Patrick R. McMullen

Using Ant Techniques to Solve the Assembly Line Balancing Problem

This paper presents an approach, based on ant techniques, to effectively address the assembly line balancing problem with the complicating factors of parallel workstations, stochastic task durations, and mixed-models. A methodology was inspired by the behavior of social insects in an attempt to distribute tasks among workers so that strategic performance measures are optimized. This methodology is used to address several assembly line balancing problems from the literature. The assembly line layouts obtained from these solutions are used for simulated production runs so that output performance measures (such as cycle time performance) are obtained. Output performance measures resulting from this approach are compared to output performance measures obtained from several other heuristics, such as simulated annealing. A comparison shows that the ant approach is competitive with the other heuristic methods in terms of these performance measures.

Multi-objective assembly line balancing via a modified ant colony optimization technique

A technique derived from ant colony optimization is presented that addresses multiple objectives associated with the general assembly line-balancing problem. The specific objectives addressed are crew size, system utilization, the probability of jobs being completed within a certain time frame and system design costs. These objectives are addressed simultaneously, and the obtained results are compared with those obtained from single-objective approaches. Comparison shows the relative superiority of the multi-objective approach in terms of both overall performance and the richness of information.

Optimal product design using a colony of virtual ants

Abstract The optimal product design problem, where the “best” mix of product features are formulated into an ideal offering, is formulated using ant colony optimization (ACO). Here, algorithms based on the behavior of social insects are applied to a consumer decision model designed to guide new product decisions and to allow planning and evaluation of product offering scenarios. ACO heuristics are efficient at searching through a vast decision space and are extremely flexible when model inputs continuously change. When compared to complete enumeration of all possible solutions, ACO is found to generate near-optimal results for this problem. Prior research has focused primarily on optimal product planning using consumer preference data from a single point in time. Extant literature suggests these formulations are overly simplistic, as a consumer’s level of preference for a product is affected by past experience and prior choices. This application models consumer preferences as evolutionary, shifting over time.

Combining approaches for evaluating auditing populations: A simulation study

This research explores ways of combining four distinct bounds for the mean error in an auditing population. Two competing objectives for a bound are to be close to the true mean being estimated and to be reliable: not less than the true mean in more than 5% of estimations. The optimal combination should provide the best balance of these competing objectives. Estimating the mean error by a single approach is difficult because typically most accounts have no error and the distribution of the errors among those that do is discontinuous and highly skewed. This study reveals that the weights in the optimal combination are not constant but depend on the characteristic of the population being estimated. The optimally combined bound is only 7% smaller overall than the best of the constituents. However, while the best of the constituents fails in 50% of most challenging populations, the optimal combination never fails.

An objective function to address production sequencing with minimal tooling replacements

A strategy is presented to obtain production sequences resulting in minimal tooling replacements. An objective function is employed to distribute the tool wear as evenly as possible throughout the sequence. This objective function is an extension of Miltenburgs earlier work (1989) concerned with obtaining production sequences while evenly distributing the satisfaction of demand. Smaller problems are solved to optimality, while larger problems are solved as close as possible to optimality. The production sequences are simulated to estimate required tooling replacements. The methodology presented here consistently results in fewer tooling replacements when compared with earlier published work (McMullen et al. 2002, McMullen 2003).