Abstract:This study considers a multi-objective combined budget constrained facility location/network design problem (FL/NDP) in which the system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure the network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including, transshipment costs, facility location (FL) costs, fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider of several system uncertainty, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient Sub-gradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. At the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering of practical factors (e.g., several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and subsequently more efficient utilization of the established network.
In acceptance sampling plans, the decisions on either accepting, rejecting the lot or inspecting all items in the lot is still a challenging problem. We developed a new acceptance sampling plan to decide about the received lot based on cost objective function in the presence of inspection errors. It is assumed that inspection is not perfect and type I and type II errors occur in the inspection process. The problem of acceptance sampling plan in the presence of inspection errors is modeled using decision tree approach, where the sample size and decision of accepting, rejecting or inspecting are decision nodes. Bayesian inference is used to update the probability distribution function of nonconforming proportion. Then the cost at terminal nodes is analysed and optimal decisions are determined using a backward recursive approach. A case study is solved for illustrating the application of the model and sensitivity analysis is carried out on the parameters of the proposed methodology and the behavior of model by changing the parameters is investigated. We also compared the proposed model with single sampling model. At the end, the model is generalized in order to consider different potential decisions that can be made in practice.
This study investigates the reliable multi-configuration capacitated logistics network design problem (RMCLNDP) under system disturbances, which relates to locating facilities, establishing transportation links, and also allocating their limited capacities to the customers conducive to provide their demand on the minimum expected total cost (including locating costs, link constructing costs, and also expected costs in normal and disturbance conditions). In addition, two types of risks are considered; (I) uncertain environment, (II) system disturbances. A two-level mathematical model is proposed for formulating of the mentioned problem. Also, because of the uncertain parameters of the model, an efficacious possibilistic robust optimization approach is utilized. To evaluate the model, a drug supply chain design (SCN) is studied. Finally, an extensive sensitivity analysis was done on the critical parameters. The obtained results show that the efficiency of the proposed approach is suitable and is worthwhile for analyzing the real practical problems.
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