An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

Zhang, Rui; Wu, Cheng

doi:10.3390/e13091708

Cited by 39 publications

(16 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simplified multi-objective genetic algorithm [13] Uniform Expected makespan Effective multiobjective Estimation of Distribution Algorithm (EDA) [14] Uniform Expected makespan & total tardiness Hybrid evolutionary algorithm [15] Uniform Expected makespan Artificial bee colony algorithm [16] Uniform; Normal; Exponent Maximum lateness Two-stage particle swarm optimization [17] Uniform; Normal; Exponent Expected total weighted tardiness Evolutionary strategy in ordinal optimization [19] Uniform; Normal; Exponent Expected makespan & total tardiness Algorithm based on artificial neural networks [18] Uniform; Normal; Exponent Expected makespan Novel parallel quantum genetic algorithm [20] Normal Expected makespan Cooperative coevolution genetic programming (CCGP) [21] Uniform Expected makespan Co-evolutionary quantum genetic algorithm [22] Normal Expected makespan Two-stage optimization [23] Uniform; Normal; Exponent Expected makespan Cooperative co-evolution algorithm (CEA) is widely used in the research area of a large scale optimization problem. Because the basic optimization strategy, i.e., "divide-and-conquer", is appropriate for the problems with the increasing solution space, various researchers studied the performance of EAs applied for combinational optimization problems.…”

Section: Methodologies Distribution(s) Objective(s) (Min)mentioning

confidence: 99%

“…To show the superiority of the proposed algorithm with more conviction, various researchers modelled the stochastic processing time not only by the uniform distribution, but also by the normal distribution and the exponential distribution. Zhang and Wu proposed an artificial bee colony algorithm for minimizing the maximum lateness of JSP [16]. Zhang and Song proposed a two-stage particle swarm optimization for minimizing the expected total weighted tardiness [17].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Sun

Lin

et al. 2019

Mathematics

View full text Add to dashboard Cite

Flexible job shop scheduling is an important issue in the integration of research area and real-world applications. The traditional flexible scheduling problem always assumes that the processing time of each operation is fixed value and given in advance. However, the stochastic factors in the real-world applications cannot be ignored, especially for the processing times. We proposed a hybrid cooperative co-evolution algorithm with a Markov random field (MRF)-based decomposition strategy (hCEA-MRF) for solving the stochastic flexible scheduling problem with the objective to minimize the expectation and variance of makespan. First, an improved cooperative co-evolution algorithm which is good at preserving of evolutionary information is adopted in hCEA-MRF. Second, a MRF-based decomposition strategy is designed for decomposing all decision variables based on the learned network structure and the parameters of MRF. Then, a self-adaptive parameter strategy is adopted to overcome the status where the parameters cannot be accurately estimated when facing the stochastic factors. Finally, numerical experiments demonstrate the effectiveness and efficiency of the proposed algorithm and show the superiority compared with the state-of-the-art from the literature.Mathematics 2019, 7, 318 2 of 20 optimization problem with two sub-problems, i.e., operation sequence and machine assignment, and was proved as an NP-hard problem [9]. MotivationVarious researchers concentrated on the effective algorithms for minimizing the maximum completion time which named makespan of FJSP. Lin and Gen provided a survey in 2018 and presented the information of scheduling, e.g., mathematical formulation and graph representations [10]. Chaudhry and Khan reviewed solution techniques and methods published for flexible scheduling and presented an overview of the research trend of flexible scheduling in 2016 [11]. However, most of the existing research assumes that the processing time of each operation on the corresponding machine is fixed and given in advance. In actual, the stochastic factors in the real-world applications cannot be ignored. In addition, the processing time appears to be particularly important [12]. Therefore, in this paper, we consider the stochastic FJSP (S-FJSP), and the stochastic processing time is modeled as three probability distributions, i.e., the normal distribution, the Gaussian distribution and the exponential distribution.In recent years, various research was studied for stochastic scheduling problems. As listed in Table 1, Lei proposed a simplified multi-objective genetic algorithm (MOGA) for minimizing the expected makespan of JSP [13]. Hao et al. proposed an effective multi-objective estimation of distribution algorithm (moEDA) for minimizing the expected makespan and the total tardiness of JSP [14]. Kundakc and Kulak proposed a hybrid evolutionary algorithm for minimizing the expected of JSP makespan as well [15]. Represented by the above studies, some researchers modelled the stochastic processing timess by the unifor...

show abstract

Section: Methodologies Distribution(s) Objective(s) (Min)mentioning

confidence: 99%

mentioning

confidence: 99%

Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Sun

Lin

et al. 2019

Mathematics

View full text Add to dashboard Cite

show abstract

“…The maximum fuzzy processing time after rescheduling is (23, 29, 38). The maximum fuzzy completion time is decreased from (26,38,50) to (23,29,38).…”

Section: New Job Insertionmentioning

confidence: 99%

“…Banharnsakun et al [27,28] employed the best-so-far ABC (B-ABC) to solve the JSP. Zhang [29] proposed an ABC algorithm for the JSP with random processing times. Wang [30,31] designed two effective ABC algorithms for mono-objective and multi-objective the FJSP.…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion

Gao

Suganthan

Pan

et al. 2016

Knowledge-Based Systems

124

View full text Add to dashboard Cite

“…Zhang and Cheng were utilized ABC algorithm for job shop scheduling problem with random processing times [55]. In this paper, the authors proposed ABC algorithm for stochastic job shop scheduling problem with the objective of minimizing the maximum lateness.…”

Section: Schedulingmentioning

confidence: 99%

A review on Artificial Bee Colony algorithm

Verma

Kumar

2013

IJET

View full text Add to dashboard Cite

In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.

show abstract

An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

Cited by 39 publications

References 34 publications

Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion

A review on Artificial Bee Colony algorithm

Contact Info

Product

Resources

About