Optimization technique by genetic algorithms for international logistics

Takeyasu, Kazuhiro; Kainosho, Masaaki

doi:10.1007/s10845-013-0823-1

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…Here the material needs more attention and therefore it has an important role in the optimization. There are also some attempts to give mathematical formulation to international logistic type problems (Takeyasu and Kainosho 2014 [11]). This formules are too general in the transportation type and can not optimize to the driver states.…”

Section: Historymentioning

confidence: 99%

Truck routing and scheduling

Csehi

Farkas

2016

Cent Eur J Oper Res

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The problem is part of a complex software solution for truck itinerary construction for one of the largest public road transportation companies in the EU. In practice a minor improvement on the operational cost per tour can decide whether a freight services company is profitable or not. Thus the optimization of routes has key importance in the operation of such companies. If parking places and gas stations are included in the planning then it is NP hard to find an optimal solution. This means that for long range tours an approximately optimal solution for refueling has to be given within an acceptable running time. Also the corridoring of the trucks is an important problem so that we try to optimize the performance, hence tours cannot be recalculated at each data arrival. The vehicle assignment part of this work is already finished and applied with very good results. The remaining part is subject of an ongoing research which started at January 2014. The company started to apply and test our product in the beginning of 2015 under increased human supervision. As a consequence of the project a large cost saving is anticipated by the company.

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Section: Historymentioning

confidence: 99%

Truck routing and scheduling

Csehi

Farkas

2016

Cent Eur J Oper Res

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“…It has been widely used to solve various optimization problems such as time dependent inventory routing problem (Cho et al 2014), yard crane scheduling , optimization for international logistics (Takeyasu and Kainosho 2014), scheduling of JIT cross-docking systems ), straight and U-shaped assembly line balancing (Alavidoost et al 2014), hybrid flow shop scheduling with single and batch processing machines (Li et al 2014), etc. In addition many researchers have applied genetic algorithms to solve the parallel machine scheduling problems.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

Afzalirad

Shafipour

2015

J Intell Manuf

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This study addresses a resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions. Majority of the traditional scheduling problems in parallel machine environment deal with machine as the only resource. However, other resources such as labors, tools, jigs, fixtures, pallets, dies, and industrial robots are not only required for processing jobs but also are often restricted. Considering other resources makes the scheduling problems more realistic and practical to implement in manufacturing environments. First, an integer mathematical programming model with the objective of minimizing makespan is developed for this problem. Noteworthy, due to NP-hardness of the considered problem, application of meta-heuristic is avoidable. Furthermore, two new genetic algorithms including a pure genetic algorithm and a genetic algorithm along with a heuristic procedure are proposed to tackle this problem. With regard to the fact that appropriate design of the parameters has a significant effect on the performance of algorithms, hence, we calibrate the parameters of these algorithms by using the response surface method. The performance of the proposed algorithms is evaluated by a number of numerical examples. The computational results demonstrated that the proposed genetic algorithm is an effective and appropriate approach for our investigated problem.

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“…And the related parameters of FSVM are also critical for assuring the recognition accuracy. The parameters needed to be optimized include the degree of polynomial kernel d, the width of Gaussian kernel γ , the combination coefficient of hybrid kernel β and the penalty factor of FSVM C. As a stochastic optimization algorithm simulating the natural selection and genetic mechanism in the process of biological evolution, GA has excellent global searching ability and has been widely used to solve optimization problems (Whitley 1994;Ventura and Yoon 2013;Ahmed et al 2014;Takeyasu and Kainosho 2014). Consequently, it is employed to achieve parameters optimization and input feature choice for FSVM-based classifiers.…”

Section: Optimizing the Input Feature Set And Parameters Of Fsvm Usinmentioning

confidence: 99%

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

2015

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Accurate control chart patterns recognition (CCPR) plays an essential role in the implementation of control charts. However, it is a challenging problem since nonrandom control chart patterns (CCPs) are normally distorted by "common process variations". In this paper, a novel method of CCPR by integrating fuzzy support vector machine (SVM) with hybrid kernel function and genetic algorithm (GA) is proposed. Firstly, two shape features and two statistical features that do not depend on the distribution parameters and number of samples are presented to explicitly describe the characteristics of CCPs. Then, a novel multiclass method based on fuzzy SVM with a hybrid kernel function is proposed. In this method, the influence of outliers on classification accuracy of SVM-based classifiers is weakened by assigning a degree of membership for every training sample. Meanwhile, a hybrid kernel function combining Gaussian kernel and polynomial kernel is adopted to further enhance the generalization ability of the classifiers. To solve the issue of features selection and parameters optimization, GA is used to simultaneously optimize the input features subsets and parameters of fuzzy SVM-based classifier. Finally, several simulation experiments and a real example are addressed to validate the feasibility and effectiveness of the proposed methodology. And the results of simulation experiments demonstrate that it can achieve excellent performance for CCPR and outperforms other approaches, such as learning B Pingyu Jiang vector quantization network, multi-layer perceptron network, probability neural network, fuzzy clustering and SVM, in term of recognition accuracy. The results of the practical cases manifest that the proposed method has application potential for solving the problem of control chart interpretation in real-world.

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Optimization technique by genetic algorithms for international logistics

Cited by 14 publications

References 8 publications

Truck routing and scheduling

Truck routing and scheduling

Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

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