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This study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of m_k identical machines in stage k and the production flow is immaterial.

Computational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances.

The proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality.

The authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system.

First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly.

Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance.

In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

Under the development trend of intelligent manufacturing, the unstructured environment requires the robot to have a good generalization performance to adapt to the scene changes. The purpose of this paper aims to present a learning from demonstration (LfD) method (task parameterized [TP]-dynamic movement primitives [DMP]-GMR) that combines DMPs and TP-LfD to improve generalization performance and solve object manipulation tasks.

The dynamic time warping algorithm is applied to processing demonstration data to obtain a more standard learning model in the proposed method. The DMPs are used to model the basic trajectory learning model. The Gaussian mixture model is introduced to learn the force term of DMPs and solve the problem of learning from multiple demonstration trajectories. The robot can learn more local geometric features and generalize the learned model to unknown situations by adding task parameters.

An evaluation criterion based on curve similarity calculated by the Frechet distance was constructed to evaluate the model’s interpolation and extrapolation performance. The model’s generalization performance was assessed on 2D virtual data sets, and first, the results show that the proposed method has better interpolation and extrapolation performance than other methods.

The proposed model was applied to the axle-hole assembly task on real robots, and the robot’s posture in grasping and placing the axle part was taken as the task parameter of the model. The experiment results show that The proposed model is competitive with other models.

For oil film thickness measurement using ultrasonic spring model, obtaining the isolated reflection from the oil film layer is the key point. While for oil film thickness measurement in thrust bearings with thin liner, the reflection from the substrate-Babbitt interface will overlap with the reflection from the oil film layer. This overlapping will render the ultrasonic spring model invalid. To obtain the isolated reflected signal from the oil film layer accurately, an adaptive method was developed to recover the overlapping echoes.

A genetic-algorithm-based support matching pursuit (GA-based SMP) was developed to provide the optimal echo number and initial parameters guesses automatically and efficiently. Then, the traditional expectation maximization (EM) model was used to fine tune the accurate results.

The developed method was tested using both simulated echoes and the overlapping echoes encountered in the ultrasonic oil film thickness measurement of thrust bearings. The results demonstrated that the developed method performed well on recovering overlapping echoes adaptively.

The work shows an adaptive method to recover the ultrasonic overlapping echoes. When used in ultrasonic oil film thickness measurement, it can help extend the application of traditional ultrasonic spring model to objects with four or more layers.

The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed.

The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC.

Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes.

An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

India has the largest public distribution system (PDS) in the world, working through over five million fair price shops (FPS) to distribute food grains among its beneficiaries at a subsidized rate. In this paper, the authors study the inventory system of Indian FPS. The system involves a distributor, who is solely responsible for the replenishment of the FPS. In a real-world scenario, the distributor is subjected to random supply and transportation disruptions. The purpose of this paper is to investigate and minimize the impacts of such disruptions.

In this paper, the authors adopt a simulation-based technique to explore the impacts of various traits of disruptions like frequency and duration on the FPS inventory system. A simulation model for the Indian FPS is developed and the impacts of disruptions are investigated by a case study.

The authors use a simulation-based optimization technique to suggest a simple managerial change that can lead to a minimization of inventory shortage up to 60 per cent and system cost up to 21 per cent over the existing practice.

The present study addresses the FPS inventory system of Indian PDS, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

Civil engineering structures are regularly confronted with failures that can lead to catastrophic consequences. It is important, after a failure, to be able to identify the origin and the sequence of factors that led to it. This failure analysis by experts, called forensic engineering investigation, generally leads to the drafting of an expert report. These reports do not inform on the processes that guided the experts to a conclusion and the uncertainties involved. This paper aims to propose a new methodological approach to formalize the opinions of experts in forensic engineering.

The research consists in combining abstract argumentation with the theory of imprecise probabilities to take into account epistemic and stochastic uncertainties to support forensic engineering investigation.

A model and a tool to support forensic analysis are presented. An application on the collapse of the Brumadinho dam highlights the interest of the chosen approach.

This work is the first use of the abstract argument framework in civil engineering, and so in forensic engineering. Furthermore, it provides an innovative model based on imprecise probability for AAF.

Teaching students/library patrons twenty-first century literacies (such as information and library literacies) is important within a library setting. As such, finding an appropriate manner to teach these skills in a practical manner at tertiary level is important. As vehicles for constructivist learning, games provide a unique opportunity to teach these twenty-first century literacies in an engaging, practical, format. The purpose of this paper is to discuss the implementation of an alternate reality game (ARG) to teach these literacies through gameplay.

An ARG was designed and developed where the core gameplay tasks taught and exercised twenty-first century literacies. The game, once completed, was then analysed as a case study to determine the effectiveness of the game-based approach to literacy learning.

Throughout the play of the game, players spent increasingly more time in the library, often using it as a common meeting point during play. Players reported that they learnt or exercised the skills that each game task focussed on, additionally noting that the game-based context made the process of learning and exercising these skills more enjoyable.

The findings suggest that the creation of games, whether real world or digital, may be useful in engaging students/patrons with twenty-first century literacies as well as with their local library. The documentation of a successful ARG to teach twenty-first century literacies provides a model for future research to follow when designing engaging library-oriented games.

The first company under the Amara Raja Group was established in 1984, i.e. Amara Raja Electronics Limited (AREL) followed by Amara Raja Batteries Limited (ARBL). Its founder leveraged the presence of his family in Renigunta, a rural village in South India, and chose to start the industry there to create employment opportunities. Preference is given to local population in all ARG enterprises. Despite its strong people orientation, the HR department/function at ARG got strengthened only after Jaikrishna strived to make it central to business. The department's evolution has been demarcated in three phases. The first and second phase saw few initiatives, and during the third phase the HR department was structured according to the Dave Ulrich Strategic HR Model. While this structure had been successful until now, certain sections in ARG still doubted its sustainability.

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application.

In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis.

This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis.

The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.