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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.

To determine optimum design and operation parameters for ternary batch distillation under fixed product demand.

In this study, two different scenarios are considered. In the first scenario, the column specification (in terms of number of plates and vapour load) and product demand are given and the optimum operation policy is determined. In the second scenario, with a fixed batch time and product demand, the optimal design (in terms of number of plates and vapour load) and operation policy (in terms of reflux ratio profile) are determined. In both scenarios, maximisation of a profit function reflecting capital cost, operating cost and penalty due to under/over production and customer dissatisfaction is considered. A detailed dynamic model consisting of mass and energy balances with rigorous thermodynamic property calculation model is used. The optimisation problem is solved using modified simulated annealing algorithm.

Two ternary mixtures leading to easy and difficult separation were considered. The off‐cut production and recycling has been found to be more beneficial for difficult separation mixture than that for easy separation mixture. The net profit increases with over production more than under production of the products. This is because of the penalty imposed for customer dissatisfaction. It is better to over produce, as that will achieve the maximum profit and (at the same time) satisfy the customer. Finally, for a typical case study, the net profit with optimum design is found to be about 25 per cent more compared to the net profit obtained with fixed design.

Optimal design and operation of multicomponent batch distillation has received limited attention in the past. Also, these studies were not geared for fixed product demand scenario. The optimisation problem formulation considered in the past, led to unlimited production of products (based on the assumption that all products produced are saleable) to maximise the profitability. Also, there were no penalties for over or under production of the desired products, production of off‐cuts and customer dissatisfaction due to not meeting the order (amount of products and delivery time, etc.). In this work, for the first time, these issues are addressed in the optimisation problem formulation.

This paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach, wherein the construction cost is predicted as a function of time, resources and environmental impact, which is further used as a surrogate model for cost optimization.

Taking a dataset from literature, the paper has applied various ML algorithms, namely, simple and regularized linear regression, random forest, gradient boosted trees, neural network and Gaussian process regression (GPR) to predict the construction cost as a function of time, resources and environmental impact. Further, the trained models were used to optimize the construction cost applying single-objective (with and without constraints) and multi-objective optimizations, employing Bayesian optimization, particle swarm optimization (PSO) and non-dominated sorted genetic algorithm.

The results presented in the paper demonstrate that the ensemble methods, such as gradient boosted trees, exhibit the best performance for construction cost prediction. Further, it shows that multi-objective optimization can be used to develop a Pareto front for two competing variables, such as cost and environmental impact, which directly allows a practitioner to make a rational decision.

Note that the sequential nature of events which dictates the scheduling is not considered in the present work. This aspect could be incorporated in the future to develop a robust scheme that can optimize the scheduling dynamically.

The paper demonstrates that a ML approach coupled with optimization could enable the development of an efficient and economic strategy to plan the construction operations.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

For the automatic drilling and riveting in panel assembly, gaps between the skin and strangers are inevitable and undesirable. At present, the determination of pre-joining schemes relies on workers’ experience, introducing excessive number and inappropriate locations of pre-joining. This paper aims to present a new method for the evaluation of residual clearances after pre-joining and the pre-joining scheme optimization, providing operation guidance for the workers in panel assembly workshop.

In this paper, an equivalent gap assembly model for pre-joining is proposed on the basis of the mechanism of variation. This model retains the essential elastic behavior of the key features during the pre-joining operation and calculates the residual clearances in the view of the potential energy. Subsequently, this method is embedded into a Pareto optimality-based genetic algorithm, and the optimal pre-joining schemes are achieved with the consideration of the total residual clearances and the permissive tolerances.

The equivalent gap assembly model has the capability to predict an acceptable degree of accuracy of the residual clearances and achieve the optimized pre-joining schemes with less number of pre-joining at the same level of residual clearances.

The optimized pre-joining schemes are given in the form of Pareto optimality set, and workers can select suitable results according to their inclination to the quality and efficiency.

The paper is the first to propose the equivalent gap assembly model for the pre-joining operation, which provides for the simplification of the calculation of residual clearances based on the constrained variation principles.

The paper has two main aims: to focus on how the spare parts optimization process was conducted in the Norwegian Defence procurement projects that had used the system approach based on OPUS10, and whether coordination issues affected the process and results; and to analyse empirical data in order to evaluate whether the theoretical claim of the system approach used through OPUS10, being better than other methods in terms of availability and spare parts investment cost holds up in reality.

Both qualitative and quantitative methods were used in order to answer the different questions of the study.

Very few Norwegian Defence projects have used the system approach through OPUS10. Empirical data however comply with the theoretical claims of potential large savings in spare parts investment cost and/or improvement in operational availability. Several organizational factors can explain the lack of use of OPUS10. The most important being lack of resources, lack of a centralized concept and a somewhat low‐project leader attitude towards the approach.

The study of Norwegian Defence cases makes generalizations of findings not applicable. The research model could however easily be transferred and utilized in the study of other organizations' spare parts optimization processes.

The Norwegian Defence should alter their concept for project governance and management in order to gain the full potential of the system approach used through OPUS10.

Few research papers have evaluated the promising theoretical findings of system‐based optimization based on empirical operational data. Even fewer, if any, studies have used a combination of factors from organization theory, economic organization theory and operation management theory in order to explain findings based on predefined hypotheses. This research should have value for both practitioners and researchers within the field spare parts optimization in general and systems management in particular.

Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage.

By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations.

This model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work.

Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management.

The purpose of this article is to analyze the optimization process in depth, elaborating on the components of the entire process and the techniques used. Researchers have been drawn to the expanding trend of optimization since the turn of the century. The rate of research can be used to measure the progress and increase of this optimization procedure. This study is phenomenal to understand the optimization process and different algorithms in addition to their application by keeping in mind the current computational power that has increased the implementation for several engineering applications.

Two-dimensional analysis has been carried out for the optimization process and its approaches to addressing optimization problems, i.e. computational power has increased the implementation. The first section focuses on a thorough examination of the optimization process, its objectives and the development of processes. Second, techniques of the optimization process have been evaluated, as well as some new ones that have emerged to overcome the above-mentioned problems.

This paper provided detailed knowledge of optimization, several approaches and their applications in civil engineering, i.e. structural, geotechnical, hydraulic, transportation and many more. This research provided tremendous emerging techniques, where the lack of exploratory studies is to be approached soon.

Optimization processes have been studied for a very long time, in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, different techniques and their prediction modes often require high computational strength, such parameters can be mitigated with the use of different techniques to reduce computational cost and increase accuracy.

The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor.

The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation.

This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy.

In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system.

This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries.

The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

The paper aims to apply numerical optimization to the aircraft design procedures applied in the airspace industry.

It is harder than ever to achieve competitive construction. This is why numerical optimization is becoming a standard tool during the design process. Although optimization procedures are becoming more mature, yet in the industry practice, fairly simple examples of optimization are present. The more complicated is the task to solve, the harder it is to implement automated optimization procedures. This paper presents practical examples of optimization in aerospace sciences. The methodology is discussed in the article in great detail.

Encountered problems related to the numerical optimization are presented. Different approaches to the solutions of the problems are shown, which have impact on the time of optimization computations and quality of the obtained optimum. Achieved results are discussed in detail with relation to the used settings.

Investigated different aspects of handling optimization problems, improving quality of the obtained optimum or speeding-up optimization by parallel computations can be directly applied in the industry optimization practice. Lessons learned from multidisciplinary optimization can bring industry products to higher level of performance and quality, i.e. more advanced, competitive and efficient aircraft design procedures, which could be applied in the industry practice. This can lead to the new approach of aircraft design process.

Introduction of numerical optimization methods in aircraft design process. Showing how to solve numerical optimization problems related to advanced cases of conceptual and preliminary aircraft design.