in this paper show that such complex problems can be addressed in a computationally efficient manner.

Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule

Abstract Energy efficient operation of microgrids, a localized grouping of controllable loads with distributed energy resources like solar photovoltaic panels, requires the development of energy management systems (EMSs) with the capability of controlling the loads so as to optimize the aggregate performance of the microgrid. In microgrids comprising of buildings of different nature (residential, commercial, industrial, etc.), where the occupants exhibit heterogeneous occupancy schedules, the objective of an effective management strategy is to optimize the aggregate performance by intelligently exploiting the occupancy schedules and the intermittent production of solar energy. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous occupancy schedule. The microgrid exchanges energy, buying or selling it, with the main grid and the EMS optimizes an aggregate multi-objective criterion that takes into account both the energy cost and the thermal comfort of the occupants of the microgrid. Simulative results obtained using a microgrid test case developed in EnergyPlus demonstrate the effectiveness of the proposed approach: the proposed EMS strategy is shown to take advantage of the occupancy information, intelligently and automatically changing the energy demand of each building according to the occupants’ behavior, and achieving relevant improvements with respect to alternative EMS strategies.

Application of ant colony algorithm in the simulation-based approach to improve airport surface operations

In this paper, we apply the ant colony algorithm in the optimiser for the simulation-based optimisation approach to improve airport surface operations. The major components of the system architecture and detailed descriptions of the implementations of the ant colony algorithm are introduced. The proposed ant colony algorithm and the simulation-based optimisation approach are applied to the case studies of the east side of the Dallas-Fort Worth airport (DFW). The initial results have demonstrated the success and benefit of applying the ant colony algorithm in the simulation-based approach for improving airport surface operations. The results have also implied that applying the combined genetic algorithm and ant colony algorithm in the optimiser can derive better results than using any one of the two algorithms solely.

A Metamodel Simulation-based Optimization Approach for the Efficient Calibration of Stochastic Traffic Simulators

This work considers the calibration of stochastic microscopic traffic simulators. It formulates the calibration problem as a simulation-based optimization (SO) problem, and uses metamodel SO ideas. The main idea is to embed within the calibration algorithm an analytical problem-specific description of how the calibration parameters are related to the simulation-based objective function. Preliminary results on a toy network are presented. Ongoing work applies these ideas to the calibration of a Berlin metropolitan area network.

Simulation‐based optimisation of wastewater flow and composition for on‐site treatment in the food and beverage industry utilising reference nets in combination with genetic algorithms

AbstractThis study introduces the concept of computer modelling and simulation of complex bioprocesses and systems using an approach that combines the reference net formalism with machine learning and optimisation techniques. Reference nets are an extension of high level Petri Nets, which can be used as a central visualisation and modelling tool. The net‐in‐net paradigm used by reference nets makes it possible to model complex processes, such as those found in the food and beverage industry. A plugin/interface system based on the java programming language allows implementation of advanced mathematical modelling techniques at specific points in entire system simulations. Separate optimisation tools can also run and modify existing reference net models for fast solutions to efficiency problems. We present an example system that simulates a specific section of a beer brewery using the reference net formalism, which is optimised using a genetic algorithm. We show in detail how the different software packages can be combined for a simulation based optimisation approach. The optimisation technique specifically addresses the wastewater pollution load in regard to its chemical oxygen demand. A beer brewery was chosen as an example for this study due to the constantly increasing requirements to lower energy and water consumption in this industry. One possibility to lower the energy and water demands is to effectively treat wastewater produced by the brewery, which can introduce cost savings by providing recycled water and biogas. Most approaches to wastewater treatment are end‐of‐pipe solutions that do not consider the brewery as a whole. A brewery contains many processes that can be running concurrently and interacting with one another (e.g. brewing, clean‐in‐place and bottling) with each process producing varying amounts of wastewater with different pollution loads. Optimisation of the scheduling of the different processes with respect to the wastewater production will allow for more effective wastewater treatment, and therefore cost and energy savings. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

A Metamodel Simulation Based Optimisation Approach for the Tidal Turbine Location Problem

A metamodel simulation based optimisation approach for the tidal turbine location problem is introduced. The method comprises design of experiments, computational simulations, metamodel construction and formulation of a mathematical optimisation model. Sample plans with different number of data points are used to fit 2nd and 3rd order polynomial as a function of two design parameters: longitudinal and lateral spacing, with a view to approximating the power output of tidal turbine farms with inline and staggered layouts, each of them with a fixed number of turbines. The major advantage this method has, in comparison to those reported in the literature, is the capability to analyse different design parameter combinations that satisfy optimality criteria in reasonable computational time, while taking into account complex flow-turbine interactions.