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This clearly written , mathematically rigorous text includes a novel algorithmic exposition of the simplex method and also discusses the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network...

Ising machines, including quantum annealing machines, are promising next-generation computers for combinatorial optimization problems. However, due to hardware limitations, most Ising-type hardware can only solve objecti...

Metaheuristics Algorithms are widely recognized as one of most practical approaches for Combinatorial Optimization Problems. This paper presents a comparison between two metaheuristics to solve a problem of Phase Bal...

FOUNDATIONS. The Scope of Integer and Combinatorial Optimization. Linear Programming. Graphs and Networks. Polyhedral Theory. Computational Complexity. Polynomial-Time Algorithms for Linear Programming. Integer Lattices....

Ising machines are novel computing devices for the energy minimization of Ising models. These combinatorial optimization problems are of paramount importance for science and technology, but remain difficult to tackle on ...

El problema de programación flexible del taller de trabajos (FJSSP) es uno de los problemas de optimización combinatoria más desafiantes, con aplicabilidad práctica en un entorno de producción real. En este trabajo,...

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (fi...

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. ...

The Ant Colony Optimization (ACO) metaheuristic is a bio-inspired approach for hard combinatorial optimization problems for stationary and non-stationary environments. In the ACO metaheuristic, a colony of artificial ant...

The portfolio optimization problem is a mathematical and financial decision-making method that aims to achieve an optimal trade-off between financial risk and return. This paper presents the first application of the nove...

In recent years, there has been a growing interest in optical simulation of lattice spin models for applications in unconventional computing. Here, we propose optical implementation of a three-state Potts spin model by u...

This review is an updated and expanded version of two prior reviews that were published in this journal in 1997 and 2003. In the case of all approved agents the time frame has been extended to include the 251/2 years fro...

Abstract The computation of optimal routes considering multiple factors is a key challenge in operations research, with significant impact on practical decision-making and real-world efficiency. Optimal bus transit route...

The Time-Dependent Travelling Salesman Problem (TDTSP) is a generalization of the traditional TSP where the travel cost between two cities depends on the moment of the day the arc is travelled. In this paper, we focus on...

Subjects / keywords: Metaheuristics; Operations research; Combinatorial optimization; Approximation theory

Subjects / keywords: Computer science; Travelling salesman problem; Computation; Nonlinear system; Optimization problem; Artificial neural network; Mathematical optimization; Combinatorial optimization; Analog computer; Artificial intelligen...

This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to ...

Subjects / keywords: graphs and hypergraphs, network theory, combinatorial optimization, coding theory, block designs; Science: Mathematics

BackgroundLast-mile logistics accounts for approximately 28% of total logistics costs and is a major contributor to urban carbon emissions. The integration of drones with electric trucks offers a path toward more efficie...

This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrain...