Graduate Thesis Or Dissertation
 

Hybrid flowshop scheduling with dual resources in a supply chain

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  • This dissertation addresses a hybrid-flow shop scheduling problem with dual resource constraints in a supply chain. Most of the traditional scheduling problems deal with machine as the only resource. However, other resources such as labor is not only required for processing jobs but are often constrained. Considering the second resource (labor) makes the scheduling problems more realistic and practical to implement in industries. In this research labor has different skill levels and the skill level required to perform the setup could be different from that needed to perform the run. The setup time is sequence-dependent, and job release times and machine availability times are dynamic. Also machine skipping is allowed. In tactical supply chain decisions such as scheduling, the goal is to minimize the cost of producer. However, when looking at the whole network, minimizing the cost of the producer alone may not lead to minimizing the cost of the whole supply chain. In fact the coordination between the producer and other entities in the network can minimize the cost. In this dissertation coordination between producer and customers is considered in order to make effective scheduling decisions. The goal of this research is to minimize the work-in-process inventory for the producer and maximize customers' service level to maintain producer-customers coordination. A linear mixed-integer mathematical programming model is proposed and CPLEX solver is used to find solutions for generated example problems with branch-and-bound technique. As the problem is NP-hard in the strong sense three different meta-search heuristic algorithms based on tabu search are developed in order to quickly solve the scheduling problems. A total of 243 examples were generated in small, medium and large size problems. Search algorithms performance in small size problems can be assessed by comparing them with the optimal solution from branch-and-bound method. However, in medium and large size problems, branch-and-bound method cannot find the optimal solution and therefore for assessing the performance of search algorithms three different lower bounding methods are proposed. The first method is based on Logic-Based Benders Decomposition and the second and third methods are two different variations of iterative selective linear programming (LP) relaxation called fractional LP relaxation and positive LP relaxation. An experimental analysis based on a nested-factorial design with blocking is developed in order to identify statistically significant differences between the effectiveness and efficiency of the lower bounding methods and search algorithms. The results showed that the proposed search algorithms and lower bounding methods are very effective and efficient. On average the developed lower bounding methods tighten the lower bound found by branch-and-bound by 11.93%. The quality of search algorithms is the same as the upper bound found by branch-and-bound. However, the search algorithms are on average 3.8 times faster than the branch-and-bound method.
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