Workforce scheduling: A new model incorporating human factors

Othman, Mohammed; Gouw, Gerard J.; Bhuiyan, Nadia

doi:10.3926/jiem.451

Cited by 23 publications

(15 citation statements)

References 34 publications

(12 reference statements)

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“…Following Jaber and Neumann (2010), Othman et al (2012) developed a workforce scheduling model including human aspects such as skill level, training, personalities, and worker fatigue and recovery. Considering four different objective functions to minimize the cost, firing the high performance workers, fatigue, and idle time, they have created a multi-objective integer programming model for staffing decisions in a heterogeneous workforce in which workers have different personality and skill levels.…”

Section: Drc Systems With Fatigue and Recoverymentioning

confidence: 99%

The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems

Sadeghigivi¹

2021

Preprint

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Dual-Resource Constrained (DRC) systems consist of two resources: workers and machines (stations). DRCs have become common in manufacturing and service firms that emphasise flexibility, where workers perform different tasks. Although having a flexible workforce is beneficial, it comes at a cost. When workers alternate between different jobs the productivity of the system is affected. On one hand the system becomes more responsive to changes (internal/external), and on the other hand worker productivity and system throughput deteriorate because of the loss of knowledge and workers’ fatigue. This subjects workers to conflicting phenomena. When workers are performing a task they are learning but also accumulating fatigue, which may result in error or injury. When transferred to another task, or on a break, workers may forget what they have learnt but at the same time recover from fatigue, either fully or partially. In particular, forgetting and fatigue are interesting to be considered as they directly affect the quality of products. This research investigates the effects of workers’ learning-forgetting and fatigue-recovery on DRC systems. First, it modifies a known learning-forgetting model by accounting for fatigue and recovery. Second, it assumes that the quality of a production process may deteriorate and generate defective items that require rework. Third, a human error model is developed that considers human learning-forgetting and fatigue-recovery in producing defective items. Fourth, a comprehensive model is developed that integrates learning, forgetting, fatigue, and recovery into a DRC system with quality consideration. This model is investigated for different transfer and flexibility policies. Numerical results provide insights and guidelines that may help operations managers with decisions on how to improve a system’s performance and throughput, while considering worker welfare. Results indicate that it is important to consider workers capabilities and limitations when designing manufacturing systems. They also suggest that ignoring human restrictions and abilities results in unrealistic production planning and erroneous cost estimation.

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Section: Drc Systems With Fatigue and Recoverymentioning

confidence: 99%

The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems

Sadeghigivi¹

2021

Preprint

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“…For example, Agralı, Taşkın and Ünal [3] introduced an employee scheduling model in service industry, in which the flexible employ availability is considered in terms of different contract forms and employee rostering is also incorporated. In addition to the settings of different skills and flexibility, Othman et al [17] designed a multi-objective mixed integer programming model for workforce scheduling incorporating multiple human aspects, such as skills, training, fatigue, and recovery, into consideration, and identified that worker differences consideration in workforce scheduling can help to generate realistic plans with minimum costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations

2020

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The order assignment in the food delivery industry is of high complexity due to the uneven distribution of order requirements and the large-scale optimization of workforce resources. The delivery performance of employees varies in different conditions, which further exacerbates the difficulty of order assignment optimization. In this research, a non-linear multi-objective optimization model is proposed with human factor considerations in terms of both deteriorating effect and learning effect, in order to acquire the optimal solutions in practice. The objectives comprised the minimization of the operational cost in multiple periods and the workload balancing among multiple employees. The proposed model is further transformed to a standardized mixed-integer linear model by the exploitation of linearization procedures and normalization operations. Numerical experiments show that the proposed model can be easily solved using commercial optimization softwares. The results indicate that the variance of employee performance can affect the entire delivery performance, and significant improvement of workload balancing can be achieved at the price of slight increase of the operational cost. The proposed model can facilitate the decision-making process of order assignment and workforce scheduling in the food delivery industry. Moreover, it can provide managerial insights for other labor-intensive service-oriented industries.

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“…The research addressed the influence of different level of staff's knowledge for product development and processing time based on their specialized backgrounds, expertise and responsibilities [5,6]. The paper considered that staffs' skills, training, workers' personalities, staffs' break and staffs' fatigue and recovery levels could influence machining process which included staffs' knowledge level [7]. In the aerospace industry ''Human Factors'' had become synonymous with crew resource management and ergonomics, the influence of human factors on operational efficiency was addressed [8].…”

Section: Introductionmentioning

confidence: 99%

A study of job shop standard setup time quota

Song

Shen

2015

Prod. Eng. Res. Devel.

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For the multi-item and small lot size production mode and single machine job shop scheduling sequence independent setup time, many setup times are difficult to estimate accurately, which influences the ability to achieve accurate production cycles and costs of products. The survey shows that the length of the setup time depends on the level of employee's knowledge. Therefore, a method for determining the standard setup time quota based on the level of employees' knowledge is proposed. First, an evaluation index system for the level of employee's knowledge is built; the level of employee's knowledge is estimated by the masses and experts fuzzy comprehension evaluation and entropy method. Second, the range and definition of the level of employees' knowledge index are developed. Third, the relational model of the employee's knowledge level and the level of employees' knowledge index is constructed through the least squares method. Finally, an example application is presented to verify the feasibility and effectiveness of the proposed model.

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Workforce scheduling: A new model incorporating human factors

Cited by 23 publications

References 34 publications

The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems

The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems

Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations

A study of job shop standard setup time quota

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