Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility

Vital-Soto, Alejandro; Azab, Ahmed; Baki, Mohammed Fazle

doi:10.1016/j.jmsy.2019.11.010

Cited by 48 publications

(13 citation statements)

References 61 publications

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“…From the small-scale instances, it can be seen that when the range of the processing time of operations is enlarged from (1,5) to (1,10), the running time of CPLEX increases significantly. While the running time of the genetic algorithm, the simulated annealing algorithm, and L-F algorithm does not change significantly.…”

Section: Small-scale Instancesmentioning

confidence: 99%

“…For meeting the unpredictable demand, manufacturing companies often acquire more manufacturing equipment. Scheduling jobs to be processed on these manufacturing equipment increase the complexity of business operations [1]. Job scheduling is mainly used to assist decision makers in deciding the processing sequence of jobs and the allocation of machines.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Algorithms for MapReduce Scheduling Problem with Open-Map Task and Series-Reduce Tasks

Zheng

Wang

et al. 2020

Scientific Programming

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Based on the classical MapReduce concept, we propose an extended MapReduce scheduling model. In the extended MapReduce scheduling problem, we assumed that each job contains an open-map task (the map task can be divided into multiple unparallel operations) and series-reduce tasks (each reduce task consists of only one operation). Different from the classical MapReduce scheduling problem, we also assume that all the operations cannot be processed in parallel, and the machine settings are unrelated machines. For solving the extended MapReduce scheduling problem, we establish a mixed-integer programming model with the minimum makespan as the objective function. We then propose a genetic algorithm, a simulated annealing algorithm, and an L-F algorithm to solve this problem. Numerical experiments show that L-F algorithm has better performance in solving this problem.

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Section: Small-scale Instancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic Algorithms for MapReduce Scheduling Problem with Open-Map Task and Series-Reduce Tasks

Zheng

Wang

et al. 2020

Scientific Programming

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“…Alejandro Vital-Soto et al [10] presented the Mathematical modeling and the hybridized bacterial foraging optimization algorithm (HBFOA) for solving the FJSP with sequencing flexibility. The steps of the bacterial foraging optimization algorithm are based on the food foraging behavior of E. coli bacteria.…”

Section: Introductionmentioning

confidence: 99%

The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

Phu-ang

2021

ECTI-CIT

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This paper proposes a hybrid algorithm that applied the firefly algorithm (FA) with the new idea labeled as the fuzzy movement method for solving a complex scheduling problem called the flexible job shop scheduling. The step of the proposed algorithm is similar to the original FA, which is based on the concept of flashing behavior to attract the other fireflies. In order to improve the efficiency of the FA algorithm for the FJSP, the proposed algorithm introduces three new ideas. In the first idea, the genetic algorithm (GA) is used to generate the high quality of the initial population. Next, the self-adaptive roulette wheel selection which embedded in the GA introduced to increase the diversity of the machine selection process. Finally, the fuzzy movement method is presented to enhance the work balancing ability between the high workload machines and the low workload machines. The proposed algorithm has been evaluated with a benchmark data set and compared to the other algorithm. The experimental results demonstrate that the proposed algorithm can effectively solve the flexible job shop scheduling problem.

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“…As a fresh comer of swarm intelligence (SI), bacterial foraging optimization (BFO) [ 10 ] has gotten much attention from different areas, and widely used in many real-world problems including supply chain optimization problem [ 11 ], flexible job-shop scheduling problem [ 12 ], and vehicle routing problem [ 13 ]. And a modified BFO with linear decreasing chemotaxis step (BFO-LDC) has been already successfully applied to solve the PO [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Modified Bacterial Foraging Optimization for Fuzzy Mean-Semivariance-Skewness Portfolio Selection

Gao

Huang

2020

Lecture Notes in Computer Science

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In this paper, a novel bacterial foraging optimization with decreasing chemotaxis step combined with sine function is employed to solve a fuzzy portfolio optimization with a modified mean-semivariance-skewness model which includes the transaction fee and no short sales. First of all, a decreasing chemotaxis step combined with sine function (BFO-SDC) takes the place of constant chemotaxis step size. It is a nonlinear decreasing strategy at every iteration of the algorithm. And then, the variance is replaced by semivariance and skewness is taken into account in order to generate asymmetry of return distributions to overcome the inadequacy of the standard mean-variance model. Finally, fuzzy variables are used to express the uncertain and imprecise elements in the decision-making process. The results of the simulation show that the model can be solved more reasonably and effectively by BFO-SDC than the original bacterial foraging optimization.

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Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility

Cited by 48 publications

References 61 publications

Heuristic Algorithms for MapReduce Scheduling Problem with Open-Map Task and Series-Reduce Tasks

Heuristic Algorithms for MapReduce Scheduling Problem with Open-Map Task and Series-Reduce Tasks

The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

Modified Bacterial Foraging Optimization for Fuzzy Mean-Semivariance-Skewness Portfolio Selection

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