جستجوی مقالات مرتبط با کلیدواژه « Stochastic Optimization » در نشریات گروه « فناوری اطلاعات »

This study aims to develop a capacitated hub location-routing model to design a rapid transit network under uncertainty. The mathematical model is formulated by making decisions about the location of the hub and spoke (non-hub) nodes, the selection of the hub and spoke edges, the allocation of the spoke nodes to the hub nodes, the determination of the hub and spoke lines, the determination of the percentage of satisfied origin-destination demands, and the routing of satisfied demand flows through the lines. Capacity constraints are considered in the hub and spoke nodes and also the hub and spoke edges. Uncertainty is assumed for the demands and transportation costs, represented by a finite set of scenarios. The aim is to maximize the total expected profit, where transfers between the lines are penalized by including their costs in the objective function. The performance of the proposed model is evaluated by computational tests and some managerial insights are also provided through the analysis of the resulting networks under various parameter settings.

Here, issues connected with characteristic stochastic practices are considered. In the first part, the plausibility of covering the arrangements of an improvement issue on subjective subgraphs is studied. The impulse for this strategy is a state where an advancement issue must be settled as often as possible for discretionary illustrations. Then, a preprocessing stage is considered that would quicken ensuing inquiries by discovering a settled scattered subgraph covering the answer for an arbitrary subgraph with a high likelihood. This outcome is grown to the basic instance of matroids, in addition to advancement issues taking into account the briefest way and resource covering sets. Next, a stochastic improvement model is considered where an answer is sequentially finished by picking an accumulation of “points”. Our crucial idea is the profit of adaptivity, which is investigated for an extraordinary sort of an issue. For the stochastic knapsack issue, the industrious upper and lower cutoff points of the “adaptivity hole” between ideal adaptive and non-adaptive methodologies are checked. Also, an algorithm is described that accomplishes a close ideal estimate. Finally, complicational results are shown to verify the optimal adaptive approaches.