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In this paper, the authors introduced a real world new problem, the multi-trip vehicle routing problem with time windows and the possible use of a less-than-truckload carrier to satisfy customer demands. The purpose of this paper is to develop a heuristic algorithm to route the private trucks with time windows and to make a selection between truckload and less-than-truckload carriers by minimizing a total cost function.

Both mathematical model and heuristic algorithm are developed for routing the private trucks with time windows and for selecting of less-than-truckload carriers by minimizing the total cost function.

In all, 40 test problems were examined with the heuristics. Computational results show that the algorithm obtains the optimal or near-optimal solutions efficiently in terms of time and accuracy.

The research described in this paper differs from the previous one on fleet planning or vehicle routing, in that it modifies the Clarke and Wright method by shifting the performance measure from a distance to cost and also incorporates the fixed cost of different types of trucks into the model. In addition, the authors simultaneously consider the multiple trip vehicle routing problems with time windows and the selection of less-than-truckload carriers that is an integrated scenario of real-world application. To the best of the authors’ knowledge, this scenario has not been considered in the literature.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

We consider the problem of assigning ship assembly blocks to storage locations at a ship yard while allowing one-way movement of the blocks. The objective is to minimize the number of blocks obstructing other blocks’ movements. We show that the integer program in a previous study contains errors in identifying obstructive blocks and therefore, suggest another integer program without the error. Also, we suggest construction heuristics and a tabu search algorithm using properties characterizing optimal solutions of subproblems. Computational experiment results indicate that our heuristics outperform the existing algorithm in terms of solution quality.

This paper aims to deal with the design optimization of a synchronous reluctance machine to be used in an X-ray tube, where the goal is to maximize the torque while keeping low the amount of material used, by means of gradient-based free-form shape optimization.

The presented approach is based on the mathematical concept of shape derivatives and allows to obtain new motor designs without the need to introduce a geometric parametrization. This paper presents an extension of a standard gradient-based free-form shape optimization algorithm to the case of multiple objective functions by determining updates, which represent a descent of all involved criteria. Moreover, this paper illustrates a way to obtain an approximate Pareto front.

The presented method allows to obtain optimal designs of arbitrary, non-parametric shape with very low computational cost. This paper validates the results by comparing them to a parametric geometry optimization in JMAG by means of a stochastic optimization algorithm. While the obtained designs are of similar shape, the computational time used by the gradient-based algorithm is in the order of minutes, compared to several hours taken by the stochastic optimization algorithm.

This paper applies the presented gradient-based multi-objective optimization algorithm in the context of free-form shape optimization using the mathematical concept of shape derivatives. The authors obtain a set of Pareto-optimal designs, each of which is a shape that is not represented by a fixed set of parameters. To the best of the authors’ knowledge, this approach to multi-objective free-form shape optimization is novel in the context of electric machines.

The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method.

The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly.

The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable.

The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels.

The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud.

This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

Cornelia de Lange syndrome (CdLS) is a congenital disorder characterized by distinctive facial features, growth retardation, limb abnormalities, intellectual disability, and behavioral problems. Cornelia de Lange syndrome is associated with abnormalities on chromosomes 5, 10 and X. Heterozygous point mutations in three genes (NIPBL, SMC3 and SMC1A), are responsible for approximately 50-60% of CdLS cases. CdLS is characterized by autistic features, notably excessive repetitive behaviors and expressive language deficits. The prevalence of autism spectrum disorder (ASD) symptomatology is comparatively high in CdLS. However, the profile and developmental trajectories of these ASD characteristics are potentially different to those observed in individuals with idiopathic ASD. A significantly higher prevalence of self-injury are evident in CdLS. Self-injury was associated with repetitive and impulsive behavior. This study describes the behavioral phenotype of four children with Cornelia de Lange syndrome and ASDs and rehabilitative intervention that must be implemented.

The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.

Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.

The electricity costs of the bus route can be reduced by applying the optimal charging plans.

This paper produces a viable option for transit agencies to reduce their operation costs.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost.

A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search.

Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space.

This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured.

The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases.

The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.