New branch-and-bound rules for linear bilevel programming
SIAM Journal on Scientific and Statistical Computing
Hierarchical optimization: an introduction
Annals of Operations Research - Special issue on hierarchical optimization
Heuristic algorithms for delivered price spatially competitive network facility location problems
Annals of Operations Research - Special issue on hierarchical optimization
Practical Bilevel Optimization: Algorithms and Applications (Nonconvex Optimization and Its Applications)
Decentralized multi-objective bilevel decision making with fuzzy demands
Knowledge-Based Systems
Multi-objective Group Decision Making: Methods, Software and Applications With Fuzzy Set Techniques
Multi-objective Group Decision Making: Methods, Software and Applications With Fuzzy Set Techniques
On bilevel multi-follower decision making: General framework and solutions
Information Sciences: an International Journal
Incremental learning optimization on knowledge discovery in dynamic business intelligent systems
Journal of Global Optimization
Particle swarm optimization for bi-level pricing problems in supply chains
Journal of Global Optimization
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Classic bilevel programming deals with two level hierarchical optimization problems in which the leader attempts to optimize his/her objective, subject to a set of constraints and his/her follower's solution. In modelling a real-world bilevel decision problem, some uncertain coefficients often appear in the objective functions and/or constraints of the leader and/or the follower. Also, the leader and the follower may have multiple conflicting objectives that should be optimized simultaneously. Furthermore, multiple followers may be involved in a decision problem and work cooperatively according to each of the possible decisions made by the leader, but with different objectives and/or constraints. Following our previous work, this study proposes a set of models to describe such fuzzy multi-objective, multi-follower (cooperative) bilevel programming problems. We then develop an approximation Kth-best algorithm to solve the problems.