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The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

Twin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM).

This study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets.

The experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise.

This study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.

The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost.

In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period.

The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time.

Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight.

This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits.

This paper aims to address a distributed assembly permutation flow-shop scheduling problem (DAPFSP) considering budget constraints and factory eligibility. The first stage of the considered production system is composed of several non-identical factories with different technology levels and so the factories' performance is different in terms of processing time and cost. The second stage is an assembly stage wherein there are some parallel work stations to assemble the ready parts into the products. The objective function is to minimize the maximum completion time of products (makespan).

First, the problem is formulated as mixed-integer linear programing (MIP) model. In view of the nondeterministic polynomial (NP)-hard nature, three approximate algorithms are adopted based on variable neighborhood search (VNS) and the Johnsons' rule to solve the problem on the practical scales. The proposed algorithms are applied to solve some test instances in different sizes.

Comparison result to mathematical model validates the performance accuracy and efficiency of three proposed methods. In addition, the result demonstrated that the proposed two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm outperforms the other two proposed methods. Moreover, the proposed model highlighted the effects of budget constraints and factory eligibility on the makespan. Supplementary analysis was presented by adjusting different amounts of the budget for controlling the makespan and total expected costs. The proposed solution approach can provide proper alternatives for managers to make a trade-off in different various situations.

The problem of distributed assembly permutation flow-shop scheduling is traditionally studied considering identical factories. However, processing factories as an important element in the supply chain use different technology levels in the real world. The current paper is the first study that investigates that problem under non-identical factories condition. In addition, the impact of different technology levels is investigated in terms of operational costs, quality levels and processing times.

This study aims to investigate the impact on organizational members of team marks and peer feedback in a classroom as an organizational setting, where equals were engaged in a hierarchical form of accountability. It uses Roberts’s framework of hierarchical, socializing, and intelligent forms of accountability and discusses the viability of intelligent accountability in higher education, given the accountability structure for academics.

Autoethnography based on excerpts from the lecturer’s diary.

The blurred boundaries of hierarchical and socializing forms of accountability create both tensions and kinships for students, and these two forms of accountability constantly impact on each other. Although the accounting tools have an individualizing effect on some students, several examples of intelligent accountability are uncovered. It is concluded that academia’s audit culture, which focuses on immediate outcomes, and academics’ ever-increasing workloads make successful innovations less likely.

This study contributes to the accountability literature in revealing a constant dynamic between hierarchical and socializing forms of accountability through examination of a unique setting in which the boundaries between the two are completely blurred. By empirically examining how accounting individualizes and how intelligent accountability emerges, this study contributes to the limited empirical literature on the impact of accountability on individuals, and particularly to studies of classrooms as organizations, with implications for education policies.

Teaching management information systems (MIS) fundamental course remains a challenging task to date, encountering several continuing complaints from students as well as instructors, as per several studies. Several reasons are reported, some of which are related to little embrace of more innovative non-traditional techniques, and limited literature guidance on selection and effective use of appropriate strategies to various settings. Toward instructional innovation, this paper proposes and tests a pedagogical instrument. By introducing this tool, the ultimate objective is to minimize the confusion that students usually encounter during this course and to empower the instructors in the delivery of its interdisciplinary knowledge.

A triangulation of four methods is being employed: conceptual development, experimentation for 12 years, assessment in light of the self-regulated learning theory, and empirical analysis. For the latter method, an open-ended questionnaire as well as group interviewing were carried out.

The proposed instrument (need-function instrument [NFI]) was found to be in support of self-regulated learning to a promising extent in terms of its four core aspects. The participants were more inclined to accept as well as appreciate the instrument than report drawbacks. Yet, there are few complaints for the inability to distinguish between two introduced terms, ‘Information Problem’ and ‘Operations Problem’. Therefore, a future effort is deemed necessary to explore this aspect.

The results are based on a single case of educational institution which is not enough to generalize for other educational environments. Future research is necessary for testing in a variety of settings in terms of type of classroom, number of students enrolled, type of institution (public or private), etc.

Whereas the instrument is anticipated to render improvements in the students’ understanding of MIS content and to alleviate the task of the course leader in delivering the course, there are possible practical implications that should be paid careful attention to. They were analyzed in terms of four dimensions: the Instructor, the Student, the Classroom and Time.

The paper introduces a new pedagogical instrument/tool for teaching the MIS fundamental course.

Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.

This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.

The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.

This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I.

Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed.

A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality.

The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.

Considering the changing dimensions of client influence in the emerging sub-market in Nigeria, different from previous general insinuations, this article examines the new strategies adopted by clients to influence estate surveyors and valuers (ESVs), factors that predispose ESVs to client influence and the effects of clients' influence on valuation outcomes and real estate markets in emerging sub-market, using Ibadan market as the study area.

The paper is situated within a client influence assessment framework, modified to reflect contextual incidents. Contextualization was made possible with the involvement of both practitioners and academic researchers. Validated copies of the questionnaire were administered to the registered practicing ESVs in an intact group during their monthly state (provincial) meeting and through direct delivery at their firms. Data collected were analyzed using descriptive and inferential statistics.

Contrary to the previous studies, the authors found no significant relationship between ESV professional qualifications, the firm's staff strength and the frequency of clients' influence in valuation assignments. Hiding important information and clauses, begging, lobbying, and seeking undue favor and promises for future jobs or appointments are the influencing strategies clients employ to pressure valuer. The topmost factors are emerging sub-market and economic-induced factors, lack of due process, and adequate transparency on the parts of firms and Valuers. It was established that the new dimension of client influence leads to the mortgage valuation accuracy dilemma, discredit of professional confidence, default and financial distress, and generating mistrust in the property market.

The implication is the new dimension of client influence, different from the previous studies, thus calling for professional and policy attention. As real estate investment and transactions transcend globally, understanding the local sub-market condition is imperative.

The novelty of the paper is the exposition on the dimensions of client influence within the economy and the implication for the professional body regulatory policy.

During high demand for the virtualized resources in cloud environment, efficient task scheduling achieves the desired performance criteria by balancing the load in the system.

It is a task scheduling approach used for load balancing in cloud environment. Task scheduling in such an environment is used for the task execution on a suitable resource by considering some parameters and constraints to achieve performance.

The presented mechanism is an extension of the previous proposed work quality of service (QoS)-enabled join minimum loaded queue (JMLQ) (Sharma et al., 2019c). The proposed approach has been tested in the CloudSim simulator, and the results show that the proposed approach achieves better results in comparison to QoS-enabled JMLQ and its other variants in the cloud environment.

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