Human aspect and its incorporation into production line design 16

Because human beings are commonly used as resources in production systems, un-derstanding the nature of human work is important when investigating decisions pertaining to design of assembly lines. However, it is not an easy task since it is rel-evant to research in several disciplines such as ergonomics and psychology. Existing research appreciates human aspects such as learning, performance heterogeneity, ergonomics as well as human behavior.

Folgado et al. (2015) conduct two empirical studies for assessing the influence of worker heterogeneity on line output by using industrial data collected from an as-sembly line that produces automotive components. They study two different pacing mechanisms. One system imposes a fixed takt time (“rigid pacing”, Murrell (1972)) while the other paces workers by an hourly production target (“system with mar-gins”, Murrell (1972)). In the latter case, they find that slower workers show higher variability than faster workers. The difference in workers’ performance disappears in the rigid system, which leads to a 13% higher output in their study.

Carnahan et al. (2001), Otto and Scholl (2011) and Battini et al. (2016) investigate the assembly line balancing problem (ALBP) after incorporating the ergonomic as-pect into it. Particularly, they criticize optimizing task allocation decisions solely from the economical view point, because this might lead to severe consequences for operators’ physical well-being. They propose Pareto optimization, in other words, using an objective function that combines economic and ergonomic aspects. Car-nahan et al. (2001) use an objective function which equally weights fatigue and cycle time, Otto and Scholl (2011) investigate the trade-off between the number of workstations and ergonomic risks while Battini et al. (2016) analyze the trade-off between time smoothness and energy smoothness of task allocations. Battini et al. (2011) develop an integrated framework for assembly systems design that si-multaneously takes technological variables such as assembly times and ergonomics variables such as human diversity into account. Applying this framework to two industrial case studies, they show an increase in productivity by up to 15% while lowering fatigue levels and injuries.

Dode et al. (2016) integrate worker fatigue and learning effects into their simu-lation models and propose a line design for consumer electronics production by taking human factors into account. The proposed line allows up to 33% lower fa-tigue dosage compared to the existing line. Furthermore, a model that accounts for human learning, estimates 10.5% higher output compared to a model that ignores this effect. Neumann and Medbo (2016) also take human learning into account when comparing two types of assembly lines based on throughput during ramp-up:

a serial and a parallel flow line. They find that serial lines facilitate faster learning and a shorter ramp-up time, however, the latter flow type overtakes the former at a point in time, due to providing higher throughput potential.

Furthermore, there is a considerable amount of literature discussing the human reaction to the flow of work in their immediate environment when performing tasks

from the behavioral perspective. Edie (1954) states that the processing times de-crease with the amount of congestion. The studies by Doerr et al. (1996) and Schultz et al. (1998) suggest that the average processing time of workers are shorter in low inventory systems. Schultz et al. (1999) focus on psychological aspects such as goal setting, feedback and peer pressure for predicting how the workers adjust themselves and for explaining the previous findings about low inventory systems.

Hertel et al. (2000) show that increasing difference between the abilities of the two peers increases the effort of the weaker member. Schultz et al. (2003) assert that visible feedback on the performance increases the pace at which the workers oper-ate. Kc and Terwiesch (2009) use data from two health-care delivery services and show that the rate at which workers provide service increases with the system load.

However, if the system remains highly loaded for a long duration, the service rate decreases. By using real-world manufacturing data, Schultz et al. (2010) show that there is a reaction by workers to the speed of their co-workers, which varies from one to the other. Other studies that suggest a dependency between a worker’s be-havior and his coworkers include; Falk and Ichino (2006), Mas and Moretti (2009), Siemsen et al. (2007), Gould and Winter (2009).

However, only a small number of studies use these findings to model human be-havior more realistically when analyzing production lines. An important finding by the study of Schultz et al. (1998) is that the lines with low inventory can be as efficient as high inventory lines due to the adaptive behavior of workers. Powell and Schultz (2004) investigate the relationship between the line length and the through-put of a production system under the assumption that workers adjust their speeds dependent on the system state. Their findings show that the lines are more efficient in the existence of this behavior. Furthermore, the efficiency deterioration due to increasing the number of stages in a production line is not as large as estimated by studies ignoring this behavioral effect. Heimbach et al. (2012) consider the workload allocation problem and study the effect of state-dependent behavior on the optimal allocation of workload. The shape of the optimal allocation changes with the speed adjustment factor. A bowl-shaped allocation is observed for the case where the speed adjustment is zero, whereas a balanced or a reverse bowl-shaped allocation is observed for moderate and large values of the adjustment parameter. The last two studies can be criticized for modeling workers as stationary resources which are always capable of increasing their speed up-to the required amount as long as the condition for speed-up is satisfied. This turns out to be a strong assumption espe-cially when workload allocation problem is considered. In Heimbach et al. (2012), the results for high values of the speed-up parameter (f = 0.9) suggest nearly all the workload to be assigned to the worker in the middle station in a three-stage

system when the inter-stage buffers are small (single buffer space). Schultz et al.

(2010) consider the problem of optimizing the order of workers in a production line with the objective of maximizing the worker output. They suggest that the workers should be ordered from fastest to slowest in such a way that each worker can only see the faster one in front.

Chapter 5 compares paced and unpaced assembly lines using a simulation model while Chapter 6 investigates the optimal line design problem by introducing an ex-act model. The state-dependent worker behavior is incorporated into both models in combination with fatigue.

3. Impact of Priority Sequencing Decisions on On-Time Probability and Expected Tardiness of Orders in MTO Production Systems with External Due-Dates

We model the priority sequencing problem in a make-to-order (MTO) production system where the customers specify the amount of time they are willing to wait for their orders to be fulfilled as a Markov decision process (MDP). The objective function is the sum of a fixed and a variable cost of tardiness that combines two external customer-related criteria: “on-time probability of orders” and “expected tardiness of orders”. We benchmark several simple rules against the optimal policy and analyze the efficient frontier of on-time probability and expected tardiness. The numerical results show that it is possible to obtain near optimal performance by employing simple rules. Whenever a fixed cost of tardiness is involved, the optimal priority sequencing policy deviates from the earliest-due-date (EDD) principle, how-ever, an adjusted EDD rule performs well. Furthermore, postponement of priority sequencing decisions until the next completion improves performance.