An Evolutionary Algorithm for Sequencing Production on a Paced Assembly Line

Abstract

A new sequencing method for mixed-model assembly lines is developed and tested. This method, called the Evolutionary Production Sequencer (EPS) is designed to maximize production on an assembly line. The performance of EPS is evaluated using three measures: minimum cycle time necessary to achieve 100% completion without rework, percent of items completed without rework for a given cycle time, and sequence "smoothness." The first two of these measures are based on a simulated production system. Characteristics of the system, such as assembly line station length, assembly time and cycle time, are varied to better gauge the performance of EPS. More fundamental variation is studied by modeling two production systems. In one set of tests, the system consists of an assembly line in isolation (i.e., a single-level system). In another set of tests, the production system consists of the assembly line and the fabrication system supplying components to the line (i.e., a two-level system). Sequence smoothness is measured by the mean absolute deviation (MAD) between actual component usage and the ideal usage at each point in the production sequence.The performance of EPS is compared to those of well-known assembly line sequencing techniques developed by Miltenburg (1 989), Okamura and Yamashina (1979), and Yano and Rachamadugu (1991). EPS performed very well under all test conditions when the criterion of success was either minimum cycle time necessary to achieve 100% production without rework or percent of items completed without rework for a given cycle time. When MAD was the criterion of success, EPS was found inferior to the Miltenburg heuristic but better than the other two production-oriented techniques.

A heuristic based on Vogel's approximation method for sequencing mixed-model assembly lines

“…If work overload is compensated by additional workers, this amount of work is also called utility work. Papers which deal with these objectives are, for instance, Wester and Kilbridge (1964), Thomopoulos (1967), Macaskill (1973), Yano and Bolat (1989), Yano and Rachamadugu (1991), Bolat and Yano (1992b), Tsai (1995), Scholl et al (1998), Sumichrast et al (2000), Sarker and Pan (2001), Kim and Jeong (2007) and Bautista and Cano (2008).…”

Workforce‐constrained Preventive Maintenance Scheduling Using Evolution Strategies

“…Indeed, it has been suggested that overemphasizing low-level operators and components (which is done in GAS) causes the loss of the selection of good behaviors in evolutionary processes (Fogel, 1995b). Finally, GA has been found to be less efficient as compared to ES in solving problems with many constraints (Sumichrast, Oxenrider, & Clayton, 2000).…”

“…We ran sample experiments with different parameter settings and arrived at the chosen parameters upon the preliminary findings. This procedure is generally followed for new categories of algorithms that have not been applied in a particular context (e.g., Sumichrast et al, 2000).…”

Sequencing mixed-model assembly lines: Survey, classification and model critique

“…This raises the question for the real-world prerequisites under which part-oriented level scheduling is a suited surrogate model to minimize the actual model sequence-dependent costs of material supply. Preliminary computational studies on the material flows resulting from optimal sequences of part-oriented level scheduling in simulated production environments are provided by Sumichrast and Clayton (1996) as well as Sumichrast et al (2000).…”