Fuzzy Bi-level Decision-Making Techniques: A Survey <s> Resource allocation in hierarchy <s> This paper focuses on a multiproject resource allocation problem in ::: a bilevel organization. To solve this problem, a bilevel multiproject resource allocation model under a fuzzy random ::: environment is proposed. Two levels of decision makers are ::: considered in the model. On the upper level, the company manager ::: aims to allocate the company's resources to multiple projects to ::: achieve the lowest cost, which include resource costs and a ::: tardiness penalty. On the lower level, each project manager attempts ::: to schedule their resource-constrained project, with minimization of ::: project duration as the main objective. In contrast to prior ::: studies, uncertainty in resource allocation has been explicitly ::: considered. Specifically, our research uses fuzzy random variables ::: to model uncertain activity durations and resource costs. To search ::: for the optimal solution of the bilevel model, a hybrid algorithm ::: made up of an adaptive particle swarm optimization, an adaptive ::: hybrid genetic algorithm, and a fuzzy random simulation algorithm is ::: also proposed. Finally, the efficiency of the proposed model and ::: algorithm is evaluated through a practical case from an industrial ::: equipment installation company. The results show that the proposed ::: model is efficient in dealing with practical resource allocation problems in a bilevel organization. <s> BIB001 </s> Fuzzy Bi-level Decision-Making Techniques: A Survey <s> Resource allocation in hierarchy <s> This paper investigates a transportation scheduling problem in large-scale construction projects under a fuzzy random environment. The problem is formulated as a fuzzy, random multi-objective bilevel optimization model where the construction company decides the transportation quantities from every source to every destination according to the criterion of minimizing total transportation cost and transportation time on the upper level, while the transportation agencies choose their transportation routes such that the total travel cost is minimized on the lower level. Specifically, we model both travel time and travel cost as triangular fuzzy random variables. Then the multi-objective bilevel adaptive particle swarm optimization algorithm is proposed to solve the model. Finally, a case study of transportation scheduling for the Shuibuya Hydropower Project in China is used as a real world example to demonstrate the practicality and efficiency of the optimization model and algorithm. <s> BIB002
In regard to handling resource allocation in hierarchy, Gang et al. BIB001 focused on a multi-project resource allocation problem using bi-level optimization. In this problem, the company manager at the upper level aims to allocate the company's resources to multiple projects to achieve the lowest cost that include resource costs and a tardiness penalty, while each project manager at the lower level attempts to schedule their resourceconstrained project with the minimization of project duration. The authors applied a fuzzy random bi-level decision model to describe this problem in which fuzzy random variables are used to model uncertain activity durations and resource costs. Xu and Gang BIB002 studied a material flow traffic assignment problem with fuzzy random variables in a large-scale construction project. A fuzzy random bi-level multi-objective decision model was established in which the transportation time and cost in each arc are considered as fuzzy random variables, and the authors developed a PSO algorithm to solve the resulting model.