Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

Zhou, Rong; Nee, A. Y. C.; Lee, H.P.

doi:10.1080/00207540701644219

Cited by 76 publications

(25 citation statements)

References 12 publications

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“…It may not be always available or requires extensive computational efforts to detect them. A similar partial restart strategy via pheromone trails was proposed for the DJSSP [136,73]. Angus and Hendtlass [110] used the old pheromone trails and modified proportionally to the maximum pheromone trail value for DTSP.…”

Section: Increasing Diversity After a Changementioning

confidence: 99%

“…The dynamic changes in dynamic VRP (DVRP) may occur on the weights of the arcs [33] or new customers may be revealed [67]. Dynamic changes occur when new jobs arrive during the execution for the dynamic job shop scheduling problem (DJSSP) [73,74]. The changes in the dynamic knapsack problem (DKP) may occur on the value and weight of items as well as the capacity of knapsack or directly to the objective function [75].…”

Section: The Generation Of Dynamicsmentioning

confidence: 99%

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A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

492

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Increasing Diversity After a Changementioning

confidence: 99%

Section: The Generation Of Dynamicsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

492

213

View full text Add to dashboard Cite

show abstract

“…The proposed approach was aimed to find the near optimal solutions in short computation time. Other methods commonly used in the scheduling problems belong to Swarm intelligence family, including ant colony optimization [36], pheromone approach [37] and chemotaxis-enhanced bacterial foraging algorithm [38].…”

Section: Introductionmentioning

confidence: 99%

Reentrant FMS scheduling in loop layout with consideration of multi loading-unloading stations and shortcuts

Rifai

Dawal

Zuhdi

et al. 2015

Int J Adv Manuf Technol

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The scheduling problem in flexible manufacturing systems (FMS) environment with loop layout configuration has been shown to be a NP-hard problem. Moreover, the improvement and modification of the loop layout add to the difficulties in the production planning stage. The introduction of multi loading-unloading points and turntable shortcut resulted on more possible routes, thus increasing the complexity. This research addressed the reentrant FMS scheduling problem where jobs are allowed to reenter the system and revisit particular machines. The problem is to determine the optimal sequence of the jobs as well as the routing options. A modified genetic algorithm (GA) was proposed to generate the feasible solutions. The crowding distance-based substitution was incorporated to maintain the diversity of the population. A set of test was applied to compare the performance of the proposed approach with other methods. Further computational experiments were conducted to assess the significance of multi loading-unloading and shortcuts in reducing the makespan, mean flow time, and tardiness. The results highlighted that the proposed model was robust and effective in the scheduling problem for both small and large size problems.

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“…Xiang and Lee found that the MAS with ant colony intelligence outperformed a MAS with a first in first out (FIFO) dispatching rule, while Renna found that the ant intelligence approach gave a solution that was comparable with a coordination approach when the dynamic changes were of low or medium frequencies. Zhou et al [19] applied ACO to a dynamic JSSP and found that it outperformed a heuristic based on the shortest processing time (SPT) while Lu and Romanowski [13] found that their version of ant colony system (ACS) outperformed well-known dispatching rules such as FIFO and SPT.…”

Section: A Related Work Using Aco For Dynamic Reschedulingmentioning

confidence: 99%

Railway platform reallocation after dynamic perturbations using ant colony optimisation

Eaton

Yang

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Abstract-Train delays at stations are a common occurrence in complex, busy railway networks. A delayed train will miss its scheduled time slot on the platform and may have to be reallocated to a new platform to allow it to continue its journey. The problem is a dynamic one because while reallocating a delayed train further unanticipated train delays may occur, changing the nature of the problem over time. Our aim in this study is to apply ant colony optimisation (ACO) to a dynamic platform reallocation problem (DPRP) using a model created from real-world train schedule data. To ensure that trains are not unnecessarily reallocated to new platforms we introduce a novel best-ant-replacement scheme that takes into account not only the objective value but also the physical distance between the original and the new platforms. Results showed that the ACO algorithm outperformed a heuristic that places the delayed train in the first available time-slot and that this improvement was more apparent with high-frequency dynamic changes.

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Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

Cited by 76 publications

References 12 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Reentrant FMS scheduling in loop layout with consideration of multi loading-unloading stations and shortcuts

Railway platform reallocation after dynamic perturbations using ant colony optimisation

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