Algebraic Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem With Total Flowtime Criterion

Santucci, Valentino; Baioletti, Marco; Milani, Alfredo

doi:10.1109/tevc.2015.2507785

Cited by 98 publications

(29 citation statements)

References 29 publications

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“…Next, we test the performance of the trained neural network scheduler (denoted by NNS) for solving the scheduling problem by comparing with the NEH heuristic, Suliman heuristic, and five state-of-the-art metaheuristic algorithms including a shuffled complex evolution algorithm (SCEA) [32] , an algebraic differential evolution (ADE) algorithm [33] , a teaching–learning based optimization (TLBO) algorithm [34] , a biogeography-based optimization (BBO) algorithm [35] , [36] , and a discrete water wave optimization (WWO) algorithm [15] , [37] . Before applying the network to real-world instances, we select 50 instances with different sizes from the training instances, use the above five metaheuristics to solve each of them, and select the best solution obtained by them as the label of the instance.…”

Section: Computational Resultsmentioning

confidence: 99%

Real-time neural network scheduling of emergency medical mask production during COVID-19

Liao

Karataş

et al. 2020

Applied Soft Computing

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During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that must be processed within a short response time. It is of critical importance for the manufacturer to schedule and reschedule mask production tasks as efficiently as possible. However, when the number of tasks is large, most existing scheduling algorithms require very long computational time and, therefore, cannot meet the needs of emergency response. In this paper, we propose an end-to-end neural network, which takes a sequence of production tasks as inputs and produces a schedule of tasks in a real-time manner. The network is trained by reinforcement learning using the negative total tardiness as the reward signal. We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China. Computational results show that the neural network scheduler can solve problem instances with hundreds of tasks within seconds. The objective function value obtained by the neural network scheduler is significantly better than those of existing constructive heuristics, and is close to those of the state-of-the-art metaheuristics whose computational time is unaffordable in practice.

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Section: Computational Resultsmentioning

confidence: 99%

Real-time neural network scheduling of emergency medical mask production during COVID-19

Liao

Karataş

et al. 2020

Applied Soft Computing

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“…Different problems are solved using Differential Evolution algorithm such as scheduling and resource allocation [27][28][29][30][31][32], clustering [33,34], scheduling of the hydro power generator [31]. Santucci V. et al [28], proposed Differential Evolution for variation flow-shop scheduling problem with the overall flow time criterion. For the mutation operator, they used the biased selection strategy, mimetic restart procedure, and heuristic based initialization.…”

Section: Related Workmentioning

confidence: 99%

Differential Evolution Algorithm for Optimizing Virtual Machine Placement Problem in Cloud Computing

Adamuthe¹,

Patil²

2018

IJISA

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Primary concern of any cloud provider is to improve resource utilization and minimize cost of service. Different mapping relations among virtual machines and physical machines effect on resource utilization, load balancing and cost for cloud data center. Paper addresses the virtual machine placement as optimization problem with resource constraints on CPU, memory and bandwidth. In experimentations, datasets are formed using random data generator. Paper presents random fit algorithm, best fit algorithm based on resource wastage and an evolutionary algorithm-Differential Evolution. Paper presents results of Differential Evolution algorithm with three different mutation approaches. Results show that Differential Evolution algorithm with DE/best/2 mutation operator works efficient than basic DE, best fit and random fit algorithms.

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“…Differential evolution (DE), proposed by Storn and Price [43], is a population-based optimizer. It is one of the most popular paradigms of evolutionary algorithms and has been successfully applied to solve different kinds of optimization problems [44]- [48]. Suppose that an optimization problem is to minimize the objective function ( ).…”

Section: Concepts Of Differential Evolutionmentioning

confidence: 99%

A Two-Phase Differential Evolution for Uniform Designs in Constrained Experimental Domains

Wang

Sun

et al. 2017

IEEE Trans. Evol. Computat.

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In many real-world engineering applications, a uniform design needs to be conducted in a constrained experimental domain that includes linear/nonlinear and inequality/equality constraints. In general, these constraints make the constrained experimental domain small and irregular in the decision space. Therefore, it is difficult for current methods to produce a predefined number of samples and make the samples distribute uniformly in the constrained experimental domain. This paper presents a two-phase differential evolution for uniform designs in constrained experimental domains. In the first phase, considering the constraint violation as the fitness function, a clustering differential evolution is proposed to guide the population toward the constrained experimental domain from different directions promptly. As a result, a predefined number of samples can be obtained in the constrained experimental domain. In the second phase, maximizing the minimum Euclidean distance among samples is treated as another fitness function. By optimizing this fitness function, the samples produced in the first phase can be scattered uniformly in the constrained experimental domain. The performance of the proposed method has been tested and compared with another state-of-the-art method. Experimental results suggest that our method is significantly better than the compared method in the uniform designs of a new type of automotive crash box and five benchmark test problems. Moreover, the proposed method could be considered as a

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Algebraic Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem With Total Flowtime Criterion

Cited by 98 publications

References 29 publications

Real-time neural network scheduling of emergency medical mask production during COVID-19

Real-time neural network scheduling of emergency medical mask production during COVID-19

Differential Evolution Algorithm for Optimizing Virtual Machine Placement Problem in Cloud Computing

A Two-Phase Differential Evolution for Uniform Designs in Constrained Experimental Domains

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