Evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem

Yazdani, Mehdi; Zandieh, M.; Tavakkoli–Moghaddam, Reza

doi:10.1007/s12597-019-00395-y

Cited by 46 publications

(38 citation statements)

References 37 publications

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“…VNS was composed of two neighborhood search procedures and a restarting mechanism. Yazdani et al 16 addressed the DRCFJSP with the objective of minimizing the makespan. The authors presented two efficient meta-heuristic algorithms, SA and vibration damping optimization (VDO), to solve the DRCFJSP.…”

Section: Introductionmentioning

confidence: 99%

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Guo

et al. 2018

Advances in Mechanical Engineering

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In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker's learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a threedimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

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Section: Introductionmentioning

confidence: 99%

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Guo

et al. 2018

Advances in Mechanical Engineering

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“…In this regard, combining all possible scenarios and testing them require a lot of times. To solve this problem and to reduce the number of tests, the Taguchi optimization method is employed [52]. To do so, 32 different modes are selected using the Minitab software and the model is then implemented to achieve the highest possible accuracy among these combinations.…”

Section: Evaluation Of the Proposed Modelmentioning

confidence: 99%

Development of an Ensemble Learning-based intelligent model for Stock Market Forecasting

Nezhad

Minaei-Bidgoli

2019

Scientia Iranica

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The use of artificial intelligence-based models have shown that the market is predictable despite its uncertainty and unstable nature. The most important challenge of the proposed models in the stock market is the accuracy of the results and increasing the forecasting efficiency. Another challenge, which is a prerequisite for making decision and using the results of the forecast for profitability of transactions, is to forecast the trend of stock price movements in forecasting price. To overcome the mentioned challenges, this paper employs ensemble learning (EL) model using intelligence-based learners and metaheuristic optimization methods to maximize the improvement of forecasting performance. In addition, in order to consider the direction of price change in stock price forecasting, a two-stage structure is used. In the first stage, the next movement of the stock price (increase or decrease) is forecasted and its outcome is then employed to forecast the price in the second stage. In both stages, genetic algorithm (GA) and particle swarm optimization (PSO) technique are used to optimize the aggregation results of the base learners. The evaluation results of stock market dataset show that the proposed model has higher accuracy compared to other models used in the literature.

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“…It is possible to decompose the critical path into blocks, that is the maximal sequence of adjacent critical operations on the same machine [16][25] [40]. Several neighborhood structures are defined for the scheduling problems [48]. For example, in N 1 structure, one machine is chosen and all possible swaps for the jobs that are assigned to this machine are applied.…”

Section: Neural Network Application For Solving Scheduling Problems mentioning

confidence: 99%

A Neural Network-based Hybrid Method to Generate Feasible Neighbors for Flexible Job Shop Scheduling Problem

Teymourifar¹,

Öztürk²

2018

ujam

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In this paper, a hybrid method is proposed to generate feasible neighbors for the flexible job shop scheduling problem. Many of the optimization and artificial intelligence methods have been used to solve this important and NP-hard combinatorial problem which provide the basis for solving real-life problems. It is well-known that for such problems the hybrid methods obtain better results than the other approaches. For instance, the applied non-hybrid neural networks for the combinatorial problems, as the Hopfield neural network, usually converge early. Also, their results almost always contain large gaps. These shortcomings prevent them to find good results. Another necessity for a quality search is to find suitable neighbors of the obtained solutions, however, it is possible to create infeasible neighbors during the optimization process. The aim of this study is to overcome these deficiencies. In the suggested approach, at first, an initial solution is generated and then using the left shift heuristics, its gaps are removed. Based on the critical path and critical block concepts, 6 neighbors are constructed for the obtained solution. After the generation of each neighbor, a neural network runs and controls the constraints of the problem. If the achieved neighbor is feasible it is saved. Else if it is infeasible, the neural network tries to transform it into a feasible solution. This is done by applying penalties to the start time of the operations on the violated constraints, which shifts them to the right or the left. During this process, if there are not any violated constraints, the neural network reaches the stable condition so it stops and the obtained solution is saved as a feasible neighbor. Otherwise, after a certain number of the iterations, it stops without any feasible neighbors. Then these steps are repeated for the other created neighbors. This constraint-based process provides an effective and diverse search. Finally, the obtained neighbors, are improved using the left shift heuristics. Also to demonstrate the importance of the initial solutions, they are generated randomly and also using the Giffler and Thompson's heuristic. The comparison between the proposed approach and the methods from the literature shows that it constructs better neighbors. However, using the Giffler and Thompson heuristic to create the initial solution improves the results significantly.

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Evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem

Cited by 46 publications

References 37 publications

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Development of an Ensemble Learning-based intelligent model for Stock Market Forecasting

A Neural Network-based Hybrid Method to Generate Feasible Neighbors for Flexible Job Shop Scheduling Problem

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