We consider the problem of resequencing a pre-arranged set of jobs on a moving assembly line with the objective of minimizing changeover costs. A changeover cost is incurred whenever two consecutive jobs do not share the same feature. Features are assigned from a set of job-specific feasible features. Re-sequencing is limited by the availability of offline buffers. The problem is motivated by a vehicle resequencing and painting problem at a major U.S. automotive manufacturer. We develop a model for solving the joint resequencing and feature assignment problem and an efficient solution procedure for simultaneously determining optimal feature assignments and vehicle sequences. We show that our solution approach is amenable to implementation in environments where a solution must be obtained within tight time constraints. We also show that the effect of offline buffers is of the diminishing kind with most of the benefits achieved with very few buffers. This means that limited resequencing flexibility is generally sufficient.Furthermore, we show that the value of resequencing is sensitive to the feature density matrix, with resequencing having a significant impact on cost only when density is in the mid range.
We consider the problem of resequencing a set of prearranged jobs when there is limited resequencing flexibility and sequencedependent changeover costs. Resequencing flexibility is limited by how far forward or backward a job can shift in the sequence relative to its original position. We show how the problem can be solved using dynamic programming in polynomial time with respect to the number of jobs. We also show how the same solution approach can be extended to problems where sequencing constraints are job specific and to problems where job features, which determine changeover costs, are jointly determined with the job sequence. We provide an integer programming formulation to the resequencing problem whose linear programming relaxation offers a useful lower bound. We also describe a family of decomposition heuristics that are easy to customize to provide desired levels of solution quality and solution time. We document the quality of the lower bound from the linear programming relaxation and the upper bound from the heuristic using numerical results. We also provide numerical results to support managerial insights regarding the value of flexibility. We show that the value of flexibility is of the diminishing kind with most of the benefit realized with relatively limited flexibility. We also show that a balanced allocation of flexibility among forward and backward position shifting is superior to an unbalanced one. More significantly, we show that forward and backward position shifting flexibility are complements with the value of one increasing in the amount of the other. Finally, we apply our solution approach to a real-world case from the automotive industry.
In this paper, we describe a dynamic network optimization-based solution framework that effectively integrates the management of complex information with traffic strategies and logistics support. This framework is composed of a transportation management function, a logistics support and capacity refinement function, and a validation function. The transportation management function is central to this solution framework and is mainly composed of four modules; a Rough- Cut Capacity Plan (RCCP), a Detailed Capacity Plan (DCP), a Restricted Evacuation Plan (REP) and an Enforced Evacuation Plan (EEP). This research has been motivated by the challenges experienced during the hurricane Rita evacuation of the City ofHouston. This tool is expected to provide an intelligent systematic methodology that can be used by transportation planners and emergency agencies to improve traffic management during hurricane evacuation, enhance public safety, and mitigate the overall economic impact of evacuation.
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