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The purpose of this paper is to present the terms of sequence operators and grey data mining.

The definitions of basic terms about sequence operators and grey data mining are presented one by one.

The reader could know the basic explanation about the important terms about sequence operators and grey data mining from this paper.

Many of the colleagues thought that unified definitions of key terms would be beneficial for both the readers and the authors.

It is a fundamental work to standardize all the definitions of terms for a new discipline. It is also propitious to spread the universal principles of grey system theory.

Shows an approach to workplace design, employing a well‐known prescribed technological operation. Using a non‐linear optimization method, searches for the minimal value of the objective function with respect to the constraints determined by the human locomotive sytem and by the prescribed technological operation of sewing cloth parts. Demonstrates the usefulness of this approach with a sewing example, where the reference task is the movement of the operator’s arms with the defined velocity profile.

A library that wishes to locate OCLC M300 Workstations and other terminals more than sixty feet apart is faced with an important decision. OCLC does not authorize the use of cables longer than sixty feet. This is also the longest cable obtainable from OCLC. OCLC's solution is to install an additional modem for the distant terminal or a pair of OCLC Line Extenders. The financial incentive for a library to use a longer cable as opposed to installing an additional OCLC Paradyne modem or the OCLC Line Extenders is significant.

To provide a new proof of convergence of the Adomian decomposition series for solving nonlinear ordinary and partial differential equations based upon a thorough examination of the historical milieu preceding the Adomian decomposition method.

Develops a theoretical background of the Adomian decomposition method under the auspices of the Cauchy‐Kovalevskaya theorem of existence and uniqueness for solution of differential equations. Beginning from the concepts of a parametrized Taylor expansion series as previously introduced in the Murray‐Miller theorem based on analytic parameters, and the Banach‐space analog of the Taylor expansion series about a function instead of a constant as briefly discussed by Cherruault et al., the Adomian decompositions series and the series of Adomian polynomials are found to be a uniformly convergent series of analytic functions for the solution u and the nonlinear composite function f(u). To derive the unifying formula for the family of classes of Adomian polynomials, the author develops the novel notion of a sequence of parametrized partial sums as defined by truncation operators, acting upon infinite series, which induce these parametrized sums for simple discard rules and appropriate decomposition parameters. Thus, the defining algorithm of the Adomian polynomials is the difference of these consecutive parametrized partial sums.

The four classes of Adomian polynomials are shown to belong to a common family of decomposition series, which admit solution by recursion, and are derived from one unifying formula. The series of Adomian polynomials and hence the solution as computed as an Adomian decomposition series are shown to be uniformly convergent. Furthermore, the limiting value of the mth Adomian polynomial approaches zero as the index m approaches infinity for the prerequisites of the Cauchy‐Kovalevskaya theorem. The novel truncation operators as governed by discard rules are analogous to an ideal low‐pass filter, where the decomposition parameters represent the cut‐off frequency for rearranging a uniformly convergent series so as to induce the parametrized partial sums.

This paper unifies the notion of the family of Adomian polynomials for solving nonlinear differential equations. Further it presents the new notion of parametrized partial sums as a tool for rearranging a uniformly convergent series. It offers a deeper understanding of the elegant and powerful Adomian decomposition method for solving nonlinear ordinary and partial differential equations, which are of paramount importance in modeling natural phenomena and man‐made device performance parameters.

Beginning Farmer and Rancher programs are available for operators with ten years of experience or less on any farm. These programs support farmers who are starting operations, often without an initial asset allocation. However, some beginning farmers acquire operations that are already established, with substantial assets in place. The authors investigate whether a profitability gap exists between beginning farmers entering the industry ex novo and those operating a preexisting operation and if so, what factors contribute to the gap.

The authors utilize the Blinder-Oaxaca decomposition to determine what drives financial differences between first-generation beginning farmers, second-generation beginning farmers and established farmers using a unique farm-level panel dataset from 1997 to 2021.

Results indicate that first- and second-generation beginning farmers have similar operating profit margins, but first-generation beginning farmers have a statistically higher rate of return on assets than second-generation beginning farmers. Established farmers outperform second-generation beginning farmers on both the operating profit margin and rate of return on assets. These results suggest that economic viability for beginning farmers differs depending upon the initial status of their operation, suggesting that heterogenous policies may be more impactful in supporting various pathways to enter agriculture.

This analysis is the first to identify beginning farmers that enter the industry without an asset base and those that take over a principal operator role on an established farm through an assumed farm transition. The authors quantify differences in financial performance using detailed accrual-based financial data that tracks farms over time in one dataset.

The purpose of this paper is to share the authors' initial insights into tourism industry capacity building via flexibly delivered online skilling and knowledge sharing.

An online research survey approach was employed, involving a sample of 64 micro tourism operators.

The paper finds that the major benefits perceived by operators across the pilot region, were the time saving aspects demonstrated in the smart form concept. Operators were also drawn in by the best practise examples and direct links to the online resources to bring and keep them up to date with industry information and developments.

The data presented in this paper represent initial findings of the pilot project. The project has since been completed.

The model used for the pilot in this paper has been adopted by the Tourism Accreditation Board of Victoria and is now being considered for national rollout across Australia. The model is a practical and replicable capacity building model for micro tourism operators anywhere.

The paper adopts a collaborative learning network approach to micro business capacity building and training.

With the increasing use of automatic and NC machine tools the adoption of flexible, multi‐skilled operators has also increased. However, the variety of machining capacity and manpower skills frequently identifiable in industrial studies presents a problem which has no general analytical solution. This work carried out in collaboration with a leading manufacturing company examines the capabilities of various combinations of setters, operators and setter/operators in the context of given machine capacity and product mix constraints. A description of the problem situation is followed by an account of the model's development, subsequent application and validation against actual performance. In addition to providing a useful analysis of the general characteristics of the groups considered, specific information relating to the likely effects of changes in manpower parameters was provided to assist management in the review of its manufacturing system design. Generally, this work demonstrates that although there are a number of attendant difficulties, a practical approach to computer modelling is becoming increasingly relevant as shop‐floor sophistication increases.

This paper aims to unfold the role that job rotation plays in a lean cell. Unlike many studies, the authors consider heterogeneous operators with dynamic performance factor that is impacted by the assignment and scheduling decisions. The purpose is to derive an understanding of the underlying effects of job rotations on performance metrics in a lean cell. The authors use an optimization framework and an experimental design methodology for sensitivity analysis of the input parameters.

The approach is an integration of three stages. The authors propose a set-based optimization model that considers human behavior parameters. They also solve the problem with two meta-heuristic algorithms and an efficient local search algorithm. Further, the authors run a post-optimality analysis by conducting a design of experiments using the response surface methodology (RSM).

The results of the optimization model reveal that the job rotation schedules and the human cognitive metrics influence the performance of the lean cell. The results of the sensitivity analysis further show that the objective function and the job rotation frequencies are highly sensitive to the other input parameters. Based on the findings from the RSM, the authors derive general rules for the job rotations in a lean cell given the ranges in other input variables.

The authors integrate the job rotation scheduling model with human behavioral and cognitive parameters and formulate the problem in a lean cell for the first time in the literature. In addition, they use the RSM for the first time in this context and offer a post-optimality analysis that reveals important information about the impact of the job rotations on the performance of operators and the entire working cell.

Operating procedure synthesis (OPS) has been used to generate plant operating procedures for chemical plants. However, the application of AI planning to this domain has been rarely considered, and when it has the scope of the system used has limited it to solving “toy” problems. This paper describes the application of state‐of‐the‐art AI planning techniques to the generation of operating procedures for chemical plant as part of the INT‐OP project at the Universities of Salford and Loughborough. The CEP planner is outlined and its application to a double effect evaporator test rig is discussed in detail. Particular attention is paid to the issues involved in domain modelling, requiring the description of the domain, development of AI planning operators, the definition of safety restrictions, and the definition of the problem. There is then a presentation of the results, lessons learned and problems still remaining.

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.