A New Bottleneck-Based Heuristic for Reentrant Job Shops: A Case Study in a Textile Factory

Summary The classical job shop assumes that each job visits a machine only once. In practice, this assumption is often violated. Recently, the reentrant job shop has become prominent in which a certain job may visit a specific machine or a set of machines more than once during the process ow. Reentrant job shops can be found in many production systems, particularly in high-tech industries such as semiconductor manufacturing. Another example is the manufacturing of printed circuit boards that require both surface-mounted devices and conventional pinthrough- hole devices. It is also employed in parts that go through the painting and baking divisions alternately for different coats of paint in a painting shop. The problem of minimizing makespan in a reentrant job shop is theoretically challenging. In fact, it is NP-hard in the strong sense even for the two-machine case [1]. For the solution of job shop scheduling problems (JSSPs), exact methods such as integer programming formulations [2] and branch-and-bound algorithms [3] have been developed to produce optimal solutions. However, their worst-case computational burden increases exponentially with the size of the problem instance. As noted in Aytug et al. [4], for industrial problems the computational time of any algorithm must be short enough that the resulting schedule can be used. Hence, a variety of heuristic procedures such as dispatching rules, decomposition methods, and metaheuristic search techniques have been proposed for finding “good” rather than optimal solutions in a reasonably short time.