Freshness of products and timeliness of delivery are two critical factors which have impact on customer satisfaction in terminal delivery of perishable products. This paper investigates how to make a cost-saving vehic...
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Freshness of products and timeliness of delivery are two critical factors which have impact on customer satisfaction in terminal delivery of perishable products. This paper investigates how to make a cost-saving vehicle scheduling for perishable products by maximizing customer satisfaction. Customer satisfaction is defined from the two aspects of freshness and time window. Then we develop a priority function based on customer satisfaction and use the hierarchical clustering method to identify customer service priority. Based on the priority, amultiobjective vehicle scheduling optimization model for perishable products is formulated to maximize customer satisfaction and minimize total delivery costs. To solve the proposed model, a priority-based genetic algorithm (PB-GA) is designed. Numerical experiments and sensitivity analysis are performed to show the validity and advantage of our approach. Results indicate that PB-GA can achieve better solutions than traditional genetic algorithm. The improvement of customer satisfaction is higher than the decrease rate of total costs within a certain shelf life range, which reveals that the proposed method is applicable to the terminal delivery of perishable products.
This paper proposes a Mixed Integer Non Linear Programming (MINLP) model and two-stage optimization algorithm for determining the most profitable synthesis and design of Combined Heat and Power units within a district...
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This paper proposes a Mixed Integer Non Linear Programming (MINLP) model and two-stage optimization algorithm for determining the most profitable synthesis and design of Combined Heat and Power units within a district heating network with heat storage while taking into account the optimal scheduling of the units over the year. A two-stage algorithm for tackling the challenging MINLP problem is devised: at the upper level the selection and sizing of the units is optimized by means of specifically selected evolutionary algorithms, while at the lower level the operational scheduling problem is linearized and optimized with a commercial Mixed Integer Linear Programing solver. Three different approaches, based on two different evolutionary algorithms and discrete variable relaxation, are devised and compared to tackle the upper level problem. Moreover a bounding technique is proposed to limit the computational time required to solve the lower-level problem. The overall algorithm is tested on an industrial scale problem to find the two system designs leading to the minimum energy consumption and the minimum total annual cost. Computational results indicate that the continuous relaxation of the plant sizes significantly helps to improve the convergence rate of the tested evolutionary algorithm and to find improved solutions. For the considered test case, the design optimized for the minimum energy consumption allows to save 64% of primary energy compared to the minimum total annual cost solution, but with a 28% higher total annual cost. (C) 2017 Elsevier Ltd. All rights reserved.
Population size of Differential Evolution (DE) algorithms is often specified by user and remains fixed during run. During the first decade since the introduction of DE the opinion that its population size should be re...
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Population size of Differential Evolution (DE) algorithms is often specified by user and remains fixed during run. During the first decade since the introduction of DE the opinion that its population size should be related to the problem dimensionality prevailed, later the approaches to DE population size setting diversified. In large number of recently introduced DE algorithms the population size is considered to be problem-independent and often fixed to 100 or 50 individuals, but alongside a number of DE variants with flexible population size have been proposed. The present paper briefly reviews the opinions regarding DE population size setting and verifies the impact of the population size on the performance of DE algorithms. Ten DE algorithms with fixed population size, each with at least five different population size settings, and four DE algorithms with flexible population size are tested on CEC2005 benchmarks and CEC2011 real-world problems. It is found that the inappropriate choice of the population size may severely hamper the performance of each DE algorithm. Although the best choice of the population size depends on the specific algorithm, number of allowed function calls and problem to be solved, some rough guidelines may be sketched. When the maximum number of function calls is set to classical values, i.e. those specified for CEC2005 and CEC2011 competitions, for low-dimensional problems (with dimensionality below 30) the population size equal to 100 individuals is suggested;population sizes smaller than 50 are rarely advised. For higher-dimensional artificial problems the population size should often depend on the problem dimensionality d and be set to 3d-5d. Unfortunately, setting proper population size for higher-dimensional real-world problems (d > 40) turns out too problem and algorithm-dependent to give any general guide;200 individuals may be a first guess, but many DE approaches would need a much different choice, ranging from 50 to 10d. Howev
An optimization algrothim,inspried by animal Behavioral Ecology Theory-optimal Foraging Theory named the optimal Foraging Algorithm (OFA) has been developed. As a new stochastic search algorithm, OFA is used to solve ...
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An optimization algrothim,inspried by animal Behavioral Ecology Theory-optimal Foraging Theory named the optimal Foraging Algorithm (OFA) has been developed. As a new stochastic search algorithm, OFA is used to solve the global optimization problems following the animal foraging behavior. During foraging, animals know how to find the best pitch with abundant prey;in establishing OFA, the basic operator of OFA was constructed following this foraging strategy. During foraging, an individual of the foraging swarms obtained more opportunities to capture prey through recruitment;in OFA the recruitment was adopted to ensure the algorithm has a higher chance to receive the optimal solution. Meanwhile, the precise model of prey choices proposed by Krebs et al. was modified and adopted to establish the optimal solution choosing strategy of OFA. The OFA was tested on the benchmark functions that present difficulties common to many global optimization problems. The performance comparisons among the OFA, realcoded genetic algorithms (RCGAs), Differential Evolution (DE), Particle Swarm Optimization (PSO) algorithm, Bees Algorithm (BA), Bacteria Foraging Optimization Algorithm (BFOA) and Shuffled Frog-leaping Algorithm (SFLA) are carried out through experiments. The parameter of OFA and the dimensions of the multi-functions are researched. The results obtained by experiments and Kruskal-Wallis test indicate that the performance of OFA is better than the other six algorithms in terms of the ability to converge to the optimal or the near-optimal solutions, and the performance of OFA is the second-best one from the view of the statistical analysis. (C) 2016 Elsevier B.V. All rights reserved.
In this paper, we propose a novel approach (SAPEO) to support the survival selection process in evolutionary multi-objective algorithms with surrogate models. The approach dynamically chooses individuals to evaluate e...
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The multiobjective allocation optimization of single tuned passive power filters for decreasing the power losses and harmonic mitigation in distribution networks is presented in the paper. The Pareto definition of the...
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In this paper, we present two approaches for non-domination level update problem. The first one is a space efficient non-domination level update (SENLU) approach. The second one is a binary search tree based efficient...
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In this paper, we present two approaches for non-domination level update problem. The first one is a space efficient non-domination level update (SENLU) approach. The second one is a binary search tree based efficient non-domination level update (BST-ENLU) approach which uses the basic property of binary search tree. Although the space complexity of BST-ENLU approach is higher than SENLU approach in caseof insertion, but in terms of number of dominance comparisons, BST-ENLU approach can outperform SENLU approach. Thus, these two approaches are complementary to each other. The comparative results show that in case where all the solutions are in different fronts, the maximum number of dominance comparisons using BST-ENLU approach is very less than ENLU approach. A tree based approach is introduced to identify the correct position of the solution to be deleted efficiently. Also a theoretical upper bound to the maximum number of dominance comparisons is obtained for both the proposed approaches in case of both insertion and deletion operations. (C) 2017 Elsevier B.V. All rights reserved.
Over the last few decades, a considerable number of evolutionary algorithms (EAs) have been proposed for solving constrained optimization problems (COPs). As for most of these problems, the optimal solution exists on ...
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Over the last few decades, a considerable number of evolutionary algorithms (EAs) have been proposed for solving constrained optimization problems (COPs). As for most of these problems, the optimal solution exists on the boundary of the feasible space, we aim to focus the search process around the boundary. In this paper a new concept, called reduced search space (R2S), is introduced. In the process, we first identify active constraints, based on the current solutions, and then define R2S around those constraint's boundaries. However, the search may be conducted either in the entire R2S or in some portions of it. To judge the impact of this concept, we have incorporated it with a number of state-of-the-art algorithms, and we have comprehensively tested it on three sets of benchmark test functions, namely, 24 test functions taken from IEEE CEC2006, 18 test functions with 10D and 301) taken from IEEE CEC2010 and 10 test functions taken from IEEE CEC2011. The results show that our proposed mechanism significantly improves the performances of state-of-the-art algorithms. (C) 2017 Elsevier Ltd. All rights reserved.
This paper proposes a novel surrogate-model-based multi-objective evolutionary algorithm, which is called Multi-objective Bayesian Optimization Algorithm based on Decomposition (MOBO/D). In this algorithm, a multi-obj...
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In beamformer design, the microphone configurations which represent microphone number and positions are necessary to be optimized in order to improve the effectiveness of speech enhancement. Determination of microphon...
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In beamformer design, the microphone configurations which represent microphone number and positions are necessary to be optimized in order to improve the effectiveness of speech enhancement. Determination of microphone configuration, number of elements and positions is a nonlinear and non convex NP-hard optimization problem which was not specified before. However, this is a nonlinear and non-convex NP-hard optimization problem. Gradient-based optimization methods can only converge to suboptimal solutions. Although the recently developed heuristic methods may find better configurations, they require long convergence time. In this paper, we study the effectiveness of using Taguchi method to determine microphone configuration. The Taguchi method is a robust and systematic optimization approach for designing reliable and high-quality models. The method conducts systematic trials based on an orthogonal array which represents a subset of representative configurations. It determines the configurations based on the experimental trials, while the heuristic methods determine the configurations by searching through the configuration domain until no better configuration can be found. A case study based on a common office environment is used as an example to illustrate the effectiveness of the Taguchi method and the commonly used heuristic methods. The numerical results demonstrate that the method is capable to develop the microphone configurations with similar performance compared with the heuristic methods when short computational time is only available. Hence, the method is a strong candidate to design microphone configurations when short development time is only available. (C) 2016 Elsevier Ltd. All rights reserved.
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