Parallel-Channel, Dual-Resource Constrained Queueing Systems with Heterogeneous Resources

Worker flexibility in a parallel dual resource constrained job shop

“…the real processing times will not decrease with the accumulation 458 H. Yue et al of worker experience.). An 85% utilization is consistent with literature, as well as with industrial practice (Hogg et al 1977, andMalhotra et al 1993). …”

“…Though the extent of the differences in performance between various cross-training policies might be affected by a lower level of worker utilization, the rankings of these policies will not be affected according to the previous literature (Hogg et al 1977). Note that the operating rules in our study are chosen independently from worker utilization.…”

The Impact of Learning and Labor Attrition on Worker Flexibility in Dual Resource Constrained Job Shops

“…The processing times and the inter-arrival time of jobs are generated from exponential distributions to yield worker utilization levels of approximately 85 percent. Other studies on worker flexibility in DRC environments [9] [13] have also used labor utilization rates between 75 percent and 95 percent. The routing pattern for the jobs is random, with each job visiting all six departments.…”

“…Although it has been established that worker flexibility provides benefits under a variety of different shop designs, the cost of acquiring this flexibility has only been studied in a limited fashion. The criteria most commonly used in the DRC literature to assess the cost of worker flexibility is the total number of worker transfers [6] [17], server transfer delays [7], and heterogeneous efficiencies of the transferred workers [3] [9] [15] [16] [20] [22] [24]. However, the cost of worker flexibility is not restricted to heterogeneous efficiencies, the number of transfers, and server transfer delays only.…”

The impact of the amount of work in process on the use of cross-training

“…There are studies in which workers are modelled in such a way that they are skilled for and most efficient in their 'own' or 'home' department and are also skilled for a second or even third department in which they are less efficient (see e.g. Nelson 1970, Hogg et al 1977, Bobrowski and Park 1993, Malhotra and Kher 1994, Yang 2007, Davis et al 2009). When this is combined with a where-rule that assigns workers to their most efficient department, this resembles the modelling of worker preferences.…”