Journal of Combinatorial Optimization最新文献

{"title":"Impact of payment schemes on performance in a medical cost-sharing system: bundled payment vs. total prepayment","authors":"Miao Yu, Wang Zhou, Yu Zhao","doi":"10.1007/s10878-025-01301-7","DOIUrl":"https://doi.org/10.1007/s10878-025-01301-7","url":null,"abstract":"<p>Currently, many countries are on the process of reforming their health care payment systems from post-payment to pre-payment. To explore the impact of pre-payment schemes on health system performance we investigate the two payment schemes, bundled payment (BP) and total prepayment (TP), on performance in a medical cost-sharing system. Under the BP scheme, the government compensates hospitals with a lump sum for the entire course of each patient’s care. Under the TP scheme, the government provides the total amount of integrated compensation within a period. A three Stackelberg game with an embedded queueing model is used to explore the interactions among participants: government, hospital, and patients. The government determines the compensation received by hospitals and the copayment paid by patients to maximize social welfare. Next, the hospital determines its service rate for each medical episode to maximize profit. Last, patients make decisions on whether to appeal to the hospital for medical services. We derive the optimal strategy for the participants under the BP and TP schemes, and compare the system performance through numerical analysis. Results show that BP is better than TP in reducing patient expected waiting time, while it outperforms TP in terms of system accessibility and service quality. Our study is the first to consider the total prepayment scheme in the healthcare system decision analysis and the findings offer important insights for policymakers regarding implementing medical insurance reform in practice.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"57 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144114063","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}

{"title":"Pseudo-Shapley value for weak games of threats","authors":"Daniel Li Li, Erfang Shan","doi":"10.1007/s10878-025-01319-x","DOIUrl":"https://doi.org/10.1007/s10878-025-01319-x","url":null,"abstract":"<p>For a real number <span>(omega )</span>, a weak game of threats (<i>N</i>, <i>v</i>) consists of a set <i>N</i> of <i>n</i> players and a function <span>(v:2^Nrightarrow mathbb {R})</span> such that <span>(omega v(emptyset )+(1-omega )v(N)=0)</span>, where <span>(v(emptyset )ne 0)</span> possibly. It is shown that there exists a unique value with respect to <span>(omega )</span> for weak games of threats that satisfies efficiency, linearity, symmetry and the null player property.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"42 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144114068","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}

{"title":"Better approximating SONET k-edge partition for small capacity k","authors":"Junhui Ye, Huihuang Jiang, Guangting Chen, Yong Chen, Guohui Lin, An Zhang","doi":"10.1007/s10878-025-01308-0","DOIUrl":"https://doi.org/10.1007/s10878-025-01308-0","url":null,"abstract":"<p>We study the SONET edge partition problem that models telecommunication network design to partition the edge set of a given graph into several edge-disjoint subgraphs, such that each subgraph has size no greater than a given capacity <i>k</i> and the sum of the orders of these subgraphs is minimized. The problem is NP-hard when <span>(k ge 3)</span> and admits an <span>(O(log k))</span>-approximation algorithm. For small capacity <span>(k = 3, 4, 5)</span>, by observing that some subgraph structures are more favorable than the others, we propose modifications to existing algorithms and design novel amortization schemes to prove their improved performance. Our algorithmic results include a <span>(frac{4}{3})</span>-approximation for <span>(k = 3)</span>, improving the previous best <span>(frac{13}{9})</span>-approximation, a <span>(frac{4}{3})</span>-approximation for <span>(k = 4)</span>, improving the previous best <span>((frac{4}{3} + epsilon ))</span>-approximation, and a <span>(frac{3}{2})</span>-approximation for <span>(k = 5)</span>, improving the previous best <span>(frac{5}{3})</span>-approximation. Besides these improved algorithms, our main contribution is the amortization scheme design, which can be helpful for similar algorithms and problems.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"57 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979909","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}

{"title":"Big data-driven optimal weighted fused features-based ensemble learning classifier for thyroid prediction with heuristic algorithm","authors":"K. Hema Priya, K. Valarmathi","doi":"10.1007/s10878-025-01304-4","DOIUrl":"https://doi.org/10.1007/s10878-025-01304-4","url":null,"abstract":"<p>Diagnosis of thyroid disease is a most important cause in the field of medicinal research and it is a complex onset axiom. Secretion of Thyroid hormone plays a major role in the regulation of metabolism. Hence, it is very significant to predict thyroid disease in the initial stage, which is helpful for preventing more serious health complications due to thyroid cancer. The diagnostic accuracy of machine leaning-based approaches is greater but these techniques require large amounts of data for the diagnosis process. In the conventional approaches, the time needed for the prediction process is also high. Feature engineering is less investigated in conventional models and hence error produced during the prediction process is high. Hence, in this research work, a machine learning-aided thyroid disease prediction technique is designed to provide higher prediction accuracy and reliability. Initially, the thyroid data is gathered from the standard benchmark resources. Next, the data transformation process is carried out to make the data usable for analysis and visualization. After, the features are extracted using Principal Component Analysis (PCA), “One-Dimensional Convolutional Neural Network Model (1DCNN). Moreover, the statistical features are also extracted for getting more relevant information from the data. The three sets of features such as PCA-based, 1DCNN-based and statistical are concatenated and fed to the “optimal weighted feature selection” process, where the optimal features and weights are tuned by an Improved Archimedes Optimization Algorithm (IAOA). Next, the selected optimally fused features are given to the Ensemble Learning (EL) for predicting the thyroid diseases, where the EL with be suggested by incorporating stacking classifier, XGboost, and Multivariate regression classifier. Ensembling of three different classifiers provides higher thyroid disease prediction accuracy and it makes the decision about normal and abnormal classes. Here, the same IAOA is used for optimizing the parameters of every classifier. The investigational outcomes demonstrate that the proposed ensemble classifier provides higher performance than others. Experimental results prove that the thyroid prediction accuracy of the developed EL approach is 96.30%, precision is 99.67% and F1-score is 97.93%, which is more extensive than the state-of-the-art approaches.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"17 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979910","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}

{"title":"Neighbor sum distinguishable $$k$$ -edge colorings of joint graphs","authors":"Xiangzhi Tu, Peng Li, Yangjing Long, Aifa Wang","doi":"10.1007/s10878-025-01309-z","DOIUrl":"https://doi.org/10.1007/s10878-025-01309-z","url":null,"abstract":"<p>In a graph <i>G</i>, the normal <i>k</i>-edge coloring <span>(sigma )</span> is defined as the conventional edge coloring of <i>G</i> using the color set <span>(left[ k right] =left{ 1,2,cdots ,k right} )</span>. If the condition <span>(Sleft( u right) ne Sleft( v right) )</span> holds for any edge <span>(uvin Eleft( G right) )</span>, where <span>(Sleft( u right) =sum nolimits _{uvin Eleft( G right) }{sigma left( uv right) })</span>, then <span>(sigma )</span> is termed a neighbor sum distinguishable <i>k</i>-edge coloring of the graph <i>G</i>, abbreviated as <i>k</i>-VSDEC. The minimum number of colors <span>( k )</span> needed for this type of coloring is referred to as the neighbor sum distinguishable edge chromatic number of <span>( G )</span>, represented as <span>( chi '_{varSigma }(G) )</span>. This paper examines neighbor sum distinguishable <i>k</i>-edge colorings in the joint graphs of an <i>h</i>-order path <span>({{P}_{h}})</span> and an <span>(left( z+1 right) )</span>-order star <span>({{S}_{z}})</span>, providing exact values for their neighboring and distinguishable edge coloring numbers, which are either <span>(varDelta )</span> or <span>(varDelta +1)</span>.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"39 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979912","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}

{"title":"Randomized approximation algorithms for monotone k-submodular function maximization with constraints","authors":"Yuying Li, Min Li, Yang Zhou, Shuxian Niu, Qian Liu","doi":"10.1007/s10878-025-01299-y","DOIUrl":"https://doi.org/10.1007/s10878-025-01299-y","url":null,"abstract":"<p>In recent years, <i>k</i>-submodular functions have garnered significant attention due to their natural extension of submodular functions and their practical applications, such as influence maximization and sensor placement. Influence maximization involves selecting a set of nodes in a network to maximize the spread of information, while sensor placement focuses on optimizing the locations of sensors to maximize coverage or detection efficiency. This paper first proposes two randomized algorithms aimed at improving the approximation ratio for maximizing monotone <i>k</i>-submodular functions under matroid constraints and individual size constraints. Under the matroid constraints, we design a randomized algorithm with an approximation ratio of <span>(frac{nk}{2nk-1})</span> and a complexity of <span>(O(rn(text {RO}+ktext {EO})))</span>, where <i>n</i> represents the total number of elements in the ground set, <i>k</i> represents the number of disjoint sets in a <i>k</i>-submodular function, <i>r</i> denotes the size of the largest independent set, <span>(text {RO})</span> indicates the time required for the matroid’s independence oracle, and <span>(text {EO})</span> denotes the time required for the evaluation oracle of the <i>k</i>-submodular function.Meanwhile, under the individual size constraints, we achieve an approximation factor of <span>(frac{nk}{3nk-2})</span> with a complexity of <i>O</i>(<i>knB</i>), where <i>n</i> is the total count of elements in the ground set, and <i>B</i> is the upper bound on the total size of the <i>k</i> disjoint subsets, belonging to <span>(mathbb {Z_{+}})</span>. Additionally, this paper designs two double randomized algorithms to accelerate the algorithm’s running speed while maintaining the same approximation ratio, with success probabilities of (<span>(1-delta )</span>), where <span>(delta )</span> is a positive parameter input by the algorithms. Under the matroid constraint, the complexity is reduced to <span>(O(nlog rlog frac{r}{delta }(text {RO}+ktext {EO})))</span>. Under the individual size constraint, the complexity becomes <span>(O(k^{2}nlog frac{B}{k}log frac{B}{delta }))</span>.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"123 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933588","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}

{"title":"A review on the versions of artificial bee colony algorithm for scheduling problems","authors":"Beyza Gorkemli, Ebubekir Kaya, Dervis Karaboga, Bahriye Akay","doi":"10.1007/s10878-025-01296-1","DOIUrl":"https://doi.org/10.1007/s10878-025-01296-1","url":null,"abstract":"<p>Today, artificial bee colony (ABC) algorithm is one of the most popular swarm intelligence based optimization techniques. Although it was originally introduced to work on continuous space for numerical optimization problems, several researchers also successfully use the ABC for other problem types. In this study, variants of the ABC for scheduling problems are surveyed. Since the scheduling problems are combinatorial type problems, generally some modifications related to the solution representation or neighborhood search operators are introduced in these studies. Additionally, several enhancement ideas are also presented for the ABC algorithm such as the improvements of initialization, employed bee, onlooker bee, scout bee phases and hybrid usage with other metaheuristics or local search methods. This paper evaluates the literature, provides some analyses on its current state and gaps, and addresses possible future works. It is hoped that this review study would be beneficial for the researchers interested in this field.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"96 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920692","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}

{"title":"A 3-space dynamic programming heuristic for the cubic knapsack problem","authors":"Ibrahim Dan Dije, Franklin Djeumou Fomeni, Leandro C. Coelho","doi":"10.1007/s10878-025-01294-3","DOIUrl":"https://doi.org/10.1007/s10878-025-01294-3","url":null,"abstract":"<p>The cubic knapsack problem (CKP) is a combinatorial optimization problem, which can be seen both as a generalization of the quadratic knapsack problem (QKP) and of the linear Knapsack problem (KP). This problem consists of maximizing a cubic function of binary decision variables subject to one linear knapsack constraint. It has many applications in biology, project selection, capital budgeting problem, and in logistics. The QKP is known to be strongly NP-hard, which implies that the CKP is also NP-hard in the strong sense. Unlike its linear and quadratic counterparts, the CKP has not received much of attention in the literature. Thus the few exact solution methods known for this problem can only handle problems with up to 60 decision variables. In this paper, we propose a deterministic dynamic programming-based heuristic algorithm for finding a good quality solution for the CKP. The novelty of this algorithm is that it operates in three different space variables and can produce up to three different solutions with different levels of computational effort. The algorithm has been tested on a set of 1570 test instances, which include both standard and challenging instances. The computational results show that our algorithm can find optimal solutions for nearly 98% of the standard test instances that could be solved to optimality and for 92% for the challenging instances. Finally, the computational experiments present comparisons between our algorithm, an existing heuristic algorithm for the CKP found in the literature, as well as adaptations to the CKP of some heuristic algorithms designed for the QKP. The results show that our algorithm outperforms all these methods.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"84 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880598","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}

{"title":"Chaotic guided local search algorithm for solving global optimization and engineering problems","authors":"Anis Naanaa","doi":"10.1007/s10878-025-01281-8","DOIUrl":"https://doi.org/10.1007/s10878-025-01281-8","url":null,"abstract":"<p>Chaos optimization algorithm (COA) is an interesting alternative in a global optimization problem. Due to the non-repetition and ergodicity of chaos, it can explore the global search space at higher speeds than stochastic searches that depend on probabilities. To adjust the solution obtained by COA, guided local search algorithm (GLS) is integrated with COA to form a hybrid algorithm. GLS is a metaheuristic optimization algorithm that combines elements of local search with strategic guidance to efficiently explore the solution space. This study proposes a chaotic guided local search algorithm to search for global solutions. The proposed algorithm, namely COA-GLS, contributes to optimization problems by providing a balance between quick convergence and good solution quality. Its combination of local refinement, strategic guidance, diversification strategies, and adaptability makes it a powerful metaheuristic capable of efficiently navigating complex solution spaces and finding high-quality solutions in a relatively short amount of time. Simulation results show that the present algorithms significantly outperform the existing methods in terms of convergence speed, numerical stability, and a better optimal solution than other algorithms.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"53 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876098","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}

{"title":"A multi-objective perspective on the cable-trench problem","authors":"Lara Löhken, Michael Stiglmayr","doi":"10.1007/s10878-025-01289-0","DOIUrl":"https://doi.org/10.1007/s10878-025-01289-0","url":null,"abstract":"<p>The cable-trench problem is defined as a linear combination of the shortest path and the minimum spanning tree problem. In particular, the goal is to find a spanning tree that simultaneously minimizes its total length and the total path length from a pre-defined root to all other vertices. Both, the minimum spanning tree and the shortest path problem are known to be efficiently solvable. However, a linear combination of these two objectives results in a highly complex problem. In this article, we introduce the bi-objective cable-trench problem which separates the two cost functions. We show that in general, the bi-objective formulation has additional compromise solutions compared to the cable-trench problem in its original formulation. To determine the set of non-dominated points and efficient solutions, we use <span>(varepsilon )</span>-constraint scalarizations in combination with a problem-specific cutting plane. Moreover, we present numerical results on different types of graphs analyzing the impact of density and cost structure on the cardinality of the non-dominated set and the solution time.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"35 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876069","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}