Sequencing to Minimize Work Overload in Assembly Lines with Product Options

TLDR

Sequencing jobs with many customer‑specified option combinations is common in automotive manufacturing, but the uniqueness of each job makes existing mixed‑model sequencing methods inapplicable. The study seeks to sequence jobs with many customer‑specified option combinations on a paced assembly line to maximize completed work and minimize work overload. The authors model a paced line where jobs arrive at fixed cycle times, analyze the optimal sequencing for a single station with two operation sets, and extend the solution to a heuristic for multiple stations. Computational experiments on data from a major automobile company demonstrate the effectiveness of the proposed heuristic.

Abstract

We address the problem of sequencing jobs, each of which is characterized by one of a large number of possible combinations of customer-specified options, on a paced assembly line. These problems arise frequently in the automotive industry. One job must be launched into the system at equal time intervals, where the time interval (or cycle time) is prespecified. The problem is to sequence the jobs to maximize the total amount of work completed, or equivalently, to minimize the total amount of incomplete work (or work overload). Since there is a large number of option combinations, each job is almost unique. This fact precludes the use of existing mixed model assembly line sequencing techniques. We first consider the sequencing problem for a single station which can perform two different sets of operations. We characterize the optimal solution for this problem and use the results as the basis for a heuristic procedure for multiple stations. Computational results with data from a major automobile company are reported.