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Solving Assignment Problems via Quantum Computing: a Case-Study in Train Seating Arrangement

Gioda Ilaria, Caputo Davide, Fadda Edoardo, Manerba Daniele, Silva Fernández Blanca, Tadei Roberto

Annals of Computer Science and Information Systems

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2021

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Vol. 25

217--220

EN

In recent years, researchers have oriented their studies towards new technologies based on quantum physics that should resolve complex problems currently considered to be intractable. This new research area is called Quantum Computing. What makes Quantum Computing so attractive is the particular way with which quantum technology operates and the great potential it can offer to solve real-world problems. This work focuses on solving assignment-like combinatorial optimization problems by exploiting this novel computational approach. A case-study, denoted as the Seating Arrangement Optimization problem, is considered. It is modeled through the Quadratic Unconstrained Binary Optimization paradigm and solved through two tools made available by the D-Wave Systems company, QBSolv, and a quantum-classical hybrid system. The obtained experimental results are compared in terms of solution quality and computational efficiency

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Combinatorial Etude

Stoenchev Miroslav, Todorov Venelin

Annals of Computer Science and Information Systems

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2021

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Vol. 25

231--234

EN

The purpose of this article is to consider a special class of combinatorial problems, the so called Prouhet-Tarry Escot problem, solution of which is realized by constructing finite sequences of ±1. For example, for fixed p∈N, is well known the existence of np∈N with the property: any set of np consecutive integers can be divided into 2 sets, with equal sums of its p[th] powers. The considered property remains valid also for sets of finite arithmetic progressions of complex numbers.

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Towards fully decentralized multi-objective energy scheduling

Bremer Jörg, Lehnhoff Sebastian

Annals of Computer Science and Information Systems

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2019

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Vol. 18

193--201

EN

Future demand for managing a huge number of individually operating small and often volatile energy resources within the smart grid is preponderantly answered by involving decentralized orchestration methods for planning and scheduling. Many planning and scheduling problems are of a multi-objective nature. For the single-objective case - e.g. predictive scheduling with the goal of jointly resembling a wanted target schedule - fully decentralized algorithms with self-organizing agents exist. We extend this paradigm towards fully decentralized agent-based multi-objective scheduling for energy resources e.g. in virtual power plants for which special local constraint-handling techniques are needed. We integrate algorithmic elements from the well-known S-metric selection evolutionary multi-objective algorithm into a gossiping-based combinatorial optimization heuristic that works with agents for the single-objective case and derive a number of challenges that have to be solved for fully decentralized multi-objective optimization. We present a first solution approach based on the combinatorial optimization heuristics for agents and demonstrate viability and applicability in several simulation scenarios.

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Best response dynamics for VLSI physical design placement

Rapoport Michael, Tamir Tami

Annals of Computer Science and Information Systems

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2019

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Vol. 18

147--156

EN

The physical design placement problem is one of the hardest and most important problems in micro chips production. The placement defines how to place the electrical components on the chip. We consider the problem as a combinatorial optimization problem, whose instance is defined by a set of $2$-dimensional rectangles, with various sizes and wire connectivity requirements. We focus on minimizing the placement area and the total wire length.

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Heuristic algorithms for the problem of task scheduling with moving executors

Józefczyk J.

Systems Science

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2004

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Vol. 30, no 3

95-103

EN

New heuristic algorithms for solving the task scheduling problem with moving executors to minimize the sum of completion times are considered. The corresponding combinatorial optimization problem is formulated for single executor. A hybrid solution algorithm is introduced and investigated, where evolutionary as well as simulated annealing procedures are applied. A simulated annealing algorithm assists the evolutionary algorithm in three different ways. It is used for the generation of the initial set of solutions of the evolutionary algorithm. Moreover, this algorithm attempts to enhance the best solutions at current iterations of the evolutionary procedure. The results of the evaluation of the solution algorithms, which have been performed during the computer simulation experiments, are presented. The influence of the parameters of the solution algorithm as well as the task scheduling problem on the quality of results and on the time of computation is investigated.