An efficient deterministic heuristic for two-dimensional rectangular packing

Jue

2015 IEEE Global Communications Conference (GLOBECOM)

et al. 2015

In this paper, we consider the problem of routing, modulation, and spectrum assignment (RMSA) for data-flow transfers in elastic optical networks. We design a two-dimensional resource model, in which each data transfer with known data size can be assigned a rectangular block of resources that spans both the spectrum and time dimensions. Furthermore, the dynamic spectral resource allocation problem in elastic optical networks is simplified to the two-dimensional rectangle packing problem. We design a three-tuple for each rectangle placement, and develop a dynamic heuristic algorithm, Best Rectangle Fit (BRF), to efficiently schedule requests while minimizing fragmentation in both spectrum and time domains. We simulate the proposed algorithm, and the results show that the proposed RMSA algorithm (BRF) can greatly decrease blocking probability and increase spectrum utilization.

Section: A Definitionsmentioning

confidence: 99%

Section: Two Dimensional Rectangle Packing Problemmentioning

confidence: 99%

Scheduling Large Data Flows in Elastic Optical Inter-Datacenter Networks

Jue

2015 IEEE Global Communications Conference (GLOBECOM)

et al. 2015

“…Recently, some excellent hybrid algorithms were developed [25]- [27]. In particular, a class of deterministic heuristics is developed by a number of researchers and they are successful in obtaining the better results [20], [28], [29]. Leung and Zhang [30] proposed a fast layer-heuristic algorithm, which have many applications in routing problem with loading constraints [31], [32].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm for Strip Packing Problem with Rotation Constraint

Chen

2016

MATEC Web Conf.

Abstract. Strip packing is a well-known NP-hard problem and it was widely applied in engineering fields. This paper considers a two-dimensional orthogonal strip packing problem. Until now some exact algorithm and mainly heuristics were proposed for two-dimensional orthogonal strip packing problem. While this paper proposes a two-stage hybrid algorithm for it. In the first stage, a heuristic algorithm based on layering idea is developed to construct a solution. In the second stage, a great deluge algorithm is used to further search a better solution. Computational results on several classes of benchmark problems have revealed that the hybrid algorithm improves the results of layer-heuristic, and can compete with other heuristics from the literature.

“…NP-hard problems are algorithmically solvable but computationally intractable (Shapiro and Delgado-Eckert 2012). There is no exact method that can find the global optimal solutions to NP-hard problems in polynomial time, and fast approximate heuristics and metaheuristics are the popular approaches to search for highquality/practical solutions (He et al 2012).…”

Section: Introductionmentioning

confidence: 99%

An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling

2017

Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to ''learn'' from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.