A hybrid heuristic for the 0–1 Knapsack Sharing Problem

Haddar, Boukthir; Khemakhem, Mahdi; Hanafi, Saïd; Wilbaut, Christophe

doi:10.1016/j.eswa.2015.01.049

Cited by 22 publications

(14 citation statements)

References 50 publications

(59 reference statements)

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“…To introduce the collision resolution more formally, constraints (22) and (23) have to be shown as specialized forms of constraints (18) and (19), respectively. First, the variables u gi ai in constraints (18) and (19) are fixed for tuple T , i.e., (18) and (19) are neither binding nor violated for unselected locations and movements of tuple T , and each activity a i ∈ A T has an assigned movement or location.…”

Section: Reduced Linear Programming Problemmentioning

confidence: 99%

“…This triple defines the worst collision, i.e., the collision with the biggest time intersection, occurring in a current solution. Whether this collision should be resolved by adding a constraint of type (22) or (23) is determined by the υ * = υ n * a * i ,a * j and µ * = µ n * a * i ,a * j values. If υ * ≤ µ * (µ * ≤ υ * ), then the worst collision is resolved by adding the triple to D ≥ (D ≤ ) because it may result in smaller changes in timing after the problem is resolved with the added constraints.…”

Section: Reduced Linear Programming Problemmentioning

confidence: 99%

See 1 more Smart Citation

Energy Optimization of Robotic Cells

Bukata

Šůcha

Hanzálek

et al. 2017

IEEE Trans. Ind. Inf.

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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

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Section: Reduced Linear Programming Problemmentioning

confidence: 99%

Section: Reduced Linear Programming Problemmentioning

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

“…PSO was one of the most frequently used one. [45][46][47][48][49][50][51][52] Other incorporated algorithms included GA, [53][54][55][56][57] gravitational search algorithm, [58][59][60] back-propagation (BP) neural network algorithm, 61 and so on.…”

Section: Qieamentioning

confidence: 99%

Quantum-inspired hybrid algorithm for integrated process planning and scheduling

Liu

Wen

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantuminspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.

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“…Quantum-behaved particle swarm embodies the characteristics of superposition state and the expression of probability in quantum theory [11][12][13]. The combined effect of the two can make the particle appear more states, and then improve the diversity of the population [14].…”

Section: The Quantum-behaved Particle Swarmmentioning

confidence: 99%

Fast Convergence and Improved Particle Swarm Hybrid Optimization Algorithm

Li¹,

Zhang²

2016

IJDTA

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Aiming at the problem that the particle in the traditional particle swarm optimization algorithm is easy to fall into the local optimum and the convergence rate is slow, this paper proposed an improved particle swarm optimization algorithm. In particle swarm optimization algorithm, the advantages and disadvantages of the algorithm is directly decided by the performance of the particle, the paper introduced the chaos mechanism, enhance the ergodicity and particle will be quantized in the solution space, on the premise of ensuring diversity of solution, the particle get better global search ability. Meanwhile, based on the problem of slow convergence speed of the algorithm in the late, on the one hand to dynamically adjust the inertia weight of impact speed, makes the particle movement speed tend to be reasonable, on the other hand, using k-means algorithm to optimize progeny particle and get more reasonable clustering center, make the algorithm fast convergence. Experiments show that using improved Particle Swarm Optimization algorithm with high precision, strong stability and fast convergence.

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A hybrid heuristic for the 0–1 Knapsack Sharing Problem

Cited by 22 publications

References 50 publications

Energy Optimization of Robotic Cells

Energy Optimization of Robotic Cells

Quantum-inspired hybrid algorithm for integrated process planning and scheduling

Fast Convergence and Improved Particle Swarm Hybrid Optimization Algorithm

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