Simulated Optimization Technique Research Articles

Multi-objective simulation–optimization for water resources management and uses in multi-dam systems in low-water regions

One of the challenging issues in surface water resources management is the optimal operation of multi-dam systems. In addition to the applicability in determining the optimal pattern of operation of existing dams, this approach can be implemented in specifying the optimal capacity of under design dams. In this research, the simulation–optimization technique is used along with the reservoir zoning method in order to specify the optimal role curve of each of the dams in a multi-dam system (i.e., the Marun and Jarreh dams) in monthly intervals. The objective of this study is to develop a multi-objective algorithm on the basis of the zoning of reservoirs for the optimal operation of multi-dam systems. In this technique, instead of increasing the reliability of supplying demands in the whole period regardless the dry months, some of the inflow of rainy periods is stored in dam reservoirs to be consumed in dry months in order to mitigate the severity of deficiency. To examine the efficiency of the proposed model, the results obtained from the system operation in the current conditions are compared under two scenarios including optimal and reference. In the reference scenario, during 30-years, in some dry and low-water years, especially in the last six years of planning, the percentage of the demand supply is zero or less than five percent in several consecutive months. After optimizing the system, the minimum supply percentage in critical and dry months reaches 30 to 60 percent. Also, in the optimization scenario, the percentage of downstream ecological demands is improved. This research indicates that using the solution of this research leads to the better management of reservoirs in multi-dam systems and reduces the severity of deficiency in supplying various uses in low-water months.

Systematic Optimization of Electromagnet Hardware for Electromagnetic Suspension: A Fusion of Simulation and Multi-objective Optimization Techniques

Systematic Optimization of Electromagnet Hardware for Electromagnetic Suspension: A Fusion of Simulation and Multi-objective Optimization Techniques

Multi-parameter similarity model-based lightweight design of battery electric vehicle body-in-white

The bending stiffness, torsional stiffness, and low-order modal frequencies of the body-in-white (BIW) are crucial factors in structural lightweight design. This paper introduces a BIW optimization approach for electric vehicles (BEVs) that integrates comprehensive sensitivity analysis and multi-parameter similarity models. The aim is to efficiently balance the BIW’s stiffness performance with minimal weight, while also managing potential manufacturing cost increases from design modifications. Firstly, the validity of the computational model is demonstrated by the BIW bending and torsion stiffness test and modal test. On this basis, the integrated sensitivity evaluation indexes of the structural member thickness parameters to the stiffness, weight and low-order modal frequency response of the BIW were calculated. The sheet metal thickness parameters of 17 structural members were preferred as design variables, and a multi-parameter similarity prediction model for BIW weight reduction design was constructed. Minimum weight, maximum stiffness and higher modal frequency of the BIW are defined as the optimization objectives. The optimal solution was finally solved using a multi-objective optimization algorithm. The results indicate a 1.582% reduction in weight of the BIW compared to the pre-optimization BIW. This paper delves into the intricate interplay between the parameters of the BIW’s structural components using simulation optimization techniques alongside experimental validation. This study not only proposes a new idea for the problem of how to efficiently and accurately determine the best optimization object from a large number of structural components in the development of vehicle structural safety, but also provides a research basis for automobile manufacturers to implement further weight reduction and optimization work on the BIW at the later stage of BEV development.

Analyzing the impact of opportunistic maintenance optimization on manufacturing industries in Bangladesh: An empirical study

The study investigates the impact of opportunistic maintenance (OM) optimization on manufacturing industries, especially in Bangladesh, to reduce maintenance costs. To that end, OM strategies have been proposed and optimized for multi-unit manufacturing systems, whereas most of the existing research is for single- or two-unit systems. OM strategies in this research cover one of the three policies: preventive replacement, preventive repair, and a two-level maintenance approach. The proposed two-level maintenance approach is a combination of lower-level maintenance, known as preventive repair, and higher-level maintenance, known as preventive replacement. Simulation optimization (SO) techniques using Python were utilized to evaluate the strategies. Historical data from two of Bangladesh's most promising and significant sectors, the footwear and railway industries, was used as the case study. Compared to the currently utilized corrective maintenance approach, the two-level maintenance approach is the most effective for both case studies, demonstrating cost savings of 16.9 % and 22.4 % for the footwear and railway industries, respectively. This study reveals that manufacturing industries can achieve significant cost savings by implementing the proposed OM strategies, a concept that has yet to be explored in developing countries like Bangladesh. However, the study considered the proposed approaches for major components of the system, and more significant benefits can be achieved if it is possible to apply them to all critical components of the system.

Simulation Learning and Optimization: Methodology and Applications

Due to the rapid advancement of computational power and the Internet of Things (IoT) together with recent developments in digital-twin and cyber-physical systems, the integration of big data analytics techniques (BDAT) (e.g., data mining, machine learning (ML), deep learning, data farming, etc.) into traditional simulation methodologies, along with the enhancement of past simulation optimization techniques, has given rise to an emerging field known as simulation learning and optimization (SLO). Unlike simulation optimization, SLO is a data-driven fusion approach that integrates conventional simulation modeling methodologies, simulation optimization techniques, and learning-based big data analytics (BDA) to enhance the ability to address stochastic and dynamic decision-making problems in the real-world complex system and quickly find the best outcomes for the decision support in both real-time and nonreal-time analyses to achieve the data-driven digital twin capability. Although some literature in the past has mentioned similar applications and concepts, no paper provides a structural explanation and comprehensive review of relevant literature on SLO from both methodological and applied perspectives. Consequently, in the first part of this paper, we conduct a literature review on a novel SLO methodology emerging from the fusion of traditional simulation methodology, simulation optimization algorithms, and BDAT. In the second part of this paper, we review the applications of SLO in various contexts, with detailed discussions on manufacturing, maintenance, redundancy allocation, hybrid energy systems, humanitarian logistics, and healthcare systems. Lastly, we investigate potential research directions for SLO in this new era of big data and artificial intelligence (AI).

Estimation of virus transport parameters in both unsaturated-saturated zone using numerical simulation and flower pollination algorithm

To develop a virus transport model considering both the unsaturated and saturated zone, it is essential to consider separate parameters for both the zone. This is because the virus fate and transport process depends on the degree of saturation. As such, this paper introduces a parameter estimation model that simultaneously identifies virus transport parameters in an unconfined aquifer for both the unsaturated and saturated zones. The proposed model utilizes a linked simulation-optimization technique to minimize errors between observed and simulated virus concentrations. The simulation model used in this study is termed as “variable detachment model,” this is because the simulation component of this approach incorporates varying adsorption rates, which are influenced by the degree of saturation. This study also highlights the complexities involved in parameter estimation for virus transport models, such as non-uniqueness, instability, and non-identifiability. These challenges arise when multiple parameter combinations can produce similar model predictions, leading to difficulty in determining the actual parameter values. To address these challenges, a meta-heuristic optimization algorithm, Flower Pollination Algorithm (FPA), is used, effectively solving such non-convex problems. The model could estimate all the parameters accurately except the inactivation rates of viruses. Later when the global sensitivity analysis was carried out to understand the relative importance of each parameter, inactivation rates were found to be less influential to the model output. Thus, FPA based parameter estimation model can be used as an alternative for estimating the virus transport parameters in groundwater aquifers.

Virtual Material Quality Investigation System

It is often difficult to inspect the material quality in the procurement process. In many cases, the true quality may only be revealed after several manufacturing processes or at the final test of the finished goods. This study employs a hybrid strategy consisting of predictive analytics and prescriptive analytics. The former considers the production characteristics and estimates the probability distribution of the material quality using a simulation optimization technique known as optimal computing budget allocation. The latter optimizes the feeding portfolio of the material allocation into the production line according to the customer's order specification. This study develops a virtual material quality investigation (VMQI) system considering the hybrid strategy and including four modules. An empirical study of a solar cell manufacturer in Taiwan validates the proposed VMQI system. The results show that the VMQI system improves the solution quality by at least 30% and potentially reduces the production cost by 19.8%.

A simulation study of single-vendor, single and multiple-manufacturers supply chain system, with stochastic demand and two distribution policies

This paper addresses the long-standing stochastic single-vendor, multi-manufacturer inventory control problem, using simulation optimization. It is assumed that Manufacturers producing similar goods experience very high random demand; hence, safety stock of raw materials is held in reserve in their warehouses. The vendor supplying this raw material (principal ingredients) as a policy restricts shipments to multiples of full truck load. Thus, it is necessary to take replenishment decision and coordinate delivery among these manufacturers. To solve this problem, we modeled the single vendor, single manufacturer version of the problem (AlDurgam et al., 2017) using simulation optimization techniques, which was validated numerically using parameters and results from AlDurgam et al. (2017). The simulation model was modified systematically to relax the single manufacturer assumption under two distribution policies namely, joint reorder point and vendor managed inventory. These policies were evidently modeled with stringent conditions in literature. A numerical example was provided to compare the performances of the two proposed policies, and the VMI policy was found to performed better in terms of financial savings. Lastly, we investigate the robustness of the famous continuous review (Q, R) inventory policy which is widely used in the mathematical modeling of this problem, against the common cycle assumption. The coefficient of variation is thus suggested as a judgment criterion of when to embrace simulation modeling ahead of other modeling techniques.

A Simulation Optimization Method for Coordination of Production, Transportation and Sales

This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, which are affected by a marketing strategy. Customer demands need to be satisfied by the supply chain through production, transportation and distribution. The optimization problem for coordination of production, transportation and distribution is first formulated as a linear programming with demands as input parameters in the constraint. Our objective is to maximize the expectation of the optimal profit of the supply chain given random demands by selecting an optimal marketing strategy. A simulation optimization technique is proposed to control the generation of random demands and solve the linear programming for efficiently learning the optimal marketing strategy. Numerical results show that our method can significantly improve the expected profit of the supply chain and reduce the computational burden of solving linear programming for achieving a given level of probability of correct selection of the optimal marketing strategy. Furthermore, we extend the optimization problem to a mixed integer programming and also demonstrate the computational efficiency of our proposed method.

Cost optimization of pavement thickness design using intelligent search versus linear programming algorithms

Road pavement costs expend a significant share of financial resources in road construction, and finding the optimal pavement thickness design with minimal cost remains a concern that can be determined using intelligent search algorithms. This study aims to evaluate the performance of the particle swarm optimization (PSO) algorithm based on Iranian Highway Asphalt Paving (IHAP) Code 234. Thus, using the PSO algorithm, the problem of pavement thickness design was solved numerically, and a simulation–optimization technique was devised. Comparing PSO and the linear algorithms (LP) indicates that PSO is accounted as an optimal pavement thickness design regardless of different ranges of the equivalent single-axle loads (ESALs) and resilient moduli (Mr). Considering the four-layer and three-layer pavement designs revealed that the different bitumen-stabilized bases in the four-layer design are not cost-effective. Moreover, the PSO design resulted in 22–29 percent cost savings at various ESALs and Mr; however, the three-layer design was 23–31 percent less expensive.

Estimation of contamination release histories using meshless radial point collocation method and multiverse optimization

Abstract Contaminants in groundwater may enter through various sources which are required to be identified for informed decision-making regarding remediation. In early stages of contamination of an aquifer, the sources of contamination are generally unknown. To estimate the unknown release histories at potential sources, the governing equations of contaminant transport are solved backwards in time which renders the inverse problem as ill-posed. Furthermore, due to measurement or operational errors, observed breakthrough curves can be noisy which can induce uncertainty. Simulation-optimization (SO) techniques are generally used for solving the inverse model. Nevertheless, swarm population-based optimization algorithms may get trapped in local optima in the nonlinear search space related to the problem. In this study, SO model based on meshless radial point collocation method (RPCM) and multiverse optimizer (MVO) is proposed. MVO implements gradual transformation from global exploration to local exploitation phase which helps in better estimation of unknowns. The performance of RPCM-MVO-SO model is compared with two other SO models developed by combining RPCM with grey wolf optimizer and particle swarm optimization in two case studies. The proposed model performs better compared to the other two SO models considered which suggests the efficacy of the model for release history estimation in groundwater.

Identification of Clandestine Groundwater Pollution Sources in Aquifer Polluted with BTEX

One of the biggest challenges in characterizing clandestine groundwater pollution sources is correctly estimating the number of such sources and their physical locations. Indiscreet underground disposal of toxic waste leading to groundwater pollution is widespread, and it is extremely difficult to detect the number of such sources and their locations. In this study, a real-life scenario of groundwater pollution in suburban New South Wales, Australia, is solved using a simulated annealing (SA)–based linked simulation optimization (LSO) technique with Benzene, Toluene, Ethylbenzene and Xylene (BTEX). The developed LSO considers the number of pollutant sources and their locations as explicit unknowns. This overcomes a crucial limitation in the earlier methods that limited the search to a few known potential source locations. The results show the applicability of the developed methodology to real-life groundwater pollution problems without making limiting assumptions about the number of sources or potential source locations being known, which has not been addressed to date.

Optimal siting of rainwater harvesting systems for reducing combined sewer overflows at city scale

The installation of green infrastructure (GI) is an effective approach to manage urban stormwater and combined sewer overflow (CSO) by restoring pre-development conditions in urban areas. Research on simulation-optimization techniques to aid with GI planning decision-making is expanding. However, due to high computational expense, the simulation-optimization methods are often based on design storm events, and it is unclear how much different rainfall scenarios (i.e., design storm events vs. long-term historical rainfall data) impact the optimal siting of GI. The Parallel Pareto Archived Dynamically Dimensioned Search (ParaPADDS) algorithm in a novel simulation-optimization tool OSTRICH-SWMM was used to leverage distributed computing resources. A case study was conducted to optimally site rainwater harvesting cisterns within 897 potential subcatchments throughout the City of Buffalo, New York. Seven design storm events with different return periods and rainfall durations and a one-month historical rainfall time series were considered. The results showed that the optimal solutions of siting cisterns using event-based scenarios, though less computationally expensive, may not perform well under continuous rainfall scenarios, suggesting design rainfall scenarios should be carefully considered for optimizing GI planning. The impact of rainfall scenarios was particularly significant in the middle region of the Pareto front of multi-objective optimization. Utilizing high-performance parallel computing, OSTRICH-SWMM is a promising tool to optimize GI at large spatial and temporal scales.

Digital supply network design: a Circular Economy 4.0 decision-making system for real-world challenges

This research introduces the idea of ‘Circular Economy 4.0’ to reflect the emergence of ‘digitalised’ sustainable supply networks. While often characterised by enhanced productivity and resource/energy efficiency, current perspectives are largely descriptive with limited practical relevance. A hierarchical decision-making framework and a multi-level simulation modelling and optimisation technique are constructed to explore the interplay between Circular Supply Chains and Industry 4.0. The real-world case of blue-green algae as renewable feedstock – to derive value-added omega-3 oils and biofertilisers – is investigated to develop ‘Circular Economy 4.0’ perspectives. The emerging circular supply network utilises micro-factories (i.e., photobioreactors), continuous manufacturing technologies (i.e., piezoelectric transducers), and drone operations for feedstock availability monitoring. This study contributes to theory and practice by building on the limited empirical research exploring determinants of successful transitions in Circular Economy-Industry 4.0 network contexts. Four design principles are proposed that capture the interplay between digital technologies and network design configurations, e.g., centralised – semi-centralised – decentralised. Modelling is developed across macro-, meso-, and micro-levels of analysis. Results demonstrate significant gains in terms of resources utilisation and market dynamics, enabled by the adoption of digital operations in a circular economy context, with initial insights on the evolution of such networks.

Simulation optimization in healthcare resource planning: A literature review

In healthcare, the planning and the management of resources are challenging as there are always many complex and stochastic factors in both demand and supply. Simulation Optimization (SO) that combines simulation analysis and optimization techniques is well suited for solving complicated, stochastic, and mathematically intractable decision problems. In order to comprehensively unveil the degree to which SO has been used to solve healthcare resource planning problems, this article reviews the academic articles published until 2021 and categorizes them into multiple classification fields that are related to either problem perspectives (i.e., healthcare services, planning decisions, and objectives) or methodology perspectives (i.e., SO approaches and applications). We also examine the relations between the individual fields. We find that emergency care services are the most applied domain of SO, and that discrete-event simulation and random search methods (especially genetic algorithms) are the most frequently used methods. The literature classification can help researchers quickly learn this research area and identify the publications of interest. Finally, we identify major trends, insights and conclusions that deserve special attention when studying this area. We suggest many avenues for further research that provide opportunities for expanding existing methodologies and for narrowing the gap between theory and practice.

Care process optimization in a cardiovascular hospital: an integration of simulation–optimization and data mining

To provide health services, hospitals consume electrical power and contribute to the CO2 emission. This paper aims to develop a modelling approach to optimize hospital services while reducing CO2 emissions. To capture treatment processes and the production of carbon dioxide, a hybrid method of data mining and simulation–optimization techniques is proposed. Different clustering algorithms are used to categorize patients. Using quality indicators, clustering methods are evaluated to find the best cluster sets, and then patients are categorized accordingly. Discrete-event simulation is applied to each patient category to estimate performance measures such as number of patients being served, waiting times, and length of stay, as well as the amount of CO2 emission. To optimize performance measures of patient flow, metaheuristic searches have been used. The dataset of Bushehr Heart Hospital is considered as a case study. Based on K-means, K-medoid, Hierarchical clustering, and Fuzzy C-means clustering methods, patients are categorized into two groups of high-risk and low-risk patients. The number of patients being served, total waiting time, length of stay, and CO2 emitted during care processes are improved for both groups. The proposed hybrid method is an effective method for hospitals to categorize patients based on care processes. The problems and the proposed solution approach reported in this study could be applicable to other hospitals, worldwide to help both optimize the patient flow and minimize the environmental consequences of care services.

Generating Synthetic Systems of Interdependent Critical Infrastructure Networks

The lack of data on critical infrastructure systems has hindered the research progress in modeling and optimizing the system performance. This work develops a method for generating Synthetic Interdependent Critical Infrastructure Networks (SICIN) using simulation and non-linear optimization techniques. SICIN consists of three components: (i) determining the location of facilities in individual networks via a modified simulated annealing algorithm, (ii) generating interdependent links based on a novel pseudo-tripartite graph algorithm, and (iii) simulating network flow using nonlinear optimization considering the operations of individual networks and their interdependencies. Two existing systems of interdependent infrastructure networks are used to validate the proposed method. The results demonstrate that SICIN outperforms state-of-the-art simulation methods according to multiple topological and flow measures of similarity between the simulated and real networks.

Optimization of Groundwater Pumping and River-Aquifer Exchanges for Management of Water Resources

Multi-objective optimization problems can be solved through Simulation-Optimization (S-O) techniques where the pareto front gives the optimal solutions in the problem domains. During the selection of different modelling methods, optimization techniques and management scenarios, several pareto fronts can be generated. In the present work, an attempt has been made by performing intensive comparisons between different pareto fronts to compare the efficiency and convergence of different S-O models. In this process, groundwater models were developed to simulate the River-Aquifer (R-A) exchanges for the study area as groundwater pumping influences the rate of R-A exchanges and alters the flow dynamics. The developed models were coupled with optimization models and were executed to solve the multi-objective optimization problems based on the maximization of discharge through pumping wells and maximization of groundwater input into the river through R-A exchanges. The distinctive features of the paper include a pareto front comparison where fronts developed by different S-O models were compared and analysed based on various parameters. The results show the dominance of Multi-Objective Particle Swarm Optimization (MOPSO) over other optimization algorithms and concluded that the maximization of pumping rate significantly changes after considering the R-A exchanges-based objective functions. This study concludes that the model domain also alters the output of simulation–optimization. Therefore, model domain and corresponding boundary conditions should be selected carefully for the field application of management models. The artificial neural network (ANN) models have been also developed to deal with the computationally expensive simulation models by reducing the processing time and found efficient.

Multi-Commodity distribution under uncertainty in disaster response phase: Model, solution method, and an empirical study

When earthquakes and other natural disasters occur, a surge in demand for life-supporting commodities can occur alongside transportation network disruption. Slow and ineffective delivery of water, food, medical supplies, and survival equipment can lead to high levels of anxiety in the population, mistrust for the government, and potentially social conflict and increased casualties. Thus, the issue of how to effectively and efficiently distribute essential commodities in the highly stochastic post-disaster response phase is extremely critical. In this research, we create a network flow model which allows the transportation of goods not only from distribution center to relief centers, but also among the relief centers. This structure allows local relief centers to support each other, which we show leads to much greater efficiency of commodity distribution to meet the demands of each relief center in the face of uncertain transportation network flow. We collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in Taiwan to model and simulate the relationship between earthquake attributes (e.g., magnitude, time of strike) and the resulting status of the transportation network and speed of vehicle traffic. By applying simulation optimization techniques to solve the split delivery multiple destination inventory routing problem, we are able to obtain the best vehicle and inventory routing decision under varying disaster scenarios. The resulting actionable insights can alleviate civil unrest and anxiety as well as save lives after a major earthquake.

Hybrid modeling of collaborative freight transportation planning using agent-based simulation, auction-based mechanisms, and optimization

The sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city.