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[en] Supply chain management is monitoring of activities involved in supply chain, for integrating and coordinating between suppliers, manufacturing, inventory and transportation both within and among members. The ultimate aim of supply chains is to reduce costs and increase market coverage. Procuring and purchasing requested items in a timely manner are the two most important issues for supply chain stockholders. Group purchasing is one of the purchasing strategies in supply chains. It offers great potential by ordering large volumes to decrease expenses which can increase services to customers. A clustering optimization approach is employed to model group purchasing for a set of pharmacies in the field of healthcare. The proposed model determines a cooperation strategy based on factors such as distance between pharmacies and procurement expenditure in this network. Moreover, the proposed multi-objective group purchasing model is optimized using both goal programming and non-dominated sorting genetic algorithm. To illustrate the application of the proposed model, designing a purchasing group organization for Chalus city pharmacies is investigated. Purchasing groups are established in the way that sum of pharmacies ordering quantity has been located in the second or the third level of discount rate. Thus, the results show that GPOs can take the advantage of this cooperation.
[en] This paper proposes a novel approach of coordinating decisions in an integrated supply chain (ISC): coordinating order acceptance (OA) and batch delivery (BD) due to round trip transportation (RTT) and using third-party logistics (3PL) vehicles. The paper aims at trading off among accepted orders revenue, delivery costs as well as any penalties incurred in the ISC to maximize the total benefit. A novel mixed-integer programming is proposed for the problem. In addition, the paper provides a heuristic to form batches and develops a hybrid evolutionary computation algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) to solve the problem. An information sharing mechanism is improved and applied. To explore and to locate the proposed PSO in a better neighborhood, a local search is proposed. Taguchi experimental design is utilized to set the appropriate values of the algorithms’ parameters and random instances are generated to evaluate the performance of the algorithms. The paper investigates the profitability sensitivity of the problem to parameters and analyzes the effect of the changes in the parameters on the performance of our proposed algorithms. The attained results show the appropriate performance of our algorithms.
[en] Attempts to find new advanced methods for optimization and decision-making problems in controlling real tasks have become today’s trend in mathematics, mechanics, and computer science and many other disciplines. Meta-heuristics approaches provide powerful means to find an algorithm based on nature or physical laws and phenomena such as Newtonian laws in collision (CBO), and space laws and parallel verses (MVO) and many other laws. The recently developed approach by Han and Kim (IEEE Trans. Evolut. Comput. 6(6):580–593, 2002) is based on quantum mechanics laws. A quantum evolutionary algorithm (QEA) uses Q-bit individuals in binary code analogous to genes in the conventional genetic algorithm. In different stages of iterations, the Q-bit solutions are updated using the prominent quantum gate rotational gate and -gate. This paper is devoted to the assessment of the QEA using some well-known optimization problems. QEA has excellent features such as practical exploration and exploitation of domain space due to utilizing a binary approach for generation of the solutions and rotational gate updating based on the probability of 0 or 1. Here, -gate and parallel phase are used to change the path of finding the optimal solution for escaping from local optima.
[en] In this paper, we have experimentally demonstrated light focusing through strongly scattering media by performing binary amplitude optimization with a genetic algorithm. In the experiments, we control 160 000 mirrors of digital micromirror device to modulate and optimize the light transmission paths in the strongly scattering media. We replace the universal target-position-intensity (TPI) discriminant with signal-to-background ratio (SBR) discriminant in genetic algorithm. With 400 incident segments, a relative enhancement value of 17.5% with a ground glass diffuser is achieved, which is higher than the theoretical value of for binary amplitude optimization. According to our repetitive experiments, we conclude that, with the same segment number, the enhancement for the SBR discriminant is always higher than that for the TPI discriminant, which results from the background-weakening effect of SBR discriminant. In addition, with the SBR discriminant, the diameters of the focus can be changed ranging from 7 to 70 μm at arbitrary positions. Besides, multiple foci with high enhancement are obtained. Our work provides a meaningful reference for the study of binary amplitude optimization in the wavefront shaping field. (paper)
[en] Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designed using the adaptive filtering of the interferences in the corrupted signal using the Back Propagation Neural Network (BPNN) algorithm, Genetic Algorithm (GA), and Bee Colony (BC) algorithm. These optimization algorithms are used for initialization of weights, learning parameters, activation function and selection of network structure of the artificial neural network. The adaptive filtering system is designed using an adaptive learning ability of BPNN algorithm. This paper presents a comparison of evolutionary optimization algorithm such as hybrid GA-BPNN and BC-BPNN algorithm for the interference cancellation in corrupted signals. (author)
[en] This paper presents a wavelet-based genetic algorithm strategy for optimal sensor placement (OSP) effective for time-domain structural identification. Initially, the GA-based fitness evaluation is significantly improved by using adaptive wavelet functions. Later, a multi-species decimal GA coding system is modified to be suitable for an efficient search around the local optima. In this regard, a local operation of mutation is introduced in addition with regeneration and reintroduction operators. It is concluded that different characteristics of applied force influence the features of structural responses, and therefore the accuracy of time-domain structural identification is directly affected. Thus, the reliable OSP strategy prior to the time-domain identification will be achieved by those methods dealing with minimizing the distance of simulated responses for the entire system and condensed system considering the force effects. The numerical and experimental verification on the effectiveness of the proposed strategy demonstrates the considerably high computational performance of the proposed OSP strategy, in terms of computational cost and the accuracy of identification. It is deduced that the robustness of the proposed OSP algorithm lies in the precise and fast fitness evaluation at larger sampling rates which result in the optimum evaluation of the GA-based exploration and exploitation phases towards the global optimum solution. (paper)
[en] The article A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming, written by Suning Liu and Haiyun Shi was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 16 December 2018 without open access.
[en] In this paper, the optimal design of a shock damper and its parameters along with the layout for controlling water hammer effects in water distribution systems (WDS) will be investigated. The shock damper and its governing equations are introduced, and then complex network (networks with loop and branching) analysis is performed during the occurrence of a water hammer. Because of the multiple design parameters for the shock damper and the complexity of WDS transient analysis, a standard optimization problem is defined with maximum safety as the objective function. The problem constraints are the network analysis equations in the event of a water hammer. Also, other constraints consist of the maximum and minimum range of the allowable head and values of the design parameters for the shock damper. To solve the problem, a genetic algorithm is used, and a flow chart of the problem-solving design with the genetic algorithm is also provided. To investigate the efficiency and the effect of the optimal design of the shock damper, two real water distribution networks are considered, which include a gravity network and a pumping network. To create a water hammer occurrence in the first network, the amount of discharge in one of the nodes is suddenly increased, while in the second network, one of the control valves is closed suddenly. These events lead to the occurrence of a significant water hammer in each system, resulting in both positive and negative waves and water column separation phenomena. The results show the high effectiveness of the optimal shock damper design in controlling the effects of transient flows in real water distribution systems, thereby increasing those systems’ effectiveness.
[en] Inverse materials design tackles the challenge of finding materials with desired properties, tailored to specific applications, by combining atomistic simulations and optimization methods. The search for optimal materials requires one to survey large spaces of candidate solids. These spaces of materials can encompass both known and hypothetical compounds. When hypothetical compounds are explored, it becomes crucial to determine which ones are stable (and can be synthesized) and which are not. Crystal structure prediction is a necessary step for assessing theoretically the stability of a hypothetical material and, therefore, is a crucial step in inverse materials design protocols. Here, we describe how biologically-inspired global optimization methods can efficiently predict the stable crystal structure of solids. Specifically, we discuss the application of genetic algorithms to search for optimal atom configurations in systems in which the underlying lattice is given, and of evolutionary algorithms to address the general lattice-type prediction problem. (paper)
[en] We demonstrate a modified particle swarm optimization (PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm (GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background. (paper)