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

Abstract

International audienceIn 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

Agent-based modelling of movement rules in DRC systems for volume flexibility: human factors and technical performance

“…The most common methods used to model DRC systems and assembly lines with cross-trained workforce are discrete event simulation (hereafter, DES) (Park 1990), linear programming (Slomp and Molleman 2002), mixed linear programming (Wirojanagud et al 2007) and queuing theory (Bokhorst and Gaalman 2009). Unusually, this paper presents an ABM approach which warrants some introduction.…”

Workforce flexibility in operations management

“…Furthermore, when multiple workers of a type (dedicated or cross-trained) are available, a ''who'' rule is needed for determining the worker who will take the next available job arrival. The Longest Idle Time (LIT) policy is often used for this purpose (e.g., [62,64,81,95,103]). …”

“…Algorithms for solving the IP problems have been intensively investigated, including Branch-and-Bound (BNB), Genetic Algorithms (GA), linear approximation, and heuristics. Queueing is another tool considered by some for determining cross-training configurations when systems can be represented by standard queueing models (see [83,[102][103][104]). Other methods are also used to facilitate the selection of cross-training configurations.…”

“…Aksin et al [57] reviewed the literature of cross-training for call center applications. Different from × × [58,59] × × × [60][61][62][63][64] × × [65][66][67][68][69][70][71][72][73][74][75][76][77][78] × × × [79][80][81] × × × × [82] × × × [83][84][85][86][87][88] × × [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107] other review papers dedicated to cross-train...…”

A Multi-objective Cross-training Plan Based on NSGA-II

“…Ref. [15] explored the impact of cross-trained workers in dual resource constrained (DRC) systems with machines having different mean processing times by means of queuing and simulation analysis.…”