A modified ant-colony optimisation algorithm to minimise the completion time variance of jobs in flowshops

Krishnaraj, J.; Pugazhendhi, S.; Rajendran, Chandrasekharan; Thiagarajan, S.

doi:10.1080/00207543.2011.613872

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…The main meta-heuristics included the simulated annealing algorithm (Osman and Potts 1989;Li and Mcmahon 2007;Laha and Chakraborty 2010), the genetic algorithm (GA) (Tseng and Lin 2009;Mohammadi, Ghomi, and Jafari 2011;Yuh-Min et al 2012), the particle swarm optimisation algorithm (PSO) (Li, Wang, and Liu 2008;Zhao et al 2010;Damodaran, Rao, and Mestry 2013), the ant colony optimisation (ACO) algorithm (Chang et al 2008;Tavares Neto and Godinho Filho 2011;Ahmadizar 2012), the tabu search algorithm (Grabowski and Wodecki 2004;Solimanpur and Elmi 2011;Gao, Chen, and Deng 2013), the iterated local search algorithm (Dong, Huang, and Chen 2009;El-Bouri 2012;Dong et al 2013), the shuffled frog leaping algorithm (Rahimi-Vahed and Mirzaei 2008;Rahimi-Vahed et al 2009;Pan et al 2011) and the estimation of distribution algorithm (Liu, Gao, and Pan 2011;Zhang and Li 2011;Wang et al 2013). There is also published research addressing the PFSP with ACO (Mirabi 2011;Krishnaraj et al 2012;Tzeng, Chen, and Chen 2012) and opposition-based differential evolution algorithms (Li and Yin 2013). Improvement heuristics could usually obtain fairly satisfactory solutions; however, the solution processes were always time-consuming, and the solutions varied dramatically according to the structures and parameters.…”

Section: Introductionmentioning

confidence: 99%

A shuffled complex evolution algorithm with opposition-based learning for a permutation flow shop scheduling problem

Zhao

Zhang

Wang

et al. 2014

International Journal of Computer Integrated Manufacturing

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The permutation flow shop scheduling problem (PFSP) is a typical non-deterministic polynomial-time hard problem, which has wide engineering applications, and performs an important function in manufacturing fields. In this paper, an improved shuffled complex evolution algorithm with opposition-based learning (SCE-OBL) was proposed to obtain the optimal makespan for permutation flow shop scheduling. The OBL strategy was used to improve the population quality and accelerate the convergence speed. The theoretical analysis demonstrated that the improved algorithm converged to optimum with a probability of 1. Moreover, the largest-order-value mechanism was used in the combinational optimisation problem to change the variables in the continuous domain to discrete variables, and job-based representation was adopted for encoding the solution of the PFSP. Twenty-nine typical instances were then used to test the performance of the SCE-OBL, and the computational results showed that the SCE-OBL algorithm could obtain better solutions for the PFSP than other algorithms.

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Section: Introductionmentioning

confidence: 99%

A shuffled complex evolution algorithm with opposition-based learning for a permutation flow shop scheduling problem

Zhao

Zhang

Wang

et al. 2014

International Journal of Computer Integrated Manufacturing

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“…It has been subsequently applied in the manufacturing scheduling context as it is stated to be an appropriate objective for just-in-time production systems, or any other situation where a uniform treatment of the jobs is desirable (see, e.g., in [21][22][23][24]). In the flow shop/job shop scheduling context, it has been employed by [25][26][27][28][29][30][31][32].…”

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confidence: 99%

Linking Scheduling Criteria to Shop Floor Performance in Permutation Flowshops

Framiñán

Leisten

2019

Algorithms

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The goal of manufacturing scheduling is to allocate a set of jobs to the machines in the shop so these jobs are processed according to a given criterion (or set of criteria). Such criteria are based on properties of the jobs to be scheduled (e.g., their completion times, due dates); so it is not clear how these (short-term) criteria impact on (long-term) shop floor performance measures. In this paper, we analyse the connection between the usual scheduling criteria employed as objectives in flowshop scheduling (e.g., makespan or idle time), and customary shop floor performance measures (e.g., work-in-process and throughput). Two of these linkages can be theoretically predicted (i.e., makespan and throughput as well as completion time and average cycle time), and the other such relationships should be discovered on a numerical/empirical basis. In order to do so, we set up an experimental analysis consisting in finding optimal (or good) schedules under several scheduling criteria, and then computing how these schedules perform in terms of the different shop floor performance measures for several instance sizes and for different structures of processing times. Results indicate that makespan only performs well with respect to throughput, and that one formulation of idle times obtains nearly as good results as makespan, while outperforming it in terms of average cycle time and work in process. Similarly, minimisation of completion time seems to be quite balanced in terms of shop floor performance, although it does not aim exactly at work-in-process minimisation, as some literature suggests. Finally, the experiments show that some of the existing scheduling criteria are poorly related to the shop floor performance measures under consideration. These results may help to better understand the impact of scheduling on flowshop performance, so scheduling research may be more geared towards shop floor performance, which is sometimes suggested as a cause for the lack of applicability of some scheduling models in manufacturing.

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“…Li and Yang [21] proposed a new approach of optimization and optical design for a miniature projector with two liquid lenses via integrating damped least square with the modified ant colony algorithm. Krishnaraj et al [22] proposed a modified ant-colony optimisation algorithm (MACO-I and MACO-II) to solve the permutation flowshop scheduling problem. Yoo and Han [23] proposed a modified ant colony optimization (MACO) algorithm implementing a new definition of pheromone and a new cooperation mechanism between ants.…”

Section: Introductionmentioning

confidence: 99%

Research on an Improved Multi-Population Ant Colony Optimization Algorithm and its Application

Wei¹,

Si²

2016

IJDTA

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A modified ant-colony optimisation algorithm to minimise the completion time variance of jobs in flowshops

Cited by 5 publications

References 33 publications

A shuffled complex evolution algorithm with opposition-based learning for a permutation flow shop scheduling problem

A shuffled complex evolution algorithm with opposition-based learning for a permutation flow shop scheduling problem

Linking Scheduling Criteria to Shop Floor Performance in Permutation Flowshops

Research on an Improved Multi-Population Ant Colony Optimization Algorithm and its Application

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