In the wood industry, ensuring the stability and immobility of a workpiece during machining is essential. Vacuum suction cups are commonly used as fixtures, as they can rotate to better conform to the workpiece perimeter. However, this rotational capability introduces additional complexity into fixture placement. This paper addresses the problem of optimally positioning rotatable fixtures by combining two complementary techniques. First, we solve a restricted version of the problem that neglects rotations using a constraint solver, yielding an initial feasible solution. This solution is then refined using a Particle Swarm Optimization algorithm that accounts for rotation. Experimental results show that the quality of the initial solution significantly influences the final placement, and that the proposed approach can effectively support expert operators in making improved decisions.

Mobile platforms have matured to a point where they can provide the infrastructure required to support sophisticated optimization codes. This opens the possibility to envisage new interest for distributed application codes and the opportunity to intensify research on optimization algorithms requiring limited computational resources, as provided by mobile platforms. In this article, we report on some exploratory experience in this area. We illustrate some practical, real-world cases where running optimization programs on mobile or embedded devices can be useful, with particular emphasis on matheuristics approaches. Then, we discuss a practical use case involving the feasibility version of the generalized assignment problem (GAP). We present a JavaScript implementation of a GAP solver that can be executed inside an ordinary browser supporting ECMAScript. We tested the code on different smartphones of varying age and power, as well as on desktop PCs and other embedded devices. Our experiments confirm the viability of mobile devices for computational intensive tasks.

In recent years, GPU computing has become an increasingly important tool to develop efficient applications in several areas, including optimization. One of the optimization approaches that seems to take most advantage from GPU computing is dynamic programming. In this paper, we investigate the application of GPU computing to the two-dimensional guillotine cutting problem, solved by dynamic programming. We show a possible implementation and we discuss a number of technical issues. Computational results on test instances available in the literature and on new larger instances show the effectiveness of the dynamic programming approach based on GPU computing for this problem. Data, as supplemental material, are available at http://dx.doi.org/10.1287/ijoc.2016.0693 .

Peer-to-peer (P2P) computing already accounts for a large part of the traffic on the Internet, and it is likely to become as ubiquitous as current client/server architectures in next generation information systems. This paper addresses a central problem of P2P systems: the design of an optimal overlay communication network for a set of processes on the Internet. Such a network defines membership to the P2P group and allows for members to disseminate information within the group. The problem, named the membership overlay problem (MOP), can be formulated as a dynamic optimization problem where classical combinatorial optimization techniques must face the further challenge of time-varying input data. This paper proposes an innovative, fully distributed, and asynchronous subgradient optimization algorithm for the Lagrangean relaxation of the MOP, which can run online in fully decentralized P2P systems, and integrates it with a distributed heuristic that can achieve sound hot-start states for fast response to varying network structures.

Ant Colony Optimization (ACO) is a class of metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This article is composed of three parts. The first one frames ACO in current trends of research on metaheuristics for combinatorial optimization. The second outlines some current research within the ACO community, reporting recent results obtained on different problems, while the third part focuses on a particular research line, named ANTS, providing some details on the algorithm and presenting results recently obtained on a prototypical strongly constrained problem: the set partitioning problem.

Abstract This article proposes a new transformation of undirected arc‐routing problems into equivalent node‐routing problems, with emphasis on the transformation of Capacitated Arc Routing Problems (CARP) into Capacitated Vehicle Routing Problems (CVRP). For this last case, an analogue transformation has already been proposed in Pearn et al., where each required CARP edge is mapped onto a triplet of CVRP nodes. In our case, only two CVRP nodes are needed for every CARP required edge. The transformed instances have a structure and a dimension that make most CARP benchmarks solvable by state of the art CVRP techniques. We thus propose a general purpose transformation of arc into node‐routing problems and new results on lower bounds and exact methods for CARP instances. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 47(1), 52–60 2006

This article introduces two new techniques for solving the Quadratic Assignment Problem. The first is a heuristic technique, defined in accordance with the Ant System metaphor, and includes as a distinctive feature the use of a new lower bound at each constructive step. The second is a branch-and-bound exact approach, containing some elements introduced in the Ant algorithm. Computational results prove the effectiveness of both approaches.

In recent years, there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures that can solve complex problems. We describe a distributed heuristic algorithm that was inspired by the observation of the behavior of ant colonies, and we propose its use for the quadratic assignment problem. The results obtained in solving several classical instances of the problem are compared with those obtained from other evolutionary heuristics to evaluate the quality of the proposed system.

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 main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

This paper proposes a system based on a parallel genetic algorithm with enhanced encoding and operational abilities. The system, used to evolve feedforward artificial neural networks, has been applied to two widely different problem areas: Boolean function learning and robot control. It is shown that the good results obtained in both cases are due to two factors: first, the enhanced exploration abilities provided by the search-space reducing evolution of both coding granularity and network topology, and, second, the enhanced exploitational abilities due to a recently proposed cooperative local optimizing genetic operator.