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A bilevel improved fruit fly optimization algorithm for the nonlinear bilevel programming problem

KNOWLEDGE-BASED SYSTEMS 2017年 138卷 113-123页

作者： Wang, Guangmin Ma, Linmao Chen, Jiawei China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China

This paper proposes a bilevel improved fruit fly optimization algorithm (BIFOA) to address the nonlinear bilevel programming problem (NBLPP). Considering the hierarchical nature of the problem, this algorithm is constructed by combining two sole improved fruit fly optimization algorithms. In the proposed algorithm, the lower level problem is treated as a common nonlinear programming problem rather than being transformed into the constraints of the upper level problem. Eventually, 10 test problems are selected involving low-dimensional and high-dimensional problems to evaluate the performance of BIFOA from the aspects of the accuracy and stability of the solutions. The results of extensive numerical experiments and comparisons reveal that the proposed algorithm outperforms the compared algorithms and is significantly better than the methods presented in the literature;the proposed algorithm is an effective and comparable algorithm for NBLPP. (C) 2017 Elsevier B.V. All rights reserved.

关键词： bilevel programming problem nonlinear bilevel programming problem Fruit fly optimization algorithm Improved fruit fly optimization algorithm

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Estimation of distribution algorithm for a class of nonlinear bilevel programming problems

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INFORMATION SCIENCES 2014年 256卷 184-196页

作者： Wan, Zhongping Mao, Lijun Wang, Guangmin China Univ Geosci Sch Math & Stat Wuhan 430072 Peoples R China China Univ Geosci Sch Econ & Management Wuhan 430072 Peoples R China

In this paper, a novel evolutionary algorithm called estimation of distribution algorithm (EDA) is proposed for solving a special class of nonlinear bilevel programming problems (BLPPs) in which the lower level problem is a convex programming problem for each given upper level decision. This special type of BLPP is transformed into a equivalent single-level constrained optimization problem using the Karush-Kuhn-er conditions of the lower level problem. Then, we propose an EDA based on the statistical information of the superior candidate solutions to solve the transformed problem. We stress that the new population of individuals is sampled from the probabilistic distribution of those superior solutions. Thus, one of the main advantages of EDA over most other meta-heuristics is its ability to adapt the operators to the structure of the problem, although adaptation in EDA is usually limited by the initial choice of the probabilistic model. In addition, two specific rules are established in the initialization procedure to make use of the hierarchical structure of BLPPs and to handle the constraints. Moreover, without requiring the differentiability of the objective function, or the convexity of the search space of the equivalent problem, the proposed algorithm can address nonlinear BLPPs with non-differentiable or non-convex upper level objective function and upper level constraint functions. Finally, the proposed algorithm has been applied to 16 benchmark problem;in five of these problems, all of the upper level variables and lower level variables are 10-dimensional. The numerical results compared with those of other methods reveal the feasibility and effectiveness of the proposed algorithm. (C) 2013 Elsevier Inc. All rights reserved.

关键词： nonlinear bilevel programming problem Karush-Kuhn-Tucker (KKT) condition Estimation of distribution algorithm (EDA)

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Sizing, pricing and common replenishment in a headquarter-managed centralized distribution center

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2016年第6期116卷 1086-1104页

作者： Zhang, Ting Qu, Ting Huang, George Q. Chen, Xin Wang, Zongzhong Guangdong Univ Technol Guangdong CIMS Prov Key Lab Guangzhou Guangdong Peoples R China Univ Hong Kong HKU ZIRI Lab Phys Internet Hong Kong Hong Kong Peoples R China

Purpose - Commonly shared logistics services help manufacturing companies to cut down redundant logistics investments while enhance the overall service quality. Such service-sharing mode has been naturally adopted by group companies to form the so-called headquarter-managed centralized distribution center (HQ-CDC). The HQ-CDC manages the common inventories for the group's subsidiaries and provides shared storage services to the subsidiaries through appropriate sizing, pricing and common replenishment. Apart from seeking a global optimal solution for the whole group, the purpose of this paper is to investigate balanced solutions between the HQ-CDC and the subsidiaries. Design/methodology/approach - Two decision models are formulated. Integrated model where the group company makes all-in-one decision to determine the space allocation, price setting and the material replenishment on behalf of HQ-CDC and subsidiaries. bilevel programming model where HQ-CDC and subsidiaries make decisions sequentially to draw a balance between their local objectives. From the perspective of result analysis, the integrated model will develop a managerial benchmark which minimizes the group company's total cost, while the bilevel programming model could be used to measure the interactive effects between local objectives as well as their final effect on the total objective. Findings - Through comparing the numerical results of the two models, two major findings are obtained. First, the HQ-CDC's profit is noticeably improved in the bilevel programming model as compared to the integrated model. However, the improvement of HQ-CDC's profit triggers the cost increasing of subsidiaries. Second, the analyses of different sizing and pricing policies reveal that the implementation of the leased space leads to a more flexible space utilization in the HQ-CDC and the reduced group company's total cost especially in face of large demand and high demand fluctuation. Research limitations/implications - Se

关键词： Dynamic pricing Stochastic demand Centralized distribution centre nonlinear bilevel programming problem Transhipment

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Solving the facility location and design (1|1)-centroid problem via parallel algorithms

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JOURNAL OF SUPERCOMPUTING 2011年第3期58卷 420-428页

作者： Redondo, J. L. Fernandez, J. Garcia, I. Ortigosa, P. M. Univ Almeria Dpt Comp Architecture & Elect Almeria Spain Univ Malaga Dpt Comp Architecture & Elect E-29071 Malaga Spain Univ Murcia Dpt Stat & Operat Res Murcia Spain

Several parallel strategies for solving a centroid problem are presented. In the competitive location problem considered in this paper, the aim is to maximize the profit obtained by a chain (the leader) knowing that a competitor (the follower) will react by locating another single facility after the leader locates its own facility. A global optimization memetic algorithm called UEGO_*** was proposed to cope with this hard-to-solve optimization problem. Now, five parallel implementations of the optimization algorithm have been developed. The use of several processors, and hence more computational resources, allows us to solve bigger problems and to implement new methods which increase the robustness of the algorithm at finding the global optimum. A computational study comparing the new parallel methods in terms of efficiency and effectiveness has been carried out.

关键词： Parallelism Master-slave Coarse-grain Evolutionary algorithm nonlinear bilevel programming problem Centroid (or Stackelberg) problem

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Solving the facility location and design (1|1)-centroid problem via parallel algorithms

Solving the facility location and design (1|1)-centroid prob...

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2nd Mini-Symposium on High Performance Computing (HPC) Applied to Computational problems in Science and Engineering/10th International Conference on Computational and Mathematical Methods in Science and Engineering

关键词： Parallelism Master-slave Coarse-grain Evolutionary algorithm nonlinear bilevel programming problem Centroid (or Stackelberg) problem

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