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Workload is a critical concept in the evaluation of performance and quality in healthcare systems, but its definition relies on the perspective (e.g. individual clinician-level vs unit-level workload) and type of available metrics (e.g. objective vs subjective measures). The purpose of this paper is to provide an overview of objective measures of workload associated with direct care delivery in tertiary healthcare settings, with a focus on measures that can be obtained from electronic records to inform operationalization of workload measurement.

Relevant papers published between January 2008 and July 2018 were identified through a search in Pubmed and Compendex databases using the Sample, Phenomenon of Interest, Design, Evaluation, Research Type framework. Identified measures were classified into four levels of workload: task, patient, clinician and unit.

Of 30 papers reviewed, 9 used task-level metrics, 14 used patient-level metrics, 7 used clinician-level metrics and 20 used unit-level metrics. Key objective measures of workload include: patient turnover (n=9), volume of patients (n=6), acuity (n=6), nurse-to-patient ratios (n=5) and direct care time (n=5). Several methods for operationalization of these metrics into measurement tools were identified.

This review highlights the key objective workload measures available in electronic records that can be utilized to develop an operational approach for quantifying workload. Insights gained from this review can inform the design of processes to track workload and mitigate the effects of increased workload on patient outcomes and clinician performance.

Air traffic controllers (ATCOs) play a fundamental role in the safe, orderly and efficient management of air traffic. In the interests of improving safety, it would be beneficial to know what the workload thresholds are that permit ATCOs to carry out their functions safely and efficiently. The purpose of this paper is to present the development of a simulation platform to be able to validate an affective-cognitive performance methodology based on neurophysiological factors applied to ATCOs, to define the said thresholds.

The process followed in setting up the simulation platform is explained, with particular emphasis on the design of the program of exercises. The tools designed to obtain additional information on the actions of ATCOs and how their workload will be evaluated are also explained.

To establish the desired methodology, a series of exercises has been designed to be simulated. This paper describes the project development framework and validates it, taking preliminary results as a reference. The validation of the framework justifies further study to extend the preliminary results.

This paper describes the first part of the project only, i.e. the definition of the problem and a proposed methodology to arrive at a workable solution. Further work will concentrate on carrying out a program of simulations and subsequent detailed analysis of the data obtained, based on the conclusions drawn from the preliminary results presented.

The methodology will be an important tool from the point of view of safety and the work carried out by ATCOs. This first phase is crucial as it provides a solid foundation for later stages.

To propose a methodology based on genetic algorithm (GA) to solve the parallel machine scheduling problems with precedence constraints.

Workflow balancing helps to remove bottlenecks present in a shop floor yielding faster movements of components or jobs. Multiple machines are used in parallel for processing the jobs to meet the demand. In parallel machine scheduling with precedence constraints, there are m machines to which n jobs are assigned using suitable scheduling algorithms. Workflow of a machine is the sum of processing time of all jobs assigned. All the preceding jobs are allocated first to satisfy the constraints. GA is developed to solve parallel machine scheduling problems with precedence constraints based on the objective of workflow balancing. The GA was coded on IBM/PC compatible system in the C++ language for simulation to a standard manufacturing environment.

The relative percentage of imbalance (RPI) in workloads among the parallel machines is used to evaluate the performance of the GA developed. The proposed GA produces lesser RPI values against the RANDOM heuristic algorithm for a wider range of jobs and machines.

The performance of GA can be compared with the performance of other meta‐heuristic algorithms to find out the robustness of the results obtained by this research.

The proposed GA also gives better solution for a case study of assembly scheduling.

The allocation of assembly operations to the operators is modeled into a parallel machine scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision-makers aim to design an efficient assembly line while satisfying a set of constraints.

An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP to optimize the number of stations and the workload smoothness.

To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs.

The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is used in the IGA to enhance its local search capability.

In order to fill the gap in theoretical and empirical knowledge about the characteristics of principal workload, the purpose of this paper is to explore the components of principal workload as well as its determinants and the coping strategies commonly used by principals to face this personal state.

Semi-structured interviews were conducted with 50 principals, all from the elementary and secondary educational systems of Israel. The analysis followed the principles of qualitative research.

Four subjectively held constructs of principal workload, main sources of this workload, and the key strategies used by principals to face this workload were found in this study.

It is recommended to strengthen school autonomy, increase the number of positions of middle management, prepare future principals for the heavy workload, and encourage supportive superiors who are sensitive to this issue.

This study fills the gap in theoretical knowledge concerning principal workload, assuming that the particular characteristics of the school organization have some unique impact on this personal state. It also enables us to identify the types of this personal state occurring in educational organizations from the subjective perspectives of school members and stakeholders, thereby broadening the understanding of employee workload in various settings, including educational arenas.

The purpose of this paper is to define new criteria for measuring workload smoothness of two-sided assembly lines and propose an algorithm to solve a two-sided assembly line balancing problem focusing on distributing the idle time and the workload as evenly as possible among the workstations.

This paper points out that the mean absolute deviation (MAD) and the smoothness index (SI) used to measure the workload smoothing in one-sided assembly lines are both inappropriate to evaluate the workload balance among workstations in two-sided assembly lines, as the idle time occur at the beginning and in the middle of a cycle within workstations. Then, the finish-time-based SI and MAD (FSI and FMAD) are defined, and a heuristic procedure based on the core mechanism of Moodie and Young method is proposed to smooth the assembly workload in two-sided assembly lines.

The computational results indicate that the proposed heuristic algorithm combined with the FMAD is effective in distributing the idle time and the workload among workstations as evenly as possible in two-sided assembly lines.

The two-sided assembly line balancing problem with the objective of the line efficiency can be effectively solved by the proposed approach.

The FMAD is proposed to effectively improve the workload smoothing in two-sided assembly lines.

To provide a new model to solve the assembly‐planning problem of a textile machine in a shopfloor which can help researchers and practitioners.

The assembly planning of a textile machine (repetitive manufacturing system) involves the allocation of operations to cross‐trained operators. Workflow is defined as the workloads assigned to the operators. Operators with smaller workloads are selected to be assigned new operations from the list of unscheduled operations. Three different scheduling strategies – random, shortest processing time, and longest processing time – are adopted for the selection of operations to be assigned to operators. Different combinations of these strategies are considered for the selection of both preceding and succeeding operations. A computer simulation program has been coded on an IBM/PC‐compatible system in the C++ language to study the performance of real data from the shopfloor.

The relative percentage of imbalance is adopted for evaluating the performance of these heuristics. The RL, SL and LL produced well balanced workload schedules with lesser RPI values for all operators other than heuristics.

Non‐traditional approaches like genetic algorithms can be applied to determine the robustness of the results obtained by this research.

The experiments on simulated and real data clearly indicate that the order of succeeding operations determines the balanced workflows to the assembly of operations among the operators.

The allocation of assembly operations to the operators is modeled into a parallel machine‐scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

The purpose of this paper is to examine how training specific to a given operational task, and subsequent experiential learning, can heighten skill and hence shift the level of workload at which individuals are most productively motivated.

To analyze these effects, a laboratory experiment was used involving a vehicle routing application and 156 managers exposed to a 2 × 3 complete treatment design. Both multi‐period objective in‐task data and subjective self reports are collected to tap into skill levels, actions and behavioral variables of interest.

In the absence of additional workload challenges, the paper finds that increases in skill may in fact significantly limit and in some cases actually degrade overall motivation, as well as objective performance.

Limitations potentially stem from specific operationalizations of the factors studied as well as selectivity of the subject pool and the context (vehicle routing task).

The implications of the skill‐challenge‐motivation dynamics observed have direct repercussions for existing management models in which training and experience are viewed as having strictly monotonic benefits to performance. The implications also go far to promote more informed models of worker behavior in operations modeling that otherwise view performance as static or monotonically increasing based on experience.

This is believed to be the first study that has explicitly studied the inverted‐U dynamics stemming from the interplay of both skill and workload on motivation and performance, over a multi‐period framework of analysis.

Benchmarks are the vital tools in the performance measurement and evaluation of database management systems (DBMS), including the relational database management systems (RDBMS) and the object‐oriented/object‐relational database management systems (OODBMS/ORDBMS). Standard synthetic benchmarks have been used to assess the performance of RDBMS software. Other benchmarks have been utilized to appraise the performance of OODBMS/ORDBMS products. In this paper, an analytical framework of workload characterization to extensively and expansively examine the rationale and design of the industry standard and synthetic standard benchmarks is presented. This analytical framework of workload analysis is made up of four main components: the schema analysis, the operation analysis, the control analysis, and the system analysis. These analysis results are compiled and new concepts and perspectives of benchmark design are collated. Each analysis aspect is described and each managerial implication is discussed in detail.

This paper aims to review and discuss four aspects of mixed-model assembly line balancing (MMALB) problem mainly on the optimization angle. MMALB is a non-deterministic polynomial-time hard problem which requires an effective algorithm for solution. This problem has attracted a number of research fields: manufacturing, mathematics and computer science.

This paper review 59 published research works on MMALB from indexed journal. The review includes MMALB problem varieties, optimization algorithm, objective function and constraints in the problem.

Based on research trend, this topic is still growing with the highest publication number observed in 2016 and 2017. The review indicated that the future research direction should focus on human factors and sustainable issues in the problem modeling. As the assembly cost becomes crucial, resource utilization in the assembly line should also be considered. Apart from that, the growth of new optimization algorithms is predicted to influence the MMALB optimization, which currently relies on well-established algorithms.

The originality of this paper is on the research trend in MMALB. It provides the future direction for the researchers in this field.