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This paper aims to provide decision support for precast concrete contractors about both precast concrete supply chain strategies and construction configurations.

This paper proposes a simulation-based optimisation for supply chain and construction (SOSC) during the planning phase of PC building projects. The discrete event simulation is used to capture the characteristics of supply chain and construction processes, and calculate construction objectives under different plans. Particle swarm optimisation is combined with simulation to find optimal supply chain strategies and construction configurations.

The efficiency of SOSC is compared with the parametric simulation approach. Over 70 per cent of time and effort used to simulate and compare alternative plans is saved owing to SOSC.

Building simulation model costs a lot of time and effort. The data requirement of the proposed method is high.

The proposed SOSC approach can provide decision support for PC contractors by optimising supply chain strategies and construction configurations.

This paper has two contributions: one is in providing a decision support tool SOSC to optimise both supply chain strategies and construction configurations, while the other is in building a prototype of SOSC and testing it in a case study.

The purpose of this paper is to understand and explain how firms use simulation-based decision support systems (DSSs) to optimize container space utilization.

Using a case study of a logistics company, this research analyzed the application of optimization software through simulation to make efficient loading decisions. The current study attempted to find a method for optimizing and making a loading plan to achieve higher container space utilization using a simulation method.

A simulation-based DSS and application of an optimization method contributes to the reduction of container shipment volume, and saves logistic costs and its delivery time. This research offers a method for optimizing a loading decision to optimize container space utilization.

The present study is based on a single case study of only one specific type of product, i.e., motorcycle spares parts within a specific industry.

Apart from adding value to the shipment process and improving the efficiency of loading plans, with the use of optimization software, the collaboration between buyers and suppliers can be encouraged to reduce response time and bringing transparency in the pricing process of the shipment.

This research addresses a key concern in the transportation industry: how to reduce the logistics costs and the delivery time. This study demonstrates how a simulation-based tool can be used to reduce freight cost, cycle time, instill waste minimization and improve overall value addition.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

This paper evaluates a Successive Response Surface Method (SRSM) specifically developed for simulation‐based design optimization, e.g. that of explicit nonlinear dynamics in crashworthiness design. Linear response surfaces are constructed in a subregion of the design space using a design of experiments approach with a D‐optimal experimental design. To converge to an optimum, a domain reduction scheme is utilized. The scheme requires only one user‐defined parameter, namely the size of the initial subregion. During optimization, the size of this region is adapted using a move reversal criterion to counter oscillation and a move distance criterion to gauge accuracy. To test its robustness, the results using the method are compared to SQP results of a selection of the well‐known Hock and Schittkowski problems. Although convergence to a small tolerance is slow when compared to SQP, the SRSM method does remarkably well for these sometimes pathological analytical problems. The second test concerns three engineering problems sampled from the nonlinear structural dynamics field to investigate the method's handling of numerical noise and non‐linearity. It is shown that, despite its simplicity, the SRSM method converges stably and is relatively insensitive to its only user‐required input parameter.

Modularization and level of repair analysis for fleet system influences every phase of the system life cycle. Modular based fleet system design raises new issues since the maintenance/repair services introduces further requirements than traditional product engineering. The decision of modular system and level of repair plays an important role to reduce the Life Cycle Costs (LCC) of fleet maintenance system. The concept of modularity has been extended to services in maintenance for the varieties of fleet systems such as wind turbines, gas turbines, advance machine tools and aircrafts etc. System modularity allows the designers to use of different design alternatives and ease of fault diagnosis, repair and services. The purpose of this paper to develop a joint optimization approach for optimal selection of modular design and level of repair decisions. Usually these two decisions are taken separately.

In the proposed joint approach, level of repair analysis is used to obtain the optimal modular design decisions with reduced life cycle cost. In the existing research, the effect of system modularity on the level of repair decisions is investigated. The simulation-based approach is used to solve this joint problem. Which is rarely seen in the existing literature. A genetic algorithm-based simulation is used to investigate the joint problem. The proposed approach also evaluates all the possible configurations of modular design to justify the integrated effect of modularity and maintenance decisions, that is Level of Repair (LOR).

This paper highlights interactive effect of system modularity and level of repair decisions for the system operated in multi-echelon maintenance network. A comparative study is provided on effect of system modularity and level of repair decisions considering the time dependent failure rate and constant failure rate of the system components. A simulation based joint approach is used to solve this problem. The results obtained from the investigation are shown that modularity plays an important role to allocate modularity and level of repair decisions for the fleet system. The novelty of this research work is to identify the role of modularization to optimizing the level of repair decisions. The models, that is time-dependent failure rate and constant failure rate presented in this study provides more practical approach to deal the modularity and level of repair analysis.

The proposed joint approach illustrates using a numerical case of a mechanical system operated at fleet level. More modular structure in terms of number of modules in the machine may be presented for an industrial case. Additionally, the joint approach can also be extended for the any other consumer product and system. But, the prime motive of the paper is to highlights the importance of the modular design while selecting the level of repair decisions.

This is the first work which consider the joint optimization of modular design and level of repair analysis to the best of authors knowledge. Present paper is a more practical approach for identifying the modular design and level of repair decisions for the system operated at fleet level.

The primary aim of this article is to ascertain the modalities of leveraging Lean Six Sigma (LSS) for Industry 4.0 (I4.0) with special reference to the process industries. Moreover, it intends to determine the applicability of simulation-based LSS in the automation of the mineral water industry, with special emphasis on the robust design of the control system to improve productivity and performance.

This study adopts the action research methodology, which is exploratory in nature along with the DMAIC (define-measure-analyze-improve-control) approach to systematically unearth the root causes and to develop robust solutions. The MATLAB simulation software and Minitab statistical software are effectively utilized to draw the inferences.

The root causes of critical to quality characteristic (CTQ) and variation in purity level of water are addressed through the simulation-based LSS approach. All the process parameters and noise parameters of the reverse osmosis (RO) process are optimized to reduce the errors and to improve the purity of the water. The project shows substantial improvement in the sigma rating from 1.14 to 3.88 due to data-based analysis and actions in the process. Eventually, this assists the management to realize an annual saving of 20% of its production and overhead costs. This study indicates that LSS can be applicable even in the advent of I4.0 by reinforcing the existing approach and embracing data analysis through simulation.

The limitation of this research is that the inference is drawn based on a single case study confined to process industry automation. Having said that, the methodology deployed, scientific information related to optimization, and technical base established can be generalized.

This article is the first of its kind in establishing the integration of simulation, LSS, and I4.0 with special reference to automation in the process industry. It also delineates the case study in a phase-wise manner to explore the applicability and relevance of LSS with I4.0. The study is archetype in enabling LSS to a new era, and can act as a benchmark document for academicians, researchers, and practitioners for further research and development.

The purpose of this work is to apply and compare surrogate-assisted and multi-fidelity, multi-objective optimization (MOO) algorithms to simulation-based aerodynamic design exploration.

The three algorithms for multi-objective aerodynamic optimization compared in this work are the combination of evolutionary algorithms, design space reduction and surrogate models, the multi-fidelity point-by-point Pareto set identification and the multi-fidelity sequential domain patching (SDP) Pareto set identification. The algorithms are applied to three cases, namely, an analytical test case, the design of transonic airfoil shapes and the design of subsonic wing shapes, and are evaluated based on the resulting best possible trade-offs and the computational overhead.

The results show that all three algorithms yield comparable best possible trade-offs for all the test cases. For the aerodynamic test cases, the multi-fidelity Pareto set identification algorithms outperform the surrogate-assisted evolutionary algorithm by up to 50 per cent in terms of cost. Furthermore, the point-by-point algorithm is around 27 per cent more efficient than the SDP algorithm.

The novelty of this work includes the first applications of the SDP algorithm to multi-fidelity aerodynamic design exploration, the first comparison of these multi-fidelity MOO algorithms and new results of a complex simulation-based multi-objective aerodynamic design of subsonic wing shapes involving two conflicting criteria, several nonlinear constraints and over ten design variables.

This paper aims to propose an approach for the optimization of imperfect preventive maintenance and corrective actions performed on a single machine. After maintenance, the machine returns to an age between “as good as new” and “as bad as old”.

The approach is based on fuzzy logic and simulation‐based optimization. Fuzzy logic is preferred over crisp logic because it is relatively easy to implement in this situation considering that the human factor is hardly interpreted by analytical methods because of its unpredictable nature. Simulation‐based optimization is used to have a more reactive and accurate tool for practitioners.

Taking into account the impact of the imperfections due to human factors, the period for preventive maintenance, which minimizes the expected cost rate per unit of time or maximizes the availability of the system, is evaluated by the simulation‐based optimization.

Different and more realistic maintenance levels must be considered and the traceability of a specific system could be used to determine the most appropriate failure law. For this study, cost reduction was considered as the priority, but the model can be adjusted according to the user's preferences.

This paper considers a single repairable machine as a system that undergoes periodic preventive and corrective maintenance actions. Considering maintenance imperfections, rule‐based fuzzy logic can be integrated into the maintenance program to determine the times for the periodic preventive maintenance actions.

Considering human factors in maintenance programs is indispensable to assure more accurate and realistic results. However, due to the difficulty engendered by their modeling, most theoretical maintenance models do not consider these factors. Therefore, the proposed fuzzy model in the paper can be an important tool to include them.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

In many countries, innovation in building design for improving energy performance, reducing CO2 emissions and minimizing life cycle cost has received much attention for sustainable development. This paper investigates the importance of optimization tools for enhancing the design performance in the early stages of Vietnam's cooling-dominated buildings in hot and humid climates using an integrated building design approach.

The methodology of this study exploits the non-dominated sorting genetic algorithm (NSGA-II) optimization algorithm coupled with building simulation to research a trade-off between the optimization of investment cost and energy consumption. Our approach focuses on the whole optimization problem of thermal envelope, glazing and energy systems from preliminary design phases. The methodology is then tested for a case study of a non-residential building located in Hanoi.

The results show a considerable improvement in design performance by our method compared to current building design. The optimal solutions present the trade-off between energy consumption and capital cost in the form of a Pareto front. This helps architects, engineers and investors make important decisions in the early design stages with a large view of impacts of all factors on energy performance and cost.

This is one of the original research to study integrated building design applying the simulation-based genetic optimization algorithm for cooling-dominated buildings in Vietnam. The case study in this article is for a non-residential building in the north of Vietnam but the methodology can also be applied to residential buildings and other regions.