Uncapacitated facility location problems: contributions

Facility location problem (FLP) is a mathematical way to optimally locate facilities within a set of candidates to satisfy the requirements of a given set of clients. This study addressed the uncapacitated FLP as it assures that the capacity of every selected facility is finite. Thus, even if the demand is not known, which often is the case, in reality, organizations may still be able to take strategic decisions such as locating the facilities. There are different approaches relevant to the uncapacitated FLP. Here, the cuckoo search via Lévy flight (CS-LF) was used to solve the problem. Though hybrid methods produce better results, this study employed CS-LF to determine first its potential in finding solutions for the problem, particularly when applied to a real-world problem. The method was applied to the data set obtained from a department store in Davao City, Philippines. Results showed that applying CS-LF yielded better facility locations compared to particle swarm optimization and other existing algorithms. Although these results showed that CS-LF is a promising method to solve this particular problem, further studies on other FLP are recommended to establish a strong foundation of the capability of CS-LF in solving FLP.

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“…The fitness value for each cuckoo egg C l , l = 1, 2, …, s, was computed using the fitness function represented in the study of Galvão (2004) …”

Section: Fitness Evaluationmentioning

confidence: 99%

Cuckoo search via Levy flights applied to uncapacitated facility location problem

et al. 2017

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“…Andreas and Volkan [16] employed submodularity to perform an iterative dictionary atom selection from a candidate set. Jiang and Davis [13] solved a facility location problem [11] for salient region detection. The saliency of a region is modeled in terms of its appearance and spatial location, and salient region detection is achieved by maximizing a submodular objective function.…”

Section: Related Workmentioning

confidence: 99%

Cross-Modality Submodular Dictionary Learning for Information Retrieval

Zhu

Shao

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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This paper addresses the problem of joint modeling of multimedia components in different media forms. We consider the information retrieval task across both text and image documents, which includes retrieving relevant images that closely match the description in a text query and retrieving text documents that best explain the content of an image query. A greedy dictionary construction approach is introduced for learning an isomorphic feature space, to which cross-modality data can be adapted while data smoothness is guaranteed. The proposed objective function consists of two reconstruction error terms for both modalities and a Maximum Mean Discrepancy (MMD) term that measures the cross-modality discrepancy. Optimization of the reconstruction terms and the MMD term yields a compact and modalityadaptive dictionary pair. We formulate the joint combinatorial optimization problem by maximizing variance reduction over a candidate signal set while constraining the dictionary size and coefficients' sparsity. By exploiting the submodularity and the monotonicity property of the proposed objective function, the optimization problem can be solved by a highly efficient greedy algorithm, and is guaranteed to be at least a (e − 1)/e ≈ 0.632-approximation to the optimum. The proposed method achieves state-of-the-art performance on the Wikipedia dataset.

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“…Liu et al [20] presented a method that uses the entropy rate of a random walk on a graph for compact and homogeneous clustering. Jiang and Davis [14] solved a facility location problem [10,18] for salient region detection. The saliency of a region is modeled in terms of its appearance and spatial location, and salient region detection is achieved by maximizing a submodular objective function.…”

Section: Related Workmentioning

confidence: 99%

Submodular Object Recognition

Zhu

Jiang

Shao

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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We present a novel object recognition framework based on multiple figure-ground hypotheses with a large object spatial support, generated by bottom-up processes and midlevel cues in an unsupervised manner. We exploit the benefit of regression for discriminating segments' categories and qualities, where a regressor is trained to each category using the overlapping observations between each figureground segment hypothesis and the ground-truth of the target category in an image. Object recognition is achieved by maximizing a submodular objective function, which maximizes the similarities between the selected segments (i.e., facility locations) and their group elements (i.e., clients), penalizes the number of selected segments, and more importantly, encourages the consistency of object categories corresponding to maximum regression values from different category-specific regressors for the selected segments. The proposed framework achieves impressive recognition results on three benchmark datasets, including PASCAL VOC 2007, Caltech-101 and ETHZ-shape.

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Uncapacitated facility location problems: contributions

Cited by 32 publications

References 96 publications

Cuckoo search via Levy flights applied to uncapacitated facility location problem

Cuckoo search via Levy flights applied to uncapacitated facility location problem

Cross-Modality Submodular Dictionary Learning for Information Retrieval

Submodular Object Recognition

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