Job Assignment Research Articles

Comparison of Three Job Mapping Algorithms for Supercomputer Resource Managers

Performance of supercomputer depends on the quality of resource manager, one of its functions is assignment of jobs to the nodes of clusters or MPP computers. Parts of parallel programs interact with each other with different intensity, and mapping of program to supercomputer nodes influence efficiency of the run. At each program run graph representing application program is to be mapped onto graph of nodes representing a subset of computer system. The both graphs are not known beforehand, hence the mapping must be done in reasonable time while scheduling resources. Three mapping algorithms were explored: parallel versions of simulated annealing, genetic and composite algorithms. A set of experimental runs with different algorithms parameters was performed, comparison of mapping quality and runtime was made, and suggestions on applicability of algorithms for resource managers were provided.

The Nexus between School Leadership Preparation and Student Achievement

The major objective of this study is to assess the most common school leadership practices in the Dire Dawa Administrative region and their relationship with student achievement. To this end, it used a cross-sectional and correlational study design. Sample schools were selected using purposive sampling techniques and principals and vice-principals were selected using the availability sampling method. Accordingly, 16 schools and 48 principals participated in the study. Data was collected using a self-developed questionnaire, interview and document analysis. The reliability of the self-developed questionnaire was .77. Student achievement data for the three years the principal/vice principal was assigned were collected from the regional education bureau data set. The collected data was analyzed using narrative analysis, descriptive and inferential statistics. The relationship between leadership preparation and student achievement was analyzed using correlation and simple regression. The findings indicated strong relationship between leadership development and student achievement with r=0.18 at a p<0.05 level of significance. The relationship is moderate but significant at a p<0.05 level of significance and with rho=0.426. Whereas the most common leadership preparation strategy employed is experience sharing, with other strategies such as short-term training and job assignments being less common.

SARS-CoV-2 Seroprevalence Among First Responders in Northeastern Ohio, 2020.

First responders, including firefighters, emergency medical technicians (EMTs), paramedics, and law enforcement officers, are working on the front lines to fight the COVID-19 pandemic and facing an increased risk of infection. This study assessed the seroprevalence of SARS-CoV-2 infection among first responders in northeastern Ohio. A survey and immunoglobulin G antibody test against SARS-CoV-2 nucleocapsid protein were offered to University Hospitals Health System-affiliated first-responder departments during May to September 2020. The survey contained questions about demographic characteristics and history of SARS-CoV-2 infection. A total of 3080 first responders with diverse job assignments from more than 400 fire and police departments participated in the study. Of 3080 participants, 73 (2.4%) were seropositive and 26 (0.8%) had previously positive real-time polymerase chain reaction results. Asymptomatic infection accounted for 46.6% (34 of 73) of seropositivity. By occupation, rates of seropositivity were highest among administration/support staff (3.8%), followed by paramedics (3.0%), EMTs (2.6%), firefighters (2.2%), and law enforcement officers (0.8%). Work-associated exposure rates to COVID-19 patients were: paramedics (48.2%), firefighters (37.1%), EMTs (32.3%), law enforcement officers (7.7%), and administration/support staff (4.4%). Self-reported community exposure was positively correlated with self-reported work-associated exposure rate (correlation coefficient = 0.99). Neither self-reported community nor work-associated exposure was correlated with SARS-CoV-2 seroprevalence. We found no significant difference in seroprevalence among sex/gender or age groups; however, Black participants had a higher positivity rate than participants of other racial groups despite reporting lower exposure. Despite the high work-associated exposure rate to SARS-CoV-2 infection, first responders with various roles demonstrated seroprevalence no higher than their administrative/supportive colleagues, which suggests infection control measures are effective in preventing work-related infection.

Case Article—Blending Statistics, Optimization, and Simulation in a Multiphase Case

This case integrates three different methodologies over a four-part sequence on a problem that mirrors a real-world, published project. Students apply statistics, integer optimization, and simulation analysis toward solving the job assignment problem. The case features four parts: (1) operating cost and runtime estimation via regression, (2) mixed integer optimization analysis of a baseline and two scenarios, (3) simulation of the recommended solution under uncertainty, and (4) reoptimization under uncertainty. The case has been implemented over a one- to four-week period with upper level MBA or masters in analytics students. The case has received very positive feedback.

The moderating effect of burnout on professionalism, values and competence of nurses in Saudi Arabia amidst the COVID-19 pandemic: A structural equation modelling approach.

AimTo examine the moderating effect of burnout on the relationship between nurses' competence, professional competence and professional values.BackgroundThere is a preponderance of burnout studies of nurses before and during the COVID‐19 pandemic. However, little is known about burnout's moderating influence on nurses' professionalism, competence and values during the ongoing pandemic.MethodsThis study used a correlational, cross‐sectional design and convenience sampling to recruit 809 Saudi nurses employed in four government hospitals. Four self‐report instruments were used to collect data from August 2021 to March 2022. Spearman Rho, structural equation modelling and multi‐group moderation analysis were used to analyse the data.ResultsMost of the participants had high burnout. There was a moderate to high correlation between the dimensions of professional competence, nurse professional values, burnout and nurse competence. Both professional competence and nurse professional values significantly affected nurse competence. Nurse professional values also had a direct effect on professional competence. Professional competence and nurse professional values on nurse competence may vary among those with low, moderate and high levels of burnout, suggesting the moderating effect of burnout.ConclusionsThe positive impacts of nurse professional values and professional competence on nursing competence were validated in this study, as was the evidence of burnout's moderating effect on the studied connections. To avoid burnout, it is necessary to increase organizational knowledge and support for nurses, their working conditions and the environment through strategies that promote well‐being and empowerment.Implications for nursing managementNurse managers can implement policies to help nurses minimize burnout and improve their professional values and competence. Management policies may be considered include increased staffing, opportunities to improve technical abilities through education and vocational training programs, and increased diversity of job assignments.

Unrelated parallel machine scheduling problem with special controllable processing times and setups

The controllable processing times (CPTs) have many practical applications, enabling the length of the job processing to vary within an interval flexibly with additional costs. In this paper, we study an unrelated parallel machine scheduling problem with machine- and sequence-dependent setup times and a special case of CPTs without extra costs. The objective is to maximize the difference between the sum of realized state of processing of all jobs and makespan. To solve the problem, a mixed integer programming (MIP) model is formulated first and then a logic-based Benders decomposition (LBBD) method is developed, in which the master problem is for job assignments and the subproblem is furthered decomposed into a sequencing problem for minimum total setup times on each machine and a processing time determination problem. Several LBBD-based heuristics are also employed by imposing different optimality gaps for the master problem. The performance of the MIP formulation, the exact LBBD method and the LBBD-based heuristics is compared through extensive computational experiments. The results demonstrate that the exact LBBD method with the preprocessing procedure which generates initial feasible solutions and cuts is effective and the proposed LBBD-based heuristics reduce the computation time significantly at the expense of slightly solution quality reduction.

Towards industry 5.0: A multi-objective job rotation model for an inclusive workforce

The new Industry 5.0 paradigm complements the well-known Industry 4.0 approach by specifically driving research and innovation to facilitate the transition to sustainable, human-centric and resilient industry. In the manufacturing context, workers' diversity in terms of experience, productivity and physical capacity represents a significant challenge for companies, especially those characterized by high staff turnover and manual processes with high workload and poor ergonomics. In seeking to address such challenges, this research adopts a human-centric perspective to define new flexible job arrangements by developing a new multi-objective job rotation scheduling model. The proposed model is unique in that it aims to achieve multiple job assignment objectives by simultaneously considering different socio-technical factors: workers' experience, physical capacity and limitations, postural ergonomic risks, noise and vibration exposure, and workers' boredom. The model's implementation in real environments can be supported by new sensor-based technologies that collect data on workers' efficiency, ergonomic scores and task performance and enable workers to participate in measuring perceived fatigue and boredom. The primary goal of our model is to find the most appropriate assignment of job and individual-flexible rest-break plan for each worker. The authors test the model application in an industrial setting. Useful managerial insights emerge and prescriptive recommendations are provided.

Anthropometrics Impact Army Combat Fitness Test Performance in Reserve Officer Training Corps Cadets.

Fitness is a vital component in military success. The Army is in the process of implementing a new assessment for soldier fitness, the Army Combat Fitness Test (ACFT). Success on the assessment is a major factor in job assignments and promotional opportunities. This generates questions related to modifiable (i.e., fitness and body composition) and non-modifiable (i.e., limb length and testing equipment) factors impacting performance. Currently, anthropometrics differences in ACFT performance have not been investigated. Thus, this study aimed to assess the impact of anthropometrics on ACFT performance in Reserve Officer Training Corps Cadets. Anthropometric measures and ACFT scores were collected from Reserve Officer Training Corps cadets (n = 105, age: 20.4 ± 2.4 years, body mass index: 25.0 ± 2.8 kg/m2, and M/F = 84/21). All ACFT events were evaluated by certified graders. Measurement locations were based on established anthropometric assessment standards and previous research (hand, lower arm, upper arm, upper leg, lower leg, and torso lengths). The study was approved by the Auburn University Institutional Review Board (protocol code #21-410). There were weak correlations between hand length and three-repetition maximum deadlift [0.393; P ≤ .001], standing power throw [0.399; P ≤ .001], sprint-drag-carry [-0.315; P = .002], and ACFT score [0.212; P = .035]. The lower leg had weak correlations with standing power throw [0.249; P = .013], sprint-drag-carry [-0.215; P = .033], and ACFT score [0.213; P = .034]. Hand and lower leg length impacted individual event performances when comparing shortest and longest limb lengths [all P values <.05]. Hand and lower leg length significantly impact ACFT performance. These results present practical information to individuals responsible for developing protocols and scoring for the ACFT. Reassessment of events and the equipment utilized are warranted to assure that event performance is not hindered by a non-modifiable factor that is not representative of fitness or occupational demands. Future work should investigate how different trap-bar and kettlebell handle sizes, as well as medicine ball sizes, impact performance on the ACFT.

Exact and matheuristic methods for the parallel machine scheduling and location problem with delivery time and due date

The scheduling and location (ScheLoc) problem is a new and important research topic with a wide range of practical applications. It jointly optimizes machine locations, job assignments, and sequence decisions to yield a globally optimal solution. Current studies on the ScheLoc problem are limited, and most have focused on makespan minimization. This paper investigates a new variant of the parallel-machine ScheLoc problem with job delivery time and due date considerations. This is motivated by practical situations where jobs must be transported back to their original locations upon completion of processing, and a specific due date is associated with each job. The objective is to minimize the total weighted sum of location, transportation, and tardiness penalty costs incurred if a job is delivered after its due date. We propose three models adapted from classic ones for scheduling problems. We then develop an exact logic-based Benders decomposition method, which decomposes the problem into a master problem (MP) for determining machine locations and job assignments, and a subproblem (SP) for determining job sequences on each machine. The MP is augmented by dynamically adding optimality cuts generated by solving the SP. We also develop a matheuristic (MH) and its variant to achieve near-optimal solutions. An approximate model with predetermined job processing sequences on each machine is formulated and solved to determine the machine locations and job assignments. The obtained solutions are further improved by solving a series of single-machine scheduling problems. The variant combines a kernel search framework to make MH more efficient. Computational experiments on 720 randomly generated instances show the effectiveness and efficiency of both the exact and matheuristic methods.

IMPACT OF LEADERSHIP INDUCTION TRAINING ON HEADTEACHER'S MANAGEMENT SKILLS: A QUALITATIVE STUDY IN SINDH, PAKISTAN

Professional skills and experiences are acquired through different kinds of training such as inductive training which has a significant impact on the head teacher’s management skills. This type of management and professional experience was provided to newly appointed headteachers in 2017 in Sindh, Pakistan. This experience has shaped management attitudes about job skills, rules and regulations, and requirements of the job assignment according to modern lines. Recognizing its importance and significance Sindh government made training compulsory for all newly appointed headteachers of the school education and literacy department. The objectives of this research are to explore the impact of Induction Training on the head teacher's administrative management skills, how it remained helpful in bringing about desired results, and its strength and weakness in acquired experiences in professional practices. This research study is qualitative by method and descriptive in purpose. The population of the current research study was 937headteachers from Sindh and the Sample was selected from 15 well-experienced headteachers having vast knowledge about administration. The purposive Sampling technique was observed in the current study. The design of this research study is phenomenology, the lived experiences about the phenomenon. The data were collected through a semi-structured interview protocol. The thematic analysis was made, and the themes were generated. The findings of this study have emerged from the themes that learning experiences have a great impact and significance on head teachers’ management skills, likewise 1- through inductive training 2-developing inclusive and conducive environment 3- human resource and management, and 4-learning outcomes in basic education through Planning &amp; Development, Administrative Management, and academic development. This study is helpful and effective for leadership development and management of schools in Sindh, Pakistan.

NLP and ML based approach of increasing the efficiency of environmental management operations and engineering practices

Nowadays, environmental preservation is becoming the arena where technological breakthroughs and advances are implemented as the promising way for the future sustainable development of humanity. To provide quality environmental designs and effective environmental policies as their basic functions respectively, environmental engineering and management programs are also faced with acknowledgment of modern technologies in the division of their environmental affairs. This paper proposes an approach that is based on two specific directions of information technologies (natural language processing (NLP) and machine learning (ML)) to increase the efficiency of environmental management policies and engineering practices. Those two fields of informatics are critical points in developing environmental awareness or competencies of environmental managers/engineers since there are many existing occlusions to be removed in developmental path of environmental management/engineering such as less accurate performance of systems, environmentally-savvy technologies for reforms, and so on. Literature review and stakeholder analysis methods are used in reviewing management operations and engineering practices to determine how two directions of computer science help increase the efficiency of those ecological majors and propose research informed guidelines. The results show that machine learning, and natural language processing are keys to performing “minimize risk” job assignments such as predicting and awareness.

DeConNet: Deep Neural Network Model to Solve the Multi-Job Assignment Problem in the Multi-Agent System

In a multi-agent system, multi-job assignment is an optimization problem that seeks to minimize total cost. This can be generalized as a complex problem in which several variations of vehicle routing problems are combined, and as an NP-hard problem. The parameters considered include the number of agents and jobs, the loading capacity, the speed of the agents, and the sequence of consecutive positions of jobs. In this study, a deep neural network (DNN) model was developed to solve the job assignment problem in a constant time regardless of the state of the parameters. To generate a large training dataset for the DNN, the planning domain definition language (PDDL) was used to describe the problem, and the optimal solution that was obtained using the PDDL solver was preprocessed into a sample of the dataset. A DNN was constructed by concatenating the fully-connected layers. The assignment solution obtained via DNN inference increased the average traveling time by up to 13% compared with the ground cost. As compared with the ground cost, which required hundreds of seconds, the DNN execution time was constant at approximately 20 ms regardless of the number of agents and jobs.

Less Competition, More Meritocracy?

Uncompetitive contests for grades, promotions, retention, and job assignments, which feature lax standards and limited candidate pools, are often criticized for being unmeritocratic. We show that, when contestants are strategic, lax standards and exclusivity can make selection more meritocratic. When many contestants compete for a few promotions, strategic contestants adopt high-risk strategies. Risk-taking reduces the correlation between performance and ability. Through reducing the effects of risk-taking, “Peter-Principle” promotion policies, which entail promoting some contestants that are unlikely to be worthy, can increase the overall correlation between selection and ability, and thus further the goal of meritocratic selection.

Female Firefighter Work-Related Injuries in the United States and Canada: An Overview of Survey Responses.

ObjectivesThis study explored how demographic characteristics, life experiences, and firefighting experiences have an impact on work-related injuries among female firefighters, and described events surrounding such work-related injuries.MethodsThis online survey was available from June 2019 to July 2020. Questions related to demographic characteristics, life experiences, firefighting experiences, and work-related injuries. Descriptive analyses characterized variables by the presence or absence of work-related injury, injury severity, job assignment, and country of residence.ResultsThere were 1,160 active female firefighter survey respondents from the US and Canada, 64% of whom reported having at least one work-related injury. US respondents made up 67% of the total but 75% of the injured sample. Injured respondents were older, had been in the fire service longer, and had a greater number of fires and toxic exposures than non-injured respondents. Heavier weight, tobacco use, and alcohol consumption were more common among injured respondents. The two most common contributing factors to work-related injuries were human error and firefighter fatigue. Among respondents who reported an injury-related time loss claim, 69% were wearing protective equipment when injured, and 9% of the injuries directly resulted in new policy implementation.ConclusionsThese findings can help inform resource allocation, and development of new policies and safety protocols, to reduce the number of work-related injuries among female firefighters.

Human resource management practices in senior high schools: An account of those who matter

The study reports human resource management practices in senior high schools. Specifically, the study sought to determine the existing human resource management practices and to examine the perception of teachers on human resource management practices. Descriptive survey and quantitative research approach were adopted in this study. A sample of 90 teachers and 30 management staff were selected using simple random and census sampling techniques respectively. Two sets of questionnaires were used in gathering data. Descriptive statistics (mean, standard deviations and frequency count) were the statistical tools that were used to analyse the data. The study revealed that job instruction and promotions were highly practised in the schools. However, seminars, coaching, job assignments, fringe benefits, job security, conducive working environment, recognition, participation and autonomy were not prioritised as human resource practices in the schools. It was also found that a majority of teachers had low perception of the human resource management practices. It is recommended that management of the senior high schools be given the appropriate training on effective implementation of human resource management practices so that they can positively influence the performance of employees and improve their perception on these practices.

Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network

The fifth-generation (5G) mobile network services have made tremendous growth in the Internet of Things (IoT) network. A counters number of battery-powered IoT devices are deployed to serve diverse scenarios, e.g., smart cities, autonomous farming, smart manufacturing, to name but a few. In this context, energy consumption became one of the most critical concerns in interconnecting smart IoT devices in such scenarios. Additionally, whenever these IoT devices are distributed in space and time-evolving, they are expected to deliver high volume data scalably/predictably while minimizing end-to-end latency. Furthermore, edge IoT nodes often face the biggest hurdle of performing optimal resource distribution and achieving high-performance levels while coping with the variability of task handling, energy conservation, and ultra-reliable low-latency.This paper investigates an energy-aware and low-latency oriented computing task scheduling problem in a Software-Defined Fog-IoT Network. First, we formulate the online task assignment and scheduling problem as an energy-constrained Deep Q-Learning process as a kickoff. The latter strives to minimize the network latency while ensuring energy efficiency by saving battery power under the constraints of application dependence. Then, given the task arrival process, we introduce a deep reinforcement learning (DRL) approach for dynamic task scheduling and assignment in the Software-Defined Networking (SDN)-enabled edge networks. We conducted comprehensive experiments and compared the introduced algorithm to three pioneering deep learning algorithms (i.e., deterministic, random, and A3C agents). Extensive simulation results demonstrated that our proposed solution outperforms these algorithms. Additionally, we highlight the characterizing feature of our design, energy-awareness, as it offers better energy-saving by up to 87% compared against the other approaches. We have shown that the offloading scheme could perform more task assignments with the available battery power by up to 50% less time delay. Our results back our claims that the solution we propose can readily be used to dynamically optimize task scheduling and assignment of complex jobs with task dependencies in distributed Fog IoT networks.

A dynamic sequential decision-making model on MRI real-time scheduling with simulation-based optimization.

Magnetic resonance imaging (MRI) is widely used in diagnostic medicine and contributes significantly to US health care spending. Scheduling MRI jobs involves uncertainties (e.g., patient arrival time, scanning time, and preparation time) that can lead to excessive delays and high costs in MRI operations. This study addresses real-time decision making in use of MRI scanners based on job assignment and sequencing decisions that override the appointment schedule. The decisions are made using real-time information of the waiting patients, the utilization status of the MRI scanners, and the partially revealed uncertainties of scanning times of current patients. A sequential decision-making framework and a simulation-based solution method are proposed to utilize massive real-time information and match the use of MRI rescheduling in practice. The results are then compared with a real case in a large midwestern academic medical center in the US. This study illustrates that the proposed method reduces patient waiting time by 21.7% and improves utilization of MRI scanners by 23.0%. An optimality gap of 13.6% is provided when compared to off line scheduling methods based on a mixed integer programming (MIP) model. The number of simulation replications in this approach uses the ranking and selection method, which not only reduces solution time, but also provides solution quality guarantees wherein the probability of errors in the proposed method for one day is less than 0.1%. In 100 randomly generated workday experiments, all of the scheduling decisions given by the proposed method perform better than current policy, with an average reduction of 17.93 minutes in each patient's waiting time and an improvement of scanner utilization by 7.20%.

Psychometric evaluation of the perceived perioperative competence scale-revised among the Chinese operating room nurses: a methodological research

BackgroundPerioperative competence is necessary to evaluate operating room nurses. The Perceived Perioperative Competence Scale-Revised (PPCS-R) is the only available tool developed specifically for the perioperative setting. However, there is a lack of research on the reliability and validity of this scale among Chinese nurses. Thus, the aim of this study is to translate, culturally adapt, and evaluate the psychometric properties of the Perioperative competence Scale-Revised (PPCS-R) among operating room nurses in China.MethodsInstrument cultural adaptation was carried out through forward translation, back translation, expert panel evaluation and pretesting. The psychometric properties (content validation, item analysis, construct validation, and reliability coefficient) of the Chinese PPCS-R (C-PPCS-R) were examined. An online survey was completed from June to August 2020 by operating room nurses (N = 480) in five third-grade class-A hospitals in Beijing.ResultsThe item analysis identified six items for scale reduction. Exploratory factor analysis showed the remaining 34 items loaded on six factors, which were named in accordance with the original scale. The six-factor model showed a good fit through confirmatory factor analysis. The item content validity index for the C-PPCS-R items ranged from 0.857 to 1.000, and that for the total scale was 0.875. Cronbach’s alpha was showed 0.787. Together, the six factors explained 68.62% of the variance.ConclusionsThe 34-item C-PPCS-R showed good validity and reliability to measure perceived competence among operating room nurses in the Chinese context. The scale can assist nurse managers to identify operating room nurses’ perceived competence, and provides evaluation criteria for career planning, performance appraisal, job assignment, and continuing education.

Incarcerated workers: overlooked as essential workers

ObjectiveTo use the example of COVID-19 vaccine prioritization for incarcerated workers to call attention to the need to prioritize incarcerated workers’ health.MethodsFrom November to December 2020, we searched publicly available information (e.g. Department Of Corrections websites and press releases) for 53 US prison systems, including all states, Immigration and Customs Enforcement, the Federal Bureau of Prisons, and Puerto Rico. Coders reviewed if states had prison labor policies, if states had COVID-19 specific prison labor policies, the location of work, industries both pre- and during the COVID-19 pandemic, the scope of work, and hourly wage. Findings were compared to the Centers for Disease Control and Prevention’s occupational vaccine prioritization recommendations.ResultsEvery facility has incarcerated individuals working in some capacity with some resuming prison labor operations to pre-pandemic levels. All but one prison system has off-site work locations for their incarcerated population and many incarcerated workers have resumed their off-site work release assignments. Additionally, every state has incarcerated workers whose job assignments are considered frontline essential workers (e.g. firefighters). In at least five states, incarcerated workers are participating in frontline health roles that put them at higher risk of acquiring COVID-19. Yet, no state followed the Centers for Disease Control and Prevention recommended vaccination plan for its incarcerated population given their incarcerated workers’ essential worker status.ConclusionThe Centers for Disease Control and Prevention recommended that incarcerated people be prioritized for vaccination primarily due to the risk present in congregate style prison and jail facilities. Furthermore, our review found that many incarcerated people perform labor that should be considered “essential”, which provides another reason why they should have been among the first in line for COVID-19 vaccine allocation. These findings also highlight the need for incarcerated workers’ health to be prioritized beyond COVID-19.

A c/μ‐Rule for Job Assignment in Heterogeneous Group‐Server Queues

We study a dynamic job assignment problem in queueing systems with one class of Poisson arrivals and K groups of heterogeneous servers. A scheduling policy prescribes the job assignment among servers in each group at every state n (number of jobs in the system). Our goal is to obtain the optimal policy to minimize the long‐run average cost, which involves the increasingly convex holding cost for jobs and the operating cost for working servers. This problem has wide application scenarios in operations management, such as job scheduling in manufacturing systems, packet routing in communication systems, and staffing in service systems. We prove that the optimal policy has monotone structures and quasi bang–bang control forms. Specifically, we discover that the optimal policy is governed by the marginal cost rate c − μG( n), where c is the operating cost rate, μ is the service rate, and G( n) is called the perturbation realization factor at state n. Under the condition of scale economies which can be guaranteed by any increasingly concave operating cost in μ, we prove that the optimal policy obeys a so‐called c/ μ‐ rule: Servers with a smaller c/ μ should be occupied by jobs with higher priority. Optimality of multi‐threshold type policies is further proved when the c/ μ‐rule is applied. Our c/ μ‐rule in group‐server queues can be viewed as a counterpart of the famous cμ‐rule in polling queues, which both significantly reduce the complexity of optimization problems. By utilizing these optimality structures, we also develop computational‐efficient algorithms to determine the optimal policy numerically. Simulation experiments demonstrate the good scalability and robustness of the c/ μ‐rule, which are important for managerial practice.