Towards a heterogeneous and adaptive parallel Branch-and-Bound algorithm

Chakroun, Imen; Melab, Nouredine

doi:10.1016/j.jcss.2014.06.012

Cited by 12 publications

(9 citation statements)

References 12 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the application of an LP solver to partially fixed problems has been previously studied (see, e.g., [21], [22], and [23]), the present work accelerates the heuristic by using a tailor-made Gurobi simplex algorithm for piecewise linear convex functions and the parallelization. The justification for the parallelization of combinatorial problems can be found in [24], [25], and [26].…”

Section: Mixed-integer Linear Programming Modelmentioning

confidence: 99%

Energy Optimization of Robotic Cells

Bukata

Šůcha

Hanzálek

et al. 2017

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

This study focuses on the energy optimization of industrial robotic cells, which is essential for sustainable production in the long term. A holistic approach that considers a robotic cell as a whole toward minimizing energy consumption is proposed. The mathematical model, which takes into account various robot speeds, positions, power-saving modes, and alternative orders of operations, can be transformed into a mixed-integer linear programming formulation that is, however, suitable only for small instances. To optimize complex robotic cells, a hybrid heuristic accelerated by using multi-core processors and the Gurobi simplex method for piecewise linear convex functions is implemented. The experimental results showed that the heuristic solved 93 % of instances with a solution quality close to a proven lower bound. Moreover, compared with the existing works, which typically address problems with 3 to 4 robots, this study solved real-size problem instances with up to 12 robots and considered more optimization aspects. The proposed algorithms were also applied on an existing robotic cell inŠkoda Auto. The outcomes, based on simulations and measurements, indicate that, compared with the previous state (at maximal robot speeds and without deeper power-saving modes), the energy consumption can be reduced by about 20 % merely by optimizing the robot speeds and applying power-saving modes. All the software and generated data sets used in this research are publicly available.Index Terms-robotic cells, energy optimization, holistic approach, Digital Factory, Industry 4.0, parallel hybrid heuristic, mixed-integer linear programming, simplex method for piecewise linear convex functions

show abstract

Section: Mixed-integer Linear Programming Modelmentioning

confidence: 99%

Energy Optimization of Robotic Cells

Bukata

Šůcha

Hanzálek

et al. 2017

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

show abstract

“…Another branch-and-bound implementation is described in [8] using multi-GPU systems. While the previous papers are related to multi-CPU systems on one hand and to multi-GPU systems on another hand, [13] implements a Branch-and-Bound for heterogeneous architectures (both multi-CPU systems with GPU accelerators).…”

Section: Multithreaded Branch-and-boundmentioning

confidence: 99%

Solving 0-1 Quadratic Problems with Two-Level Parallelization of the BiqCrunch Solver

Coti

Leclercq

Roupin

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-In this paper we present MLTBiqCrunch, a hierarchically parallelized version of the open-source solver BiqCrunch [1]. More precisely, this version has two levels of parallelization: a coarse grain, assigning a thread to a node evaluation and a fine grain, parallelizing a node evaluation when some threads are not busy. We present experiments on some classical binary quadratic optimization problems with comparison of their scalability and raw performance. In particular, we obtain a superlinear speedup for some of the most difficult instances.

show abstract

“…Sub-problems manager Although parallel computation has been widely studied for structural analysis (Adeli et al, 1993), integer programming (Wah et al, 1985), and Parallel B&B (McKeown et al, 1991;Clausen and Perregaard, 1999;Crainic et al, 2006;Ismail et al, 2014), proposals on parallel computing in scheduling are scarce and based on the parallelization of the B&B procedure (Perregaard and Clausen, 1998;Chakroun and Melab, 2015), but not on the subdivision of the graph.…”

Section: Database Of Problemsmentioning

confidence: 99%

A Parallel Branch and Bound Algorithm for the Resource Leveling Problem with Minimal Lags

Ponz-Tienda

Salcedo-Bernal

Pellicer

2016

Computer aided Civil Eng

View full text Add to dashboard Cite

The efficient use of resources is a key factor to minimize the cost while meeting time deadlines and quality requirements; this is especially important in construction projects where field operations make fluctuations of resources unproductive and costly. Resource Leveling Problems (RLP) aim to sequence the construction activities that maximize the resource consumption efficiency over time, minimizing the variability. Exact algorithms for the RLP have been proposed throughout the years to offer optimal solutions; however, these problems require a vast computational capability (“combinatorial explosion”) that makes them unpractical. Therefore, alternative heuristic and metaheuristic algorithms have been suggested in the literature to find local optimal solutions, using different libraries to benchmark optimal values; for example, the Project Scheduling Problem LIBrary for minimal lags is still open to be solved to optimality for RLP. To partially fill this gap, the authors propose a Parallel Branch and Bound algorithm for the RLP with minimal lags to solve the RLP with an acceptable computational effort. This way, this research contributes to the body of knowledge of construction project scheduling providing the optimums of 50 problems for the RLP with minimal lags for the first time, allowing future contributors to benchmark their heuristics methods against exact results by obtaining the distance of their solution to the optimal values. Furthermore, for practitioners, the time required to solve this kind of problem is reasonable and practical, considering that unbalanced resources can risk the goals of the construction project.

show abstract

Towards a heterogeneous and adaptive parallel Branch-and-Bound algorithm

Cited by 12 publications

References 12 publications

Energy Optimization of Robotic Cells

Energy Optimization of Robotic Cells

Solving 0-1 Quadratic Problems with Two-Level Parallelization of the BiqCrunch Solver

A Parallel Branch and Bound Algorithm for the Resource Leveling Problem with Minimal Lags

Contact Info

Product

Resources

About