Journal of Combinatorial Optimization最新文献_第5页

Solving the optimal order quantity with unknown parameters for products with stock-dependent demand and variable holding cost rate 求解需求依赖于库存且持有成本率可变的未知参数产品的最优订货量问题

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-02-09 DOI: 10.1007/s10878-025-01260-z

Zhanbing Guo, Yejie Zhang

{"title":"Solving the optimal order quantity with unknown parameters for products with stock-dependent demand and variable holding cost rate","authors":"Zhanbing Guo, Yejie Zhang","doi":"10.1007/s10878-025-01260-z","DOIUrl":"https://doi.org/10.1007/s10878-025-01260-z","url":null,"abstract":"Solving the optimal order quantity for products with stock-dependent demand is a challenging task as both exact values of multiple parameters and complicated procedures are required. Motivated by this practical dilemma, this paper develops a new method to overcome the above-mentioned two challenges simultaneously. This new method, referred as two-stage AEOQ (adaptive economic order quantity) policy, includes the following two merits when managing products with stock-dependent demand and variable holding cost rate. First, it is feasible even when the values of underlying parameters are unknown. Second, it is easy-to-implement as decisions are made via adaptively recalibrating the inputs of classical EOQ formula by observable variables in the previous period. Theoretical analysis and numerical example show that this two-stage AEOQ policy could obtain the optimal order quantity. Moreover, this two-stage AEOQ policy is robust to parameter misestimation, and performs better than the traditional solution method when the underlying parameters are volatile. Finally, it is shown that this two-stage AEOQ policy could be further simplified when the fixed ordering cost is negligible. Therefore, this study provides a feasible order policy when the exact values of underlying parameters are unable to gain or when the economic environment is volatile.","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"45 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Embedded-filter ACO using clustering based mutual information for feature selection 基于互信息聚类的嵌入式滤波蚁群算法

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-02-09 DOI: 10.1007/s10878-025-01259-6

S. Kumar Reddy Mallidi, Rajeswara Rao Ramisetty

{"title":"Embedded-filter ACO using clustering based mutual information for feature selection","authors":"S. Kumar Reddy Mallidi, Rajeswara Rao Ramisetty","doi":"10.1007/s10878-025-01259-6","DOIUrl":"https://doi.org/10.1007/s10878-025-01259-6","url":null,"abstract":"The performance of machine learning algorithms is significantly influenced by the quality of the underlying dataset, which often comprises a mix of essential and redundant features. Feature selection, which identifies and discards these redundant features, plays a pivotal role in reducing computational and storage overheads. Current methodologies for this task primarily span filter-based and wrapper-based techniques. While Ant Colony Optimization, a popular bio-inspired meta-heuristic technique, has been extensively used for feature selection, employing mutual information as a principal heuristic measure, traditional mutual information is primarily suited for categorical features. To address this limitation, this study introduces an Embedded-Filter Ant Colony Optimization feature selection strategy that incorporates Clustering-Based Mutual Information. This integration offers enhanced support for classification tasks involving continuous features. To validate the efficiency of the proposed approach, various datasets were used, and a diverse range of machine learning algorithms were employed to evaluate the derived feature subsets. In addition to comparing the proposed method with Grey Wolf Optimization and Cuckoo Search Optimization-based feature selection approaches, a comprehensive evaluation was also carried out against established Ant Colony Optimization wrapper techniques. Experimental results indicate that the proposed Embedded-Filter Ant Colony Optimization consistently selects the minimal yet most relevant feature set while largely maintaining the efficacy of machine learning algorithms.","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"9 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fuzzy approach for the intuitionistic multi-objective linear fractional programming problem using a bisection method 直观多目标线性分式规划问题的模糊求解方法

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-02-09 DOI: 10.1007/s10878-025-01261-y

Nurdan Kara, Hale Gonce Kocken, Hande Günay Akdemir

引用次数: 0

The undirected optical indices of trees 树木的非定向光学指数

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-01-20 DOI: 10.1007/s10878-024-01255-2

Yuan-Hsun Lo, Hung-Lin Fu, Yijin Zhang, Wing Shing Wong

引用次数: 0

Agent-constrained truthful facility location games 代理受限的真实设施位置博弈

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-01-20 DOI: 10.1007/s10878-025-01258-7

Argyrios Deligkas, Mohammad Lotfi, Alexandros A. Voudouris

引用次数: 0

$$(K_{1}vee {P_{t})}$$ -saturated graphs with minimum number of edges $$(K_{1}vee {P_{t})}$$ -具有最小边数的饱和图

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-01-20 DOI: 10.1007/s10878-024-01256-1

Jinze Hu, Shengjin Ji, Qing Cui

引用次数: 0

Advertising meets assortment planning: joint advertising and assortment optimization under multinomial logit model 广告满足分类规划：多项逻辑模型下的联合广告与分类优化

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-01-20 DOI: 10.1007/s10878-024-01257-0

Chenhao Wang, Yao Wang, Shaojie Tang

{"title":"Advertising meets assortment planning: joint advertising and assortment optimization under multinomial logit model","authors":"Chenhao Wang, Yao Wang, Shaojie Tang","doi":"10.1007/s10878-024-01257-0","DOIUrl":"https://doi.org/10.1007/s10878-024-01257-0","url":null,"abstract":"Despite the assortment optimization problem has been widely studied in the past decades, the interplay between advertising and its implications for this issue remains under-explored. This study seeks to bridge this research gap by tackling the combined challenge of advertising and assortment optimization. We assume that advertising can increase the awareness of specific products, and the magnitude of this effect is jointly depends on the product-specific effectiveness of advertising and the allocated advertising budget. For this joint problem, our objective is to maximize the expected revenue by finding the optimal advertising strategy and the displayed assortment. In this work, we analyze the structure of this problem and propose efficient approaches to solve it across different scenarios. In the unconstrained setting, we demonstrate that the optimal assortment includes products whose revenue exceeds a certain threshold. When there is a cardinality constraint for the assortment, we consider a relaxed problem and propose an efficient method to identify a near-optimal solution. We also examine the joint assortment, pricing, and advertising problem in both unconstrained and cardinality-constrained settings, incorporating the fairness constraint for the advertising strategy and extending our findings to account for consumer sequential decision-making patterns. Through a series of numerical tests, we confirm the validity of our methods and demonstrate that they outperform existing heuristic approaches.\u0000","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"32 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved PTAS for covering targets with mobile sensors 一种用于移动传感器覆盖目标的改进PTAS

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-01-20 DOI: 10.1007/s10878-024-01253-4

Nonthaphat Wongwattanakij, Nattawut Phetmak, Chaiporn Jaikaeo, Jittat Fakcharoenphol

引用次数: 0

Recognizing integrality of weighted rectangles partitions 加权矩形分区的完整性识别

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-01-20 DOI: 10.1007/s10878-024-01252-5

Paul Deuker, Ulf Friedrich

引用次数: 0

Degree and betweenness-based label propagation for community detection 基于度和间性标签传播的社群检测

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2025-01-20 DOI: 10.1007/s10878-024-01254-3

Qiufen Ni, Jun Wang, Zhongzheng Tang

{"title":"Degree and betweenness-based label propagation for community detection","authors":"Qiufen Ni, Jun Wang, Zhongzheng Tang","doi":"10.1007/s10878-024-01254-3","DOIUrl":"https://doi.org/10.1007/s10878-024-01254-3","url":null,"abstract":"Community detection, as a crucial network analysis technique, holds significant application value in uncovering the underlying organizational structure in complex networks. In this paper, we propose a degree and betweenness-based label propagation method for community detection (DBLPA). First, we calculate the importance of each node by combining node degree and betweenness centrality. A node i is considered as a core node in the network if its importance is maximal among its neighbor nodes. Next, layer-by-layer label propagation starts from core nodes. The first layer of nodes for label propagation consists of the first-order neighbors of all core nodes. In the first layer of label propagation, the labels of core nodes are first propagated to the non-common neighbor nodes between core nodes, and then to the common neighbor nodes between core nodes. At the same time, the flag parameter is set to record the changing times of a node’s label, which is helpful to calibrate the node’s labels during the label propagation. It effectively improves the misclassification in the process of label propagation. We test the DBLPA on four real network datasets and nine synthetic network datasets, and the experimental results show that the DBLPA can effectively improve the accuracy of community detection.","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"9 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0