Scheduling manufacturing systems in an agile environment

Document Type


Peer Reviewed


Publication Date


Journal/Book/Conference Title

Robotics and Computer-Integrated Manufacturing


Producing customized products to respond to changing markets in a short time and at a low cost is one of the goals in agile manufacturing. To achieve this goal customized products can be produced using an assembly-driven product differentiation strategy. The successful implementation of this strategy lies in efficient scheduling of the system. However, little research has been done in addressing the scheduling issues related to assembly-driven product differentiation strategies in agile manufacturing. In this paper, scheduling problems associated with the assembly-driven product differentiation strategy in a general flexible manufacturing system are defined, formulated, and solved. The manufacturing system consists of two stages: machining and assembly. At the machining stage, multiple identical machines produce parts. These parts are then assembled at the assembly stage to form customized products. The products to be produced in the system are characterized by their assembly sequences that are represented by different digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of machines at the machining stage. The scheduling problems discussed in this paper have not been solved in the literature. The originality of the paper lies in defining and formulating the problems in the context of agile manufacturing and developing optimal and near-optimal for solving them. The heuristic algorithm solves the scheduling problem in two steps. First, an optimal aggregate schedule is determined by solving a two-machine flowshop problem. Next, the optimal aggregate schedule is decomposed by solving a simple integer programming formulation model. The computational experiment shows that the heuristics provide optimal and near-optimal solutions to the scheduling problems.



Published Article/Book Citation

Robotics and Computer-Integrated Manufacturing, 17:2011-01-02T00:00:00 (2001) pp.87-97.

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