Scheduling and planning problems arises in many real-world contexts with various industrial and economic applications, from job shop scheduling and assembly line balancing to efficient cloud resource provisioning. Identifying good schedules usually leads to difficult optimization problems which involve constraints, multiple conflicting optimization criteria and uncertainty. The presence of uncertainty limits the effectiveness of deterministic models and methods. In the context of scheduling, the probabilistic models can be used as surrogates for evaluate the quality of a solution, can be used to estimate the likelihood of various events which influence the scheduling process or to incorporate prior knowledge about the problem into the search mechanism. On the other hand, the presence of constraints and of multiple optimization criteria limits the effectiveness of exact optimization methods, allowing the metaheuristics to prove their superiority for this class of problems.

The aim of this session is to group original contributions on scalable and robust scheduling metaheuristics based on appropriate probabilistic models and addressing various types of real world applications. Theoretical analyses as well as experimental studies are welcome.

The topics include but are not limited to:

• scheduling performance measures
• probabilistic models in scheduling (reliability/failure models, Bayesian networks, etc.)
• constructive scheduling heuristics versus iterative-improvement heuristics
• population-based scheduling metaheuristics (e.g. estimation of distribution algorithms, evolutionary algorithms, ant colony optimization, particle swarm optimization etc.)
• local search based scheduling metaheuristics (tabu search, variable neighborhood search, iterated local search, greedy randomized adaptive search, simulated annealing etc.)
• hybridization of metaheuristics with various local search heuristics
• scalability of scheduling algorithms
• real time scheduling
• energy aware scheduling
• applications in cloud computing, job-shop and flow-shop scheduling problems, vehicle routing problems, preventive maintenance scheduling etc.