Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan

Naderi, Bahman; Tavakkoli–Moghaddam, Reza; Khalili, Majid

doi:10.1016/j.knosys.2009.06.002

Cited by 99 publications

(39 citation statements)

References 22 publications

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“…EA is reported, also, to outperform Simulated Annealing (SA) and many well-recognized constructive heuristics as well as to yield more effective solutions than PSO [64]. Moreover, the following statements have been reported in some other studies regarding the EA: It generates feasible and near-optimal results within shorter computational times compared to the test algorithms, including Beam-ACO (Ant Colony Optimization),…”

Section: The Proposed Solution Proceduresmentioning

confidence: 89%

“…Network after random distribution of sensors [61], machine-cell formation and layout problem in cellular manufacturing system in which parts with different characteristics and needs are assigned to the cells of distinctively divided machines [62], optimization of tool path planning in 5-axis CNC machining to reduce the geometrical deviations of the ruled surfaces [63], scheduling the flow shop problem [64] and computing numerical solutions to the inverse kinematic problem of robotic manipulator [65].…”

Section: The Proposed Solution Proceduresmentioning

confidence: 99%

“…It is also interesting to note that Electromagnetism-like algorithm has been found effective to solve system design problems in hybrid forms with other heuristic algorithm [62,64]; and metaheuristic algorithms such as Particle Swarm Optimization, PSO, [71], Simulated Annealing, SA, [64,67]; as well as with conventional optimization methods [65] and in improved formats of EA algorithm [60,68].…”

Section: The Proposed Solution Proceduresmentioning

confidence: 99%

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An Electromagnetism-like Algorithm to Solve Three-dimensional Highway Alignment Problem

Mohammadi

Poorzahedy

2018

Scientia Iranica

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Road alignment design is an important determinant of the development cost of road networks.On the one side, it affects road construction and maintenance costs, which constitute a significant part of country-wide infrastructure development, management and operation budget each year.On the other side, it affects road user-related costs of travel time and vehicle use, which comprise a significant portion of the total transportation cost. This study adapts the existing Electromagnetism-like meta-heuristic algorithm to solve a three-dimensional highway alignment problem, which explores and finds a good route between two given points on a terrain. It detects the potentials of the given initial routes, which are enhanced and shaped toward better positions by the help of the local and global search. The final good solution is, then, fine-tuned for a better alignment. Several example problems are designed to show the behavior of the algorithm. The results show that the algorithm satisfactorily maneuvers to by-pass obstacles, and build highway structures where necessary. The set of the example problems in this paper may also serve to found a basis for evaluation of the performance of alternative algorithms.

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Section: The Proposed Solution Proceduresmentioning

confidence: 89%

Section: The Proposed Solution Proceduresmentioning

confidence: 99%

Section: The Proposed Solution Proceduresmentioning

confidence: 99%

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An Electromagnetism-like Algorithm to Solve Three-dimensional Highway Alignment Problem

Mohammadi

Poorzahedy

2018

Scientia Iranica

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“…Although the EMO algorithm shares some characteristics to PSO and ACO, recent works have exhibited its better accuracy regarding optimal parameters [18 -21], yet showing convergence [22]. In recent works, EMO has been used to solve different sorts of engineering problems such as flow-shop scheduling [23], communications [24], vehicle routing [25], array pattern optimization in circuits [26], neural network training [27], image processing [28] and control systems [29]. Although EMO algorithm shares several characteristics to other evolutionary approaches, recent works (see [18][19][20][21]) have exhibited a better EMO's performance in terms of computation time and precision when it is compared with other methods such as GA, PSO and ACO.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Thresholding algorithm using electromagnetism optimization

et al. 2014

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Segmentation is one of the most important tasks in image processing. It consist in classify the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for multilevel thresholding is computationally expensive since they exhaustively search the best values to optimize the objective function. Under such conditions, the use of optimization evolutionary approaches has been extended. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. In this paper, a multilevel thresholding (MT) algorithm based on the EMO is introduced. The approach combines the good search capabilities of EMO algorithm with objective functions proposed by the popular MT methods of Otsu and Kapur. The algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu's or Kapur's method. Guided by these objective values the set of candidate solutions are evolved through the EMO operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image in a reduced number of iterations. Experimental results show performance evidence of the implementation of EMO for digital image segmentation.

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“…A difficulty with the multiobjective scheduling problems arises from that the objectives are often in conflict. For example, among the four objectives mentioned above, the two former focus on improving machine utilization and productivity that correspond to a production manager's interests, whereas the latter objectives are mainly regarded as measures of conformity with due dates that are related to customer satisfaction (Naderi et al, 2010). Therefore, no single solution would simultaneously accomplish the multiple objectives and the compromise one should find as a trade-off in the conflicting circumstances.…”

Section: Introductionmentioning

confidence: 99%

Compromising Multiple Objectives in Production Scheduling: A Data Mining Approach

Hwang¹,

Lee²

2014

Management Science and Financial Engineering

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In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.

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Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan

Cited by 99 publications

References 22 publications

An Electromagnetism-like Algorithm to Solve Three-dimensional Highway Alignment Problem

An Electromagnetism-like Algorithm to Solve Three-dimensional Highway Alignment Problem

A Multilevel Thresholding algorithm using electromagnetism optimization

Compromising Multiple Objectives in Production Scheduling: A Data Mining Approach

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