Cross-trained Workers Research Articles

Recruitment, Retention, and Future Direction for a Heart Health Education and Risk Reduction Intervention Led by Community Health Workers in an African American Majority City

Heart disease is a leading cause of death for African Americans. A community-academic partnership cross-trained community health workers to engage African American adults in a 6-month heart health education and risk reduction intervention. We conducted a one-group feasibility study using a one group (pre-posttest) design. A total of 100 adults were recruited from 27 zip codes in an African American majority city through community-based organizations (46%), churches (36%), and home visits (12%). Ninety-six percent were African American; 55% were female, 39% were male, and 6% were transgender. Their mean age was 44.6 years (SD = 15.9). Ninety-two percent had health insurance. Seventy-six percent of participants averaged blood pressure (BP) readings > 130/80 mmHg. Eleven percent of participants had a 30% or higher probability of developing cardiovascular disease in the next 10 years. Six-month follow-up was completed with 96% of participants. There were statistically significant increases in knowledge and in perception of personal risk for heart disease. However, slightly more participants (n = 77, 80.2%) had BP > 130/80 mmHg. The Community Advisory Group recommended expanding the intervention to 12 months and incorporating telehealth with home BP monitoring. Limited intervention duration did not meet longer term objectives such as better control of high BP and sharing risk reduction planning with primary care providers.

Multi-skilled worker assignment in seru production system for the trade-off between production efficiency and workload fairness

PurposeThe paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS).Design/methodology/approachThree approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem.FindingsNumerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers.Practical implicationsSPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS.Originality/valueThe study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed.

Optimal cross-trained worker assignment for a hybrid seru production system to minimize makespan and workload imbalance

As a worker-centred assembly mode developed in the electronics industry in Japan, seru is receiving growing attention due to improved flexibility and responsiveness. In seru implementation, cross-trained worker assignment is a vital problem. Most previous studies focused on assigning cross-trained workers into pure divisional or rotating seru separately, but overlooked the problem for a hybrid seru production system that includes both seru types. This research fills this gap by minimizing the makespan and balancing the workers’ workload of each seru in a bi-objective mathematical model. For medium-scale instances, the exact solutions are obtained. For large-scale instances, we propose an NSGA-II-based memetic algorithm that uses two-level encoding and incorporates the bat algorithm as a local search and two K-means-based NSGA-II algorithms. The experimental results illustrate that the K-means-based NSGA-II not only outperforms other algorithms with respect to common proximity and diversity metrics but also runs an order of magnitude faster (in seconds versus minutes required by others on the same computer). Some management insights are obtained based on many numerical experiments.

Simulation-based optimisation for worker cross-training

Worker cross-training is a problem arising in many companies that involve human work. To perform certain activities, workers are required to possess certain skills. Cross-trained workers possess ev...

Service level improvement due to worker cross training with stochastic worker absence

To react on increasing customer demand uncertainty, production systems have to be flexible concerning the provided capacity. With respect to labour, one opportunity to gain such flexibility is to assign workers to different work stations which often require different skills to be operated. Therefore, cross-trained workers are needed to enable this flexibility. Since the qualifying workforce implies costs, a relevant problem is how much skills and what mix of skills is optimal for a production system. In addition, the workforce may be on vacation or have a sick leave and hence is not always available. In this paper, we study the effect of different predefined workforce qualification profiles for a streamlined production system with simulation and compare the results with simulation-based optimisation using a genetic algorithm. Specifically the effect of stochastic worker absence, in comparison to workers being always available, is evaluated for different production system scenarios. The results show that cross-trained workers can significantly improve the service level achieved and that simulation-based optimisation can provide a much better worker specific mix of skills than predefined qualification profiles. Another managerial insight is that there is a trade-off between number of skills and number of workers needed to obtain the same service level.

Line Balancing and Layout Model for Productivity Improvement in Leather Footwear Industry

The government of Ethiopia considers the leather footwear industry as one of the priority sector that is capable of accelerating economic development by creating more employment opportunities. However, the industry’s contribution to the national economy so far is not enjoyable as productivity of the industry is much lower qualitatively, quantitatively and value-wise. The focal constraint against performance of the industry is the existence of bottleneck process in the production line. The major ones are pile up of ‘UPPER’ (i.e. upper part of a shoe) at some points, because of unequal workload distribution among workstations. Accordingly, the main objective of this study is to improve the production line efficiency through line balancing technique that is demonstrated by taking Peacock shoe factory as a case study. The study considered ‘Bades shoe model’ to investigate the production line in the Stitching department. Data were collected through direct data intake from the shop-floor activities and company’s database. A well-prepared templates and stopwatch were used for the data collection. The analysis was carried out through the logic of modular system. This system uses work sharing method in which cross-trained workers perform multiple tasks to eliminate bottleneck processes and balance workloads among workstations. Furthermore, the study proposed a layout model to balance the production line. The results suggest that the production line efficiency is improved from 68.89% to 87.6%, and the labor productivity is increased from 16.67 to 23.44. The findings provide important insights into productivity improvements by creating smooth flow of components in assembly lines. Keywords: Ethiopia, leather footwear, bottleneck process, line balancing. DOI: 10.7176/IEL/10-2-03 Publication date: March 31 st 2020

Management of overlapped cross-training: with or without a supervisor?

This paper discusses a short-term or periodic optimal workforce agility management decision for a serial manufacturing cell line. We consider a manufacturing cell under an uncertain business environment with product variability. During some planning period, there is a change in product type such as the introduction of a new product or model change of an old product. As a result of the change, some worker of the production cell becomes bottleneck which may cause congestion of products flow in the upstream and idling in the downstream. To solve the bottleneck problem, strategies such as using cross-trained workers or supervisor are considered. The production cell is then staffed by cross-trained workers who are partially cross-trained (e.g. overlapped cross-trained) and/or a supervisor who is full cross-trained. These strategies are modelled and analysed under different situations. Finally, we give optimal decisions for the management of workforce agility through the analysis of the comparative statics.

Management of overlapped cross-training: with or without a supervisor?

This paper discusses a short-term or periodic optimal workforce agility management decision for a serial manufacturing cell line. We consider a manufacturing cell under an uncertain business environment with product variability. During some planning period, there is a change in product type such as the introduction of a new product or model change of an old product. As a result of the change, some worker of the production cell becomes bottleneck which may cause congestion of products flow in the upstream and idling in the downstream. To solve the bottleneck problem, strategies such as using cross-trained workers or supervisor are considered. The production cell is then staffed by cross-trained workers who are partially cross-trained (e.g. overlapped cross-trained) and/or a supervisor who is full cross-trained. These strategies are modelled and analysed under different situations. Finally, we give optimal decisions for the management of workforce agility through the analysis of the comparative statics.

The effect of buffers and work sharing on makespan improvement of small batches in assembly lines under learning effects

The effect of workers’ learning curves on the production rate in manual assembly lines is significant when producing relatively small batches of different products. This research studies this effect and suggests applying a work-sharing mechanism among the workers to improve the makespan (time to complete the batch). The proposed mechanism suggests that adjacent cross-trained workers will help each other in order to reduce idle times caused by blockage and starvation. The effect of work sharing and buffers on the makespan is studied and compared with a baseline situation, where the line does not contain any buffers and work sharing is not applied. Several linear programming and mixed-integer linear programming formulations for makespan minimization are presented. These formulations provide optimal work allocations to stations and optimal parameters of the work-sharing mechanism. A numerical study is conducted to examine the effect of buffers and work sharing on the makespan reduction in different environment settings. Numerical results are given along with some recommendations regarding the system design and operation.

A bicriterion algorithm for the allocation of cross-trained workers based on operational and human resource objectives

The problem of allocating a pool of cross-trained workers across multiple departments, units, or work centers is important for both manufacturing and service environments. Within the context of services, such as hospital nursing, the problem has commonly been formulated as a nonlinear assignment problem with an operationally-oriented objective function that focuses on the maximization of service utility as a function of deviations from target staffing levels in the departments. However, service managers might also deem it appropriate to consider human resource-oriented goals, such as accommodating worker preferences, avoiding decay of skill productivity, or the provision of training. We present a bicriterion formulation of the nonlinear worker assignment problem that incorporates both operational and human resource objective criteria. An algorithm for generating the entire Pareto efficient set associated with the bicriterion model is subsequently presented. A small numerical example is used to illustrate the bicriterion model and algorithm. A second example based on a test problem from the literature is also contained in the paper, and a third example is provided in an online supplement. In addition, a simulation experiment was conducted to evaluate the sensitivity of the algorithm to a variety of environmental characteristics.

Cross-trained workers scheduling for field service using improved NSGA-II

The proper balancing of geographically distributed task schedules and the associated workforce distributions are critical determinants of productivity in any people-centric production environment. The paper has investigated the cross-trained workers scheduling problem considering the qualified personal allocation and temporally cooperation of engineers simultaneously. A 0–1 programming model is developed and the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to deal with the NP-hard problem. In order to enforce the NSGA-II, significant improvements are made to function the approach in a more efficient way. It is observed that the improved NSGA-II outperforms the original NSGA-II in the experimental test. The promising outcomes of the formulation in the experiment make its implementation easily customisable and transferable for solving other intricate problems in the context of skilled workforce scheduling. Furthermore, the modified NSGA II can be used as an efficient and effective tool for other multiobjective optimisation problems.

Organizing knowledge workforce for specified iterative software development tasks

Organizing knowledge workers for specific tasks in a software development process is critical for the success of software projects. Assigning workforce in software projects represents a dynamic and complex problem that concerns the utilization of cross-trained knowledge workers who possess different productivities and error tendencies in coding and defect correction. This complexity is further compounded when the development process follows a software release life cycle and involves major releases of alpha, beta, and final versions in the context of iterative software development. We study this knowledge workforce problem from three essential project management perspectives: (1) timeliness — obtaining shortest development time; (2) effectiveness — satisfying budget constraint; and (3) efficiency — achieving high workforce utilization. We explore ideal workforce composites with two strategic focuses on productivity and quality and with different scenarios of workload ratios. An analytical model is formulated and a meta-heuristic approach based on particle swarm optimization is used to derive solutions in a simulation experiment. Our findings suggest that forming an ideal workforce composite is a non-trivial task and task assignments with divergent focuses for software projects under different workload scenarios require different planning strategies. Practical implications are drawn from our findings to provide insight on effectively planning workforce for software projects with specific goals and considerations.

Sample Path Optimal Policies for Serial Lines with Flexible Workers

We study the dynamic assignment of cross-trained workers in serial production lines characterized by stochastic process times and inventory buffers between stations. Throughput maximization is the objective. Each worker is trained for a subset of tasks, where emphasis is placed on systems with each worker trained for a zone of stations with stations near the zone boundaries being served (shared) by one or more other workers as well. Using sample path comparisons, we identify structural properties of optimal worker allocation policies. We identify when (i) a worker can prioritize the job in the most downstream station (last-buffer-first-served), and (ii) only the downstream (as opposed to upstream) server should serve a single task.

Sample Path Optimal Policies for Serial Lines with Flexible Workers

We study the dynamic assignment of cross-trained workers in serial production lines characterized by stochastic process times and inventory buffers between stations. Throughput maximization is the objective. Each worker is trained for a subset of tasks, where emphasis is placed on systems with each worker trained for a zone of stations with stations near the zone boundaries being served (shared) by one or more other workers as well. Using sample path comparisons, we identify structural properties of optimal worker allocation policies. We identify when (i) a worker can prioritize the job in the most downstream station (last-buffer-first-served), and (ii) only the downstream (as opposed to upstream) server should serve a single task.

Sequencing mixed-model assembly lines to minimise the number of work overload situations

The mixed-model sequencing problem is to sequence different product models launched down an assembly line, so that work overload at the stations induced by direct succession of multiple labour-intensive models is avoided. As a concept of clearing overload situations, especially applied by Western automobile producers, a team of cross-trained utility workers stands by to support the regular workforce. Existing research assumes that regular and utility workers assemble side-by-side in an overload situation, so that the processing speed is doubled and the workpiece can be finished inside a station's boundaries. However, in many real-world assembly lines the application of utility workers is organised completely differently. Whenever it is foreseeable that a work overload will occur in a production cycle, a utility worker takes over to exclusively execute work, whereas the regular worker omits the respective cycle and starts processing the successive workpiece as soon as possible. This study investigates this more realistic sequencing problem and presents a binary linear program along with a complexity proof. Different exact and heuristic solution procedures are then introduced and tested. Additional experiments show that the new model is preferable from an economic point of view whenever utility work causes considerable setup activities, for example walking to the respective station.

Cross-training performance in flexible labor scheduling environments

Cross-training effectively pools multiple demand streams, improving service levels and, when demand streams are negatively correlated, boosting productivity. When services operate for extended hours, however, those benefits are intermittent because employees take their skills home with them at the end of their shift. This study explores how cross-training and workforce management decisions interact to affect labor costs and service levels in extended hour service operations with uncertain demand and employee attendance. Using a two-stage stochastic model, we first optimally staff, cross-train, schedule, and allocate workers across departments. We then simulate demand and attendance and, as needed, re-allocate available cross-trained workers to best satisfy realized demand. Comparing the performance of full- and partial cross-training policies with that of dedicated specialists, we found that cross-training often, but not always, dominated the performance of a specialized workforce. When cross-trained workers are less proficient than specialists, however, increased cross-training forced tradeoffs between workforce size and capacity shortages. However, both workforce size and service levels often improved with increased scheduling flexibility. Further, increased scheduling flexibility appears to be an efficient strategy for mitigating the effects of absenteeism. Thus, scheduling flexibility may be an important cofactor for exploiting the benefits of cross-training in labor scheduling environments.

A two-stage stochastic program for scheduling and allocating cross-trained workers

A two-stage stochastic program is developed for scheduling and allocating cross-trained workers in a multi-department service environment with random demands. The first stage corresponds to scheduling days-off over a time horizon such as a week or month. The second stage is the recourse action that deals with allocating available workers at the beginning of a day to accommodate realized demands. After the general two-stage model is formulated, a special case is introduced for computational testing. The testing helps quantify the value of cross-training as a function of problem characteristics. Results show that cross-training can be more valuable than perfect information, especially when demand uncertainty is high.

A Constraint Programming Approach for Solving a Queueing Design and Control Problem

A facility with frontroom and backroom operations has the option of hiring specialized or cross-trained workers. Cross-trained workers can be switched between the two rooms depending on demand but are more expensive than specialized ones. Assuming stochastic customer arrival and service times, we seek a smallest-cost combination of cross-trained and specialized workers, together with a policy for switching the cross-trained workers between the rooms, which satisfies constraints on the expected customer waiting time and expected number of workers in the back room. A constraint programming approach using logic-based Benders' decomposition is presented. Experimental results demonstrate the strong performance of this approach across a wide variety of problem parameters. This paper provides one of the first links between queueing optimization problems and constraint programming.

Optimal assignment and scheduling of workers and tasks on an assembly line

In this paper the assignment of cross-trained and temporary workers to tasks on an assembly line is investigated. Cross-trained workers are skilled to perform more than one task on the assembly line in the production process. Temporary workers are viewed as either trained or untrained and may be hired or laid off as required. The solution procedure may be divided into three parts. During the first part a model is formulated to determine an optimal assignment of the workers to the production tasks. During the second part the model is extended to determine the effect of the assignment of both trained and untrained temporary workers to the tasks on the assembly line. During the final part of the model an optimal sequence of tasks in the assembly line is determined that minimises the resulting execution times of these tasks. During the first part the objective is to maximise the total production utility. This is achieved by implementing a two-phase model. The first phase maximises the utility of pro-duction by minimising labour shortage in the assembly line. During the second phase the improvement of the workers’ levels of skill is maximised while the effect of the learning and forgetting of skills is taken into consideration. A learn-forget-curve model (LFCM) is implemented to model the effect of this human characteristic on the master model. This approach ensures that the advantageous cross-trained nature of the workers is maintained and optimized, without a large deviation from the solution determined by the first phase. The objective of the second part is to minimise the labour cost of production by determin-ing the best type of workers for a certain task as well as the manner in which they should be hired or laid off. A worker is classified as either permanently or temporarily employed. Tem-porarily employed workers are further classified as either untrained or cross-trained workers. The assignment of workers to tasks on the assembly line is achieved by means of a Master Production Scheduling (MPS) model. The MPS has as its objective the minimisation of the total labour cost of performing all the tasks. The labour cost is defined as the sum of the temporary workers’ daily wages, the overtime cost of permanent workers, the overtime cost of temporary workers and the cost of employing and laying off temporary workers. Finally, during the third part an optimal sequence of tasks is determined in the production process in order to minimise the total production time. This is achieved by means of a two-phase dynamic assembly line balancing model, which is adjusted to incorporate the critical path method. During the first phase, an optimal task sequence is determined, while during the second phase, an optimal assignment of tasks to workstations and the timing thereof, is determined. The practical applicability of the model is demonstrated by means of a real life case study. The production of various styles of shoes in a leatherworks factory is considered. The production of each style requires a different set of tasks and each task requires a different level of skill. The factory under consideration employs both cross-trained and temporary workers and data sets were obtained empirically by observation, interviews and questionnaires. Upon execution of the first phase of the assignment model, an optimal utility is found and the second phase is able to maximise the increase of the workers’ skill level without deviation from this optimum. Upon execution of the employment model, it is found that labour costs are minimized by increasing the use of temporary workers and by assigning the maximum allowable number of overtime hours to them. Upon application of the scheduling model, an improved time is obtained compared to the standard execution time of each style. The results obtained from the case study indicate that the application of the model presented in this paper shows a substantial improvement in production, while reducing the cost of labour as well as improving the overall level of workers’ skills. A multi-objective model is thus developed which successfully maximises production utility, maximises skill development of workers, minimises labour costs and the occurrence of idle workers as well as minimises total execution time.

Cross-training workers in Dual Resource Constrained systems with heterogeneous processing times

In this paper, we explore the effect of cross-training workers in Dual Resource Constrained (DRC) systems with machines having different mean processing times. By means of queuing and simulation analysis, we show that the detrimental effects of pooling (cross-training) previously found in single resource constrained (SRC) heterogeneous systems are also apparent in DRC heterogeneous systems. Fully cross-training workers in DRC heterogeneous systems is only beneficial if the differences between mean processing times are not too large, otherwise cross-training should be pursued within homogeneous subgroups of machines. Due to the limited machine availability, DRC systems are unable to use some of the potential assignment flexibility from cross-trained workers (pooled queues) that can be used in SRC systems. However, it appears that this restriction in the DRC system may even improve the system mean flow (waiting) time performance compared to the SRC system for relatively large differences in processing time. Finally, in fully flexible multiple server queuing systems, restricting the assignment flexibility by applying a decentral when-rule (i.e. a commonly used labour assignment rule in practice and research) instead of a central when-rule also seems to improve the mean flow time performance under processing time differences.