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17:00 | Optimizing DSO Requests Management Flexibility for Home Appliances using CBCC-RDG3 ABSTRACT. In this article, we consider the statement of a problem described in the IEEE PES GM, CEC \& GECCO 2021 Competition devoted to "Evolutionary Computation in the Energy Domain: Smart Grid Applications" \cite{web_gecco} dedicated to combining and testing more advanced Computational Intelligence (CI) techniques applied to energy domain problems, namely the Flexibility management of home appliances to support DSO requests. First of all, the main task is to provide for aggregators' flexibility in distribution networks by building an optimal schedule that takes advantage of load flexibility resources. This, in turn, allows for the re-scheduling of shifting/real-time home appliances to provision a request from a distribution system operator (DSO). To solve this problem we use several optimization methods to find the best possible schedule that would be quite efficient and consistent with all work-related constrains. There are many existing heuristic, metaheuristic, and exact algorithms that can solve this optimization problem; yet it is quite likely that the state-of-the-art methods currently available may benefit from improvement. For these reasons, taking into account the task at hand, we decided to use the CBCC-RDG3 algorithm \cite{CBCC_RDG3_original}. Other popular algorithms were carried out in comparison with our solution to successfully demonstrate the efficiency and effectiveness of this particular approach. |

17:15 | A matheuristic for the trailers waiting time minimization with uncertain arrival times ABSTRACT. We present a new loading/unloading trailer scheduling problem for a logistics company. There is a building with several warehouses. Each warehouse stores pallets of different types of products in rooms for loading into trailers. Also, each warehouse has two gates. One gate is for the trailers, another one is for two forklifts from the central zone. At most one forklift can work in each warehouse at a time. The central zone is a production line. It produces some products which must be placed in the warehouses by the no wait rule. We know how the central zone works during each day, i.e. when each pallet becomes available for putting away. Outbound trailers come to pick some pallets of a product. Inbound trailers come to put away some pallets of a product. We assume that the arrival time for each trailer is uncertain. Our goal is to assign all trailers to warehouses and find scheduling for service all the trailers with the maximum stability radius, taking into account that the total waiting time for the trailers cannot exceed the given threshold. For this NP-complete problem, we design a two- stage matheuristic. First of all, we solve the simplified model using the Gurobi solver. Later on, the VNS algorithm is used to return the solution into the feasible region taking into account detailed information about pallets in each warehouse. Computational results for 6 warehouses, 18 types of products, and 90 trailers are discussed. |

17:35 | PRESENTER: Sviatoslav Panchenko ABSTRACT. Neural networks are used for channel decoding, channel detection, channel evaluation, and resource management in multi-input and multi-output (MIMO) wireless communication systems. In this paper, we consider the problem of finding precoding matrices with high spectral efficiency (SE) using variational autoencoder (VAE). We propose a computationally efficient algorithm for sampling precoding matrices with minimal loss of quality compared to the optimal precoding. In addition to VAE, we use the conditional variational autoencoder (CVAE) to build a unified generative model. Both of these methods are able to reconstruct the distribution of precoding matrices of high SE by sampling latent variables. This distribution obtained using VAE and CVAE methods is described in the literature for the first time. |

17:55 | Global Search in Optimization Problems with DC Inequality Constraints ABSTRACT. We consider the nonconvex optimization problem with DC inequality constraints. Using the exact penalization theory, we reduce the original problem to a penalized one without constraints. The latter problem turns out to be the DC minimization problem and can be solved using the Global Search algorithm, which consists of two basic stages: a special local search method and procedures of escaping from a critical point based on Global Optimality Conditions. The local search method (LSM) employs a consecutive solution of linearized problems combined with a change of penalty parameter. Computational testing of developed methods has been carried out on test problems. The effectiveness of the developed method is not only in escaping critical points provided by LSM but in getting global solutions. |

18:10 | Minimizing Makespan for Parallelizable Jobs with Energy Constraint ABSTRACT. We investigate the problem of scheduling parallelizable jobs to minimize the makespan under the given energy budget. A parallelizable job can be run on an arbitrary number of processors with a job execution time that depends on the number of processors assigned to it. We consider malleable and moldable jobs. Processors can vary their speed to conserve energy using dynamic speed scaling. Polynomial time algorithms with approximation guarantees are proposed. In our algorithms, a lower bound on the makespan and processing times of jobs are calculated. Then numbers of utilized processors are assigned for jobs and a feasible solution is constructed using a list-type scheduling rule. |

18:25 | Branch-and-Cut Algorithm for the Precedence Constrained Generalized Traveling Salesman Problem ABSTRACT. The Precedence Constrained Generalized Traveling Salesman Problem (PCGTSP) is an extension of the well-known Generalized Traveling Salesman Problem (GTSP), where feasible tours are restricted to visit all the clusters with respect to some given partial order. Unlike the GTSP, to the best of our knowledge, the PCGTSP is studied rather weakly both in terms of polyhedral theory and algorithms’ design and implementation. In this paper, by extending of the seminal Fischetti’s inductive approach, we establish dimension of the PCGTSP polytope and prove sufficient conditions that allow us to lift the facet-inducing inequalities proposed by E.Balas for the Precedence Constrained Asymmetric TSP polytope to the case of PCGTSP. Relying on these theoretical results, we design the first branch-and-cut algorithm for the PCGTSP and implement it in the context of the Gurobi user callbacks framework. Results of the numerical evaluation against the public PCGTSPLIB benchmark library show that proposed algorithm outperforms both the state-of-the-art MIP solver Gurobi with default setting of cutting planes and the known branch-and-bound and dynamic programming algorithms for PCGTSP, even in the case, where all competing algorithms are equipped with the same MIP-start solution. |