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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.

Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems.

This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources.

Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework.

Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.

Simulation-based optimization is a decision-making tool for identifying an optimal design of a system. Here, optimal design means a smart system with sensing, computing and control capabilities with improved efficiency. As compared to testing the physical prototype, computer-based simulation provides much cheaper, faster and lesser time-and resource-consuming solutions. In this work, a comparative analysis of heuristic simulation optimization methods (genetic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed.

In this work, a comparative analysis of heuristic simulation optimization methods (genertic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed. Further, a novel simulation annealing-based heuristic approach is proposed for critical infrastructure.

A small scale network of 50–100 nodes shows that genetic simulation optimization with multi-criteria and multi-dimensional features performs better as compared to other simulation optimization approaches. Further, a minimum of 3.4 percent and maximum of 16.2 percent improvement is observed in faster route identification for small scale Internet-of-things (IoT) networks with simulation optimization constraints integrated model as compared to the traditional method.

In this work, simulation optimization techniques are applied for identifying optimized Quality of service (QoS) parameters for critical infrastructure which in turn helps in improving the network performance. In order to identify optimized parameters, Tabu search and ant-inspired heuristic optimization techniques are applied over QoS parameters. These optimized values are compared with every monitoring sensor point in the network. This comparative analysis helps in identifying underperforming and outperforming monitoring points. Further, QoS of these points can be improved by identifying their local optimum values which in turn increases the performance of overall network. In continuation, a simulation model of bus transport is taken for analysis. Bus transport system is a critical infrastructure for Dehradun. In this work, feasibility of electric recharging units alongside roads under different traffic conditions is checked using simulation. The simulation study is performed over five bus routes in a small scale IoT network.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

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.

Casting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.

A systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.

This study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.

This paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.

The systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

This paper aims to introduce the efficiency improvement in the operating room (OR) of a local hospital using the integration of simulation and optimization.

Based on the simulation model, a Tabu search (TS) algorithm is developed as an optimizer for the meta-heuristic optimization method to find the optimum configuration of resources for the OR operation.

The computational efficiency is improved for the optimum search. Results show that 21 percent more patients can be processed compared to the existing operation. The average time stay of patients in the OR is reduced by 17 percent.

Limited resources considered in the model may limit the capacity of the proposed method, more resources including nurses, beds in post-operative units, and beds in inpatient wards will be included in the decision variables.

Long waiting lists in the OR lead to the low performance of healthcare systems. It is crucial to identify inefficiency and to improve the OR operation efficiently.

The TS-based heuristic optimizing method developed in this research shows the promise in time saving of the optimal solution search for the OR efficiency improvement.

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 present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).

Cokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.

The results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.

A MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.

Scheduling problems in steel plants tend to be difficult and require complex algorithms due to many constraints. An approach is presented where only the main constraints are included in the scheduling algorithm. The schedule is validated using a discrete‐event simulation model that includes additional detail.

The combined approach is utilised for production scheduling in a steel mill in Finland. Operational performance of the steel mill is measured before and after software installation. The paper presents the scheduling environment, the software application and the resulting increase of production.

Case experiences indicate that combining optimisation techniques with simulation is beneficial. The optimisation can be kept simpler as validation with a simulation model increases the credibility and accuracy of the resulting schedule. During software development and testing, the simulation model offered a testing environment for the optimisation algorithm.

The case implementation was a success that increased production without making trade‐offs with other production goals. Company management estimate the productivity increase directly caused by the project to be worth €2,500,000 annually.

The paper presents a successful application of simulation for schedule validation in a complex and demanding environment.