No abstract

This study addresses multi-stage hybrid flow shop scheduling in which a job is reworked if the queue time between two arbitrary stages exceeds an upper limit. The problem is to determine the allocations of jobs to machines at each stage and the start times of jobs and rework setups/operations when incurred. A mixed integer programming model is proposed for each of the makespan and the total tardiness measures. Then, because the problem is NP-hard, a scheduling mechanism is proposed that consists of three phases: (a) filtering the jobs to be delayed; (b) searching the jobs to be reworked; and (c) dispatching non-delayed and delayed jobs sequentially. Simulation results show that the mechanism proposed in this study outperforms the conventional dispatching approach in the high rework setup time case for the makespan problem and low/high setup time cases for the tardiness problem. The best priority rules of the mechanism under each of the measures are also reported.

Disassembly leveling and lot-sizing is an integrated problem of determining the depth of disassembling a product and the resulting amounts of disassembling the product and its subassemblies to satisfy component demands. This study addresses two stochastic versions of the problem for multiple product types with uncertain component demands: a basic problem and its extension with parts commonality. The objective is to minimize the sum of expected setup and operation costs. For the basic problem without parts commonality, an optimal sample average approximation algorithm is proposed and then illustrated with a numerical example after formulating it as a stochastic integer programing model. Also, due to the complexity of the extended problem, sample average approximation based heuristics are proposed and computational results are reported. In particular, the results show that the best heuristic gives near optimal solutions for small sized test instances.