Robust Simulation Optimization Research Articles

Robust simulation-optimization framework for synthesis and design of natural gas downstream Incorporating renewable hydrogen network under uncertainty

Many intrinsic uncertainties are part of natural gas downstream synthesis, design, and operation. This work extends the earlier developed deterministic model to account for new decision variables, unit operations, and uncertainty in the network. The uncertainty includes the feedstock composition and flowrate, utility requirements, by-products, and final product market price. The proposed two-stage stochastic model includes new major processing units (helium recovery units), hydrogen production via renewable technologies (biomass gasification, water electrolysis), and operating modes. The stochastic formulation simultaneously considers uncertainty in 12 different model parameters. The model's first stage aims to find the optimal network's design variables, such as processing units, technologies, and operating modes common to all scenarios. The second stage is concerned with deriving operational variables, for example, the network's overall inlet feedstock flowrates, individual units' inlet flowrates, and units' capacity. Outcomes obtained for a range of uncertain material/energy feedstock and products/by-products market prices result in higher expected operating profits than the equivalent deterministic model. The analysis's key takeaways show that the most prevailing network consists of two syngas technologies: (1) auto-thermal reforming to feed a low-temperature Fischer Tropsch synthesis unit and (2) steam methane reforming for hydrogen production. Additionally, renewable hydrogen is produced via electrolysis and biomass gasification. There is an improvement of 2.22% in the objective function value using the stochastic approach instead of the deterministic.

A Non-Convex Robust Simulation Optimization Model for Inventory Management Problem by System Dynamics

A Non-Convex Robust Simulation Optimization Model for Inventory Management Problem by System Dynamics

Robust simulation optimization for supply chain problem under uncertainty via neural network metamodeling

Real-world supply chain management problems are highly complicated such that their optimization procedure is computationally expensive due to the extensive dimensions and uncertainty of their critical variables. Simulation optimization is a commonly applied technique to determine the optimal variables since the problem is too complex. Due to the uncertain nature of the real-world systems, it is also worthy to consider the robustness of the optimal solutions. To address this issue, this study investigates the problem of determining near-optimal safety stock levels in a multi-product supply chain with regard to deviations of its overall cost. A new framework is proposed to define the decision as well as environmental variables. This novel framework results in a significant reduction of the solution space while maintains the essential supply chain control parameters. The prediction performance of artificial neural networks (ANNs) with different structural settings are compared, and the best-fitted ANNs are selected to obtain the robust solutions. Consequently, we employ a robust metamodel-based simulation optimization approach based on Taguchi’s view and optimize the multi-objective supply chain problem with respect to supply chain operational costs and customer satisfaction criteria.

A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning

Natural disasters such as earthquakes always threaten human societies. Therefore, preparedness planning seems to be necessary to reduce casualties and accelerate relief efforts. Decisions in the preparedness phase include determining the optimal location for distribution centers and suppliers, allocating them to affected areas, and determining the amount of inventory of distribution centers. In this research, a robust simulation-optimization approach is presented for the planning in the preparedness phase. Although the demand for relief commodities has been considered as a given parameters. Due to our best knowledge there is no research in which the demand for relief commodities including drinking water, food, blood, medicine, tents and non-drinking water, during the disaster is related to the interactive relations of critical infrastructures of the city affected by the earthquake, and the power of the earthquake. Most of the unplanned occasions are occurred when co-incidences of probabilities are happen together. When a critical urban infrastructure fails due to earthquake it may cause some difficulties in the other infrastructures. Co-incidences may cause some horrible tragedy. First, the critical infrastructures of the city affected by the earthquake are identified and the interactions of these infrastructures are simulated. Considering the interactions of critical infrastructures in a city, various scenarios for earthquake are designed and tested through simulation approach in order to estimate the mean and variance of demand for relief commodities. The stochastic demand behavior which is the output of the simulation approach is assumed as a stochastic parameter of a mathematical model for multi-period location–allocation–Inventory problem. Robust optimization approach is used to deal with uncertainty. The mathematical model determines the location of distribution centers and suppliers, and how they are allocated to the affected areas. The proposed mathematical model is solved using a customized genetic algorithm for a case study in Tehran.

Robust simulation-optimization of dynamic-stochastic production/inventory control system under uncertainty using computational intelligence

Robust simulation-optimization of dynamic-stochastic production/inventory control system under uncertainty using computational intelligence

A worst‐case formulation for constrained ranking and selection with input uncertainty

AbstractIn this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.

A fast and robust simulation-optimization methodology for stormwater quality management

The simulation and optimization of low impact development (LID) practices has been a key research topic in stormwater management. In this study, a fast and robust framework was proposed for providing the optimal design of LID practices by coupling a physically based model, the Markov chain, with the multi-objective shuffled frog leaping algorithm (MOSFLA). The proposed framework was then tested for chemical oxygen demand (COD) reduction in a typical urban catchment in China. The storm water management model (SWMM) was used to provide the flow/COD data during the baseline scenario, and the Markov chain method was then incorporated as a subset of the physically based model. Based on the results, the computational efficiency was improved by 500 times when the new framework was used, and the robustness of the optimal results increased over 50% compared to commonly used algorithms. The relative error between the SWMM and the Markov chain method was less than 5%, indicating that a satisfactory performance could be obtained using the proposed framework. This new method provides a useful tool for optimizing LID practices and green infrastructure, especially for complex urban catchments.

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.

Crossing weighted uncertainty scenarios assisted distribution-free metamodel-based robust simulation optimization

In practice, computer simulations cannot be perfectly controlled because of the inherent uncertainty caused by variability in the environment (e.g., demand rate in the inventory management). Ignoring this source of variability may result in sub-optimality or infeasibility of optimal solutions. This paper aims at proposing a new method for simulation–optimization when limited knowledge on the probability distribution of uncertain variables is available and also limited budget for computation is allowed. The proposed method uses the Taguchi robust terminology and the crossed array design when its statistical techniques are replaced by design and analysis of computer experiments and Kriging. This method offers a new approach for weighting uncertainty scenarios for such a case when probability distributions of uncertain variables are unknown without available historical data. We apply a particular bootstrapping technique when the number of simulation runs is much less compared to the common bootstrapping techniques. In this case, bootstrapping is undertaken by employing original (i.e., non-bootstrapped) data, and thus, it does not result in a computationally expensive task. The applicability of the proposed method is illustrated through the Economic Order Quantity (EOQ) inventory problem, according to uncertainty in the demand rate and holding cost.

Recent developments in metamodel based robust black-box simulation optimization: An overview

In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model’s output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state-of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.

Response surface approach to robust design of assembly cells through simulation

PurposeThis paper aims to present a multi-response robust design (RD) optimization approach for U-shaped assembly cells (ACs) with multi-functional walking-workers by using operational design (OD) factors in a simulation setting. The proposed methodology incorporated the design factors related to the operation of ACs into an RD framework. Utilization of OD factors provided a practical design approach for ACs addressing system robustness without modifying the cell structure.Design/methodology/approachTaguchi’s design philosophy and response surface meta-models have been combined for robust simulation optimization (SO). Multiple performance measures have been considered for the study and concurrently optimized by using a multi-response optimization (MRO) approach. Simulation setting provided flexibility in experimental design selection and facilitated experiments by avoiding cost and time constraints in real-world experiments.FindingsThe present approach is illustrated through RD of an AC for performance measures: average throughput time, average WIP inventory and cycle time. Findings are in line with expectations that a significant reduction in performance variability is attainable by trading-off optimality for robustness. Reductions in expected performance (optimality) values are negligible in comparison to reductions in performance variability (robustness).Practical implicationsACs designed for robustness are more likely to meet design objectives once they are implemented, preventing changes or roll-backs. Successful implementations serve as examples to shop-floor personnel alleviating issues such as operator/supervisor resistance and scepticism, encouraging participation and facilitating teamwork.Originality/valueACs include many activities related to cell operation which can be used for performance optimization. The proposed framework is a realistic design approach using OD factors and considering system stochasticity in terms of noise factors for RD optimization through simulation. To the best of the authors’ knowledge, it is the first time a multi-response RD optimization approach for U-shaped manual ACs with multi-functional walking-workers using factors related to AC operation is proposed.

Robust simulation optimization using φ-divergence

Article history: Received March 4 2016 Received in Revised Format April 27 2016 Accepted May 14 2016 Available online May 16 2016 We introduce a new robust simulation optimization method in which the probability of occurrence of uncertain parameters is considered. It is assumed that the probability distributions are unknown but historical data are on hand and using φ-divergence functionality the uncertainty region for the uncertain probability vector is defined. We propose two approaches to formulate the robust counterpart problem for the objective function estimated by Kriging. The first method is a minimax problem and the second method is based on the chance constraint definition. To illustrate the methods and assess their performance, numerical experiments are conducted. Results show that the second method obtains better robust solutions with less simulation runs. © 2016 Growing Science Ltd. All rights reserved

Simulation optimization in inventory replenishment: a classification

Simulation optimization is increasingly popular for solving complicated and mathematically intractable business problems. Focusing on academic articles published between 1998 and 2013, the present survey aims to unveil the extent to which simulation optimization has been used to solve practical inventory problems (as opposed to small, theoretical “toy problem”), and to detect any trends that might have arisen (e.g., popular topics, effective simulation optimization methods, frequently studied inventory system structures). We find that metaheuristics (especially genetic algorithms) and methods that combine several simulation optimization techniques are the most popular. The resulting categorizations provide a useful overview for researchers studying complex inventory management problems, by providing detailed information on the inventory system characteristics and the employed simulation optimization techniques, highlighting articles that involve stochastic constraints (e.g., expected fill rate constraints) or that employ a robust simulation optimization approach. Finally, in highlighting both trends and gaps in the research field, this review suggests avenues for further research.

An exploratory approach for adaptive policymaking by using multi-objective robust optimization

Developing robust policies for complex systems is a profound challenge because of their nonlinear and unpredictable nature. Dealing with these characteristics requires innovative approaches. A possible approach is to design policies that can be adapted over time in response to how the future unfolds. An essential part of adaptive policymaking is specifying under what conditions, and in which way, to adapt the policy. The performance of an adaptive policy is critically dependent on this: if the policy is adapted too late or too early, significant deterioration in policy performance can be incurred. An additional complicating factor is that in almost any policy problem, a multiplicity of divergent and potentially conflicting objectives has to be considered. In this paper we tackle both problems simultaneously through the use of multi-objective robust simulation optimization. Robust optimization helps in specifying appropriate conditions for adapting a policy, by identifying conditions that produce satisfactory results across a large ensemble of scenarios. Multi-objective optimization helps in identifying such conditions for a set of criteria, and providing insights into the tradeoffs between these criteria. Simulation is used for evaluating policy performance. This approach results in the identification of multiple alternative conditions under which to adapt a policy, rather than a single set of conditions. This creates the possibility of an informed policy debate on trade-offs. The approach is illustrated through a case study on designing a robust policy for supporting the transition toward renewable energy systems in the European Union. The results indicate that the proposed approach can be efficiently used for developing policy suggestions and for improving decision support for policymakers. By extension, it is possible to apply this methodology in dynamically complex and deeply uncertain systems such as public health, financial systems, transportation, and housing.

A robust simulation–optimization modeling system for effluent trading—a case study of nonpoint source pollution control

In this study, a robust simulation-optimization modeling system (RSOMS) is developed for supporting agricultural nonpoint source (NPS) effluent trading planning. The RSOMS can enhance effluent trading through incorporation of a distributed simulation model and an optimization model within its framework. The modeling system not only can handle uncertainties expressed as probability density functions and interval values but also deal with the variability of the second-stage costs that are above the expected level as well as capture the notion of risk under high-variability situations. A case study is conducted for mitigating agricultural NPS pollution with an effluent trading program in Xiangxi watershed. Compared with non-trading policy, trading scheme can successfully mitigate agricultural NPS pollution with an increased system benefit. Through trading scheme, [213.7, 288.8] × 10(3) kg of TN and [11.8, 30.2] × 10(3) kg of TP emissions from cropped area can be cut down during the planning horizon. The results can help identify desired effluent trading schemes for water quality management with the tradeoff between the system benefit and reliability being balanced and risk aversion being considered.