In practice, suppliers often provide retailers with forward financing to increase demand or decrease inventory. This paper proposes a new and practical joint replenishment and delivery (JRD) model by considering trade...
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In practice, suppliers often provide retailers with forward financing to increase demand or decrease inventory. This paper proposes a new and practical joint replenishment and delivery (JRD) model by considering trade credit. However, because of the complex mathematical properties of JRD, high-quality solutions to the problem have eluded researchers. We design an effective hybrid differential evolution algorithm based on simulated annealing (HDE-SA) that can resolve this non-deterministic polynomial hard problem in a robust and precise way. After determining the suitable parameters by a parameter-tuning test, we verify the performance of the HDE-SA through numerical JRD examples. Compared with the results of other popular evolutionary algorithms, results of randomly generated JRDs indicate that HDE-SA can always obtain slightly lower total costs than differentialevolutionalgorithm (DE) and genetic algorithm (GA) under different situations. Moreover, the convergence rate of the HDE-SA is higher than that of DE and GA. Thus, the proposed HDE-SA is a potential tool for the JRD with trade credit.
This paper considers the job-shop problem with release dates and due dates, with the objective of minimizing the total weighted tardiness. A hybrid DE (HDE) is presented by combining differentialevolutionalgorithm w...
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ISBN:
(纸本)9781479904716
This paper considers the job-shop problem with release dates and due dates, with the objective of minimizing the total weighted tardiness. A hybrid DE (HDE) is presented by combining differentialevolutionalgorithm with the improved critical path algorithm on a disjunctive graph model. Firstly, a job-grouping-order (JPO) rule is presented to convert the continuous values of individuals (real vectors) in DE to job permutations. Secondly, after the global exploration based on DE, the improved critical path algorithm are used in a local search in order to improve the local search ability. An extensive computational experiment carried out on instances of the literature shows the performance of the proposed HDE algorithm.
Satisfiability (SAT) problem is an NP-complete problem. Based on the analysis about it, SAT problem is translated equally into an optimization problem on the minimum of objective function. A hybriddifferential evolut...
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ISBN:
(纸本)9783642049613
Satisfiability (SAT) problem is an NP-complete problem. Based on the analysis about it, SAT problem is translated equally into an optimization problem on the minimum of objective function. A hybrid differential evolution algorithm is proposed to solve the Satisfiability problem. It makes full use of strong local search capacity of hill-climbing algorithm and strong global search capability of differentialevolutionalgorithm, which makes up their disadvantages, improves the efficiency of algorithm and avoids the stagnation phenomenon. The experiment results show that the hybridalgorithm is efficient in solving SAT problem.
Aiming at the educational investment scheduling that is a complex hard combinatorial problem between education and economy, an effective algorithm based on differentialevolution is proposed by using a special investi...
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ISBN:
(数字)9783642342400
ISBN:
(纸本)9783642342394
Aiming at the educational investment scheduling that is a complex hard combinatorial problem between education and economy, an effective algorithm based on differentialevolution is proposed by using a special investing scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the educational problems. The compertz model is established to predict the family education investment in 2008. After this, the government education investment in 2008 can be got through minimum education investment structure. Simulation results demonstrate the proposed algorithm is effective.
In the ever changing financial markets, investor's decision behaviors may change from time to time. In this paper, we consider the effect of investor's different decision behaviors on portfolio selection in fu...
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In the ever changing financial markets, investor's decision behaviors may change from time to time. In this paper, we consider the effect of investor's different decision behaviors on portfolio selection in fuzzy environment. We present a possibilistic mean-semivariance model for fuzzy portfolio selection by considering some real investment features including proportional transaction cost, fixed transaction cost, cardinality constraint, investment threshold constraints, decision dependency constraints and minimum transaction lots. To describe investor's different decision behaviors, we characterize the return rates on securities by LR fuzzy numbers with different shape parameters in the left- and right-hand reference functions. Then, we design a novel hybrid differential evolution algorithm to solve the proposed model. Finally, we provide a numerical example to illustrate the application of our model and the effectiveness of the designed algorithm.
There are lots of algorithms for optimal clustering. The main part of clustering algorithms includes the K-means, fuzzy c-means (FCM) and evolutionalgorithm. The main purpose of this paper was to research the perform...
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There are lots of algorithms for optimal clustering. The main part of clustering algorithms includes the K-means, fuzzy c-means (FCM) and evolutionalgorithm. The main purpose of this paper was to research the performance and characteristics of these three types of algorithms. One criteria (clustering validity index), namely TRW, was used in the optimisation of classification and eight real-world datasets (glass, wine, ionosphere, biodegradation, connectionist bench, hill-valley, musk, madelon datasets), whose dimension became higher, were applied. We made a performance analysis and concluded that it was easy of the K-means and FCM to fall into a local minimum, and the hybridalgorithm was found more reliable and more efficient, especially on difficult tasks with high dimension.
Having effective learning and retrieval phases of satisfiability logic in Discrete Hopfield Neural Network models ensures optimal synaptic weight management, which consequently leads to the production of optimal final...
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Having effective learning and retrieval phases of satisfiability logic in Discrete Hopfield Neural Network models ensures optimal synaptic weight management, which consequently leads to the production of optimal final neuron states. However, the problem with this model is that different initial states can affect the biasedness of the retrieval phase since the model memorizes final states without generating new ones and produces suboptimal final neuron states. To date, there is no recent research that solves this issue by improving both phases in the Discrete Hopfield Neural Network that involves first-order satisfiability logic. Therefore, this research contributes to the improvement of the learning and retrieval phases by integrating the hybrid differential evolution algorithm and Swarm Mutation respectively. This research utilizes Y-Type Random 2 Satisfiability, which combines first and second-order clauses to expand the storage capacity of DHNN models, facilitating the retrieval of optimal final neuron states. To evaluate the effectiveness of the hybrid differential evolution algorithm and Swarm Mutation in the learning and retrieval phases, several performance metrics are employed in terms of synaptic weight management, learning errors, testing errors, energy profiles, solution variations, and similarity for 10 different cases. Quantitative evaluations show that the proposed model successfully enhances the optimization of both phases, ranking first compared to 10 recent algorithms for all metrics. In terms of convergence analysis, the proposed model progressed fast towards the optimal solution with only one iteration for all cases. Additionally, the proposed model can generate a 100 % global minima ratio when dealing with a high number of neurons for Case 5.
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