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a b s t r a c tIn this study, we design a reliable logistics network based on a hub location problem, which is less sensitive to disruption and it performs efficiently when disruption occurs. A new mixed-integer programming model is proposed to minimize the total sum of the nominal and expected failure costs. This model considers complete and partial disruption at hubs. In addition, we propose a new hybrid meta-heuristic algorithm based on genetic and imperialist competitive algorithms. We compare the performance of the proposed algorithm with a new lower bound method in terms of the CPU time and solution quality. Furthermore, we conclude that a considerable improvement in the reliability of the network can be achieved with only a slight increase in the total cost. Finally, we demonstrate that the networks designed using our model are less conservative and more robust to disruption compared with those designed based on other robustness measures.

This study develops a new optimisation framework for process inspection planning of a manufacturing system with multiple quality characteristics, in which the proposed framework is based on a mixed-integer mathematical programming (MILP) model. Due to the stochastic nature of production processes and since their production processes are sensitive to manufacturing variations; a proportion of products do not conform the design specifications. A common source of these variations is misadjustment of each operation that leads to a higher number of scraps. Therefore, uncertainty in misadjustment is taken into account in this study. A twofold decision is made on the subject that which quality characteristic needs what kind of inspection, and the time this inspection should be performed. To cope with the introduced uncertainty, two robust optimisation methods are developed based on Taguchi and Monte Carlo methods. Furthermore, a genetic algorithm is applied to the problem to obtain near-optimal solutions. To validate the proposed model and solution approach, several numerical experiments are done on a real industrial case. Finally, the conclusion is provided.