Computers & Operations Research最新文献_第7页

On the periodic service scheduling problem with non-uniform demands 需求不均匀的周期性服务调度问题

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-24 DOI: 10.1016/j.cor.2025.107280

Elena Fernández , Jörg Kalcsics

This paper introduces the Periodic Service Scheduling Problem with Non-uniform Demands, in which the best service policy for a set of customers with periodically recurring demand through a given finite planning horizon has to be determined. Service to customers is provided at every time period by a set of potential service providers, each of them with an activation cost and a capacity. The decisions to be made include the servers to be activated at each time period together with a service schedule and server allocation for every customer that respect the periodicity of customer demand and the capacity of the activated servers, which minimize the total cost of the activated servers. We give a first Integer Linear Programming formulation with one set of decision variables associated with each of the decisions of the problem. Afterwards, we develop a logic-based Benders reformulation where one set of variables is projected out and constraints that guarantee the feasibility of the solutions are introduced. The separation problem for the new set of constraints is studied, and an exact Branch & Logic-Benders-Cut algorithm for the reformulation is proposed together with several variations and enhancements. The particular cases in which all servers are identical and in which all parameters are time-invariant are also studied. Extensive computational experiments assess the superiority of the logic-based Benders reformulation over the first formulation.

本文介绍了具有非均匀需求的周期性服务调度问题，该问题需要在给定的有限规划范围内确定一组具有周期性重复需求的客户的最佳服务策略。在每个时间段，由一组潜在的服务提供商向客户提供服务，每个服务提供商都具有激活成本和容量。要做出的决策包括在每个时间段激活的服务器，以及服务计划和每个客户的服务器分配，这些都要考虑到客户需求的周期性和激活服务器的容量，从而使激活服务器的总成本最小化。我们给出了第一个整数线性规划公式，该公式具有与问题的每个决策相关联的一组决策变量。然后，我们开发了一个基于逻辑的Benders重组公式，其中一组变量被投影出来，并引入了保证解决方案可行性的约束。研究了新约束集的分离问题，提出了一种精确的Branch &； Logic-Benders-Cut算法，并进行了一些改进和改进。还研究了所有服务器都相同且所有参数都是时不变的特殊情况。广泛的计算实验评估了基于逻辑的Benders重新表述优于第一种表述。

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引用次数: 0

A unified approach to extract interpretable rules from tree ensembles via Integer Programming 一种通过整数规划从树集成中提取可解释规则的统一方法

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-19 DOI: 10.1016/j.cor.2025.107283

Lorenzo Bonasera , Emilio Carrizosa

Tree ensembles are widely used machine learning models, known for their effectiveness in supervised classification and regression tasks. Their performance derives from aggregating predictions of multiple decision trees, which are renowned for their interpretability properties. However, tree ensemble models do not reliably exhibit interpretable output. Our work aims to extract an optimized list of rules from a trained tree ensemble, providing the user with a condensed, interpretable model that retains most of the predictive power of the full model. Our approach consists of solving a set partitioning problem formulated through Integer Programming. The extracted list of rules is unweighted and defines a partition of the training data, assigning each instance to exactly one rule, and thereby simplifying the explanation process. The proposed method works with tabular or time series data, for both classification and regression tasks, and its flexible formulation can include any arbitrary loss or regularization functions. Our computational experiments offer statistically significant evidence that our method performs comparably to several rule extraction methods in terms of predictive performance and fidelity towards the tree ensemble. Moreover, we empirically show that the proposed method effectively extracts interpretable rules from tree ensembles that are designed for time series data.

树集成是广泛使用的机器学习模型，以其在监督分类和回归任务中的有效性而闻名。它们的性能来源于多个决策树的聚合预测，这些决策树以其可解释性而闻名。然而，树集成模型不能可靠地显示可解释的输出。我们的工作旨在从训练树集成中提取优化的规则列表，为用户提供一个浓缩的、可解释的模型，该模型保留了完整模型的大部分预测能力。我们的方法包括解决一个通过整数规划制定的集划分问题。提取的规则列表是不加权的，并定义了训练数据的一个分区，将每个实例精确地分配给一个规则，从而简化了解释过程。所提出的方法适用于表格或时间序列数据，用于分类和回归任务，其灵活的公式可以包括任何任意损失或正则化函数。我们的计算实验提供了统计上显著的证据，表明我们的方法在预测性能和对树集合的保真度方面与几种规则提取方法相当。此外，我们的经验表明，该方法可以有效地从为时间序列数据设计的树集成中提取可解释的规则。

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引用次数: 0

A novel mathematical model for the scheduling of a zero inventory production: an application of process scheduling in fog computing 零库存生产调度的一种新的数学模型：过程调度在雾计算中的应用

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-19 DOI: 10.1016/j.cor.2025.107284

Mani Sharifi , Sharareh Taghipour , Abdolreza Abhari , Maciej Rysz

One of the main production-related costs in manufacturing is inventory cost since manufacturing firms allocate a vast area to raw material, semi-processed, and final products in production lines and warehouses. Reducing the volume of these inventories leads to lower production-related costs. This paper presents a novel mathematical model for zero-inventory production scheduling. In this model, the jobs arrive at fixed times and are scheduled on a set of unrelated machines. The jobs have different operations that need to be processed one by one. Since the system has zero inventory, the jobs must be processed immediately upon arrival. Also, whenever a job’s operation is complete, the following operation must instantly start (no wait time). That operation is outsourced if no machines are available to process any of the job’s operations. The jobs’ operations are dispatched to the machines from a dispatching center, and there is a latency between the dispatching center, the machines, and the outsourcing center. We present a mixed-integer non-linear programming (MINLP) model to formulate this problem. Then, the MINLP model is turned into a mixed-integer linear programming (MILP) model by linearizing its constraints. Since many production scheduling problems are known to be NP-hard, particularly those involving unrelated parallel machines, precedence constraints, and time-dependent decisions like ours, we adopt two metaheuristics to solve the problem for large-scale cases where exact methods are computationally inefficient. The first is a Genetic Algorithm (GA), and the second is a Teaching-Learning-Based Optimization (TLBO) algorithm. The performance of these algorithms is tested against the optimal solutions obtained from CPLEX for a set of small-scale problems. We consider a real case study, an image processing system, to validate the proposed developments (the MILP model and the GA). The results show that the presented model and algorithm can reduce the system’s total cost by about 12.57% compared to the existing online dispatching rules.

制造业中与生产相关的主要成本之一是库存成本，因为制造企业在生产线和仓库中为原材料、半成品和最终产品分配了很大的面积。减少这些库存的数量可以降低与生产相关的成本。提出了一种新的零库存生产调度数学模型。在这个模型中，作业在固定时间到达，并被安排在一组不相关的机器上。作业有不同的操作，需要逐一处理。由于系统的库存为零，作业必须在到达后立即处理。此外，每当作业的操作完成时，下面的操作必须立即开始（没有等待时间）。如果没有可用的机器来处理任何作业的操作，则将该操作外包。作业的操作从调度中心分发给机器，并且在调度中心、机器和外包中心之间存在延迟。我们提出了一个混合整数非线性规划（MINLP）模型来表述这个问题。然后，通过对约束条件进行线性化，将MINLP模型转化为混合整数线性规划模型。由于已知许多生产调度问题是np困难的，特别是那些涉及不相关的并行机器，优先约束和时间依赖决策的问题，我们采用两种元启发式方法来解决大规模情况下的问题，其中精确方法计算效率低下。第一种是遗传算法（GA），第二种是基于教学的优化算法（TLBO）。针对一组小规模问题的CPLEX最优解，对这些算法的性能进行了测试。我们考虑一个真实的案例研究，一个图像处理系统，来验证所提出的发展（MILP模型和遗传算法）。结果表明，与现有的在线调度规则相比，所提出的模型和算法可使系统总成本降低约12.57%。

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引用次数: 0

Optimizing high-tech product take-back schemes in a closed-loop supply chain 闭环供应链中高科技产品回收方案的优化

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-13 DOI: 10.1016/j.cor.2025.107282

Fatemeh Keshavarz-Ghorbani , Mohamad Y. Jaber , Seyed Hamid Reza Pasandideh

Frequent product development is a solution to the shortened product lifecycles in the consumer electronics industry. It enables companies to maintain competitiveness and strengthen their market share. However, environmental concerns bring reverse logistics practices into focus. A take-back policy is a strategic reverse logistics activity known to foster market share; however, it poses various challenges and uncertainties. Considering uncertain demand, we introduced an innovative adoption model with two distinct take-back policies, trade-in and credit, to address challenges in multi-generation production planning. Inspired by real-world practices of companies like Apple and Samsung, our model first examines how trade-in programs drive repeat purchases and enhance market share, with credit-based programs to attract new customers. It then captures changes in demand, production planning, recovery decisions, and internal competition among multiple product generations. Distinct from previous conclusions, this study explores how producers can strategically manage demand for new generations to slow diffusion, thereby increasing refurbishment and recycling volumes over time. Our findings highlight the pivotal role of adaptive pricing strategies and production scalability in maximizing profitability and promoting sustainability in competitive high-tech industries.

频繁的产品开发是消费电子行业缩短产品生命周期的一种解决方案。它使公司保持竞争力，加强市场份额。然而，环境问题使逆向物流实践成为焦点。回收政策是一种战略性的逆向物流活动，以提高市场份额；然而，这也带来了各种挑战和不确定性。考虑到不确定的需求，我们引入了一种具有两种不同回收政策的创新采用模型，即以旧换新和信贷，以解决多代生产计划中的挑战。受苹果和三星等公司现实实践的启发，我们的模型首先研究了以旧换新计划如何推动重复购买和提高市场份额，并利用基于信用的计划吸引新客户。然后，它捕获需求、生产计划、恢复决策和多代产品之间的内部竞争的变化。与之前的结论不同，本研究探讨了生产商如何从战略上管理对新一代产品的需求，以减缓扩散，从而随着时间的推移增加翻新和回收量。我们的研究结果强调了适应性定价策略和生产可扩展性在竞争激烈的高科技产业中最大化盈利能力和促进可持续性方面的关键作用。

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引用次数: 0

An adaptive K-means and reinforcement learning (RL) algorithm to effective vaccine distribution 基于自适应k -均值和强化学习（RL）算法的有效疫苗分配

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-11 DOI: 10.1016/j.cor.2025.107275

Elson Cibaku , İ. Esra Büyüktahtakın

We present a new adaptive reinforcement learning (RL) approach, integrated with a K-means clustering algorithm and guided by simulated annealing, to address the capacitated vehicle routing for vaccine distribution (CVRVD) problem. This integrated method provides an efficient and scalable solution for optimizing vaccine distribution logistics. By incorporating cost factors related to travel distance, inventory levels, and penalty terms – while adhering to delivery time windows – our approach improves both operational efficiency and vaccine allocation effectiveness. Experimental results demonstrate that our K-means supported RL algorithm significantly outperforms traditional solvers in tackling this NP-hard problem, particularly in large-scale scenarios. Specifically, our approach can efficiently solve CVRVD instances with up to 1,000 facilities—scenarios that are computationally intractable for exact methods. We demonstrate the effectiveness of the adaptive K-means supported RL algorithm using data from New Jersey, USA, where facility-level vaccination data were available through the state’s Immunization Information System. Beyond vaccine distribution, our method has broad applicability in logistics and transportation, enabling more efficient and cost-effective allocation of critical resources such as vaccines and medical supplies.

我们提出了一种新的自适应强化学习（RL）方法，结合K-means聚类算法，并以模拟退火为指导，来解决疫苗配送（CVRVD）的有能力车辆路线问题。这种综合方法为优化疫苗配送物流提供了一种高效、可扩展的解决方案。通过结合运输距离、库存水平和处罚条款等相关成本因素，同时遵守交货时间窗口，我们的方法提高了运营效率和疫苗分配效率。实验结果表明，我们的K-means支持RL算法在解决np困难问题方面明显优于传统的求解器，特别是在大规模场景中。具体来说，我们的方法可以有效地解决多达1000个设施的CVRVD实例，这些场景对于精确的方法来说是难以计算的。我们使用来自美国新泽西州的数据证明了自适应K-means支持的RL算法的有效性，其中设施级疫苗接种数据可通过该州的免疫信息系统获得。除了疫苗分发之外，我们的方法还广泛适用于物流和运输，使疫苗和医疗用品等关键资源的分配更加有效和具有成本效益。

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引用次数: 0

Q-learning-based hyper-heuristic algorithm for open dimension irregular packing problems 基于q学习的开维不规则包装问题超启发式算法

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-10 DOI: 10.1016/j.cor.2025.107279

Yongchun Wang , Qingjin Peng , Zhen Wang , Shuiquan Huang , Zhengkai Xu , Chuanzhen Huang , Baosu Guo

Heuristic methods provide a computationally efficient framework for addressing two-dimensional irregular packing problems, particularly in resource-constrained industrial settings. As a typical combinatorial optimization problem, irregular packing exhibits exponential growth in computational complexity with increasing workpiece counts, while the solution space dynamically reconfigures due to geometric variability among workpieces. Although heuristic algorithms can generate feasible layouts within acceptable timeframes, their reliance on fixed search rule limits adaptability to diverse scenarios, necessitating more flexible approaches. In this paper, a hyper-heuristic algorithm based on Q-Learning is proposed to solve open dimension packing problems, including one-open and two-open dimension problems. Q-Learning is adopted as the high-level strategy for its ability to optimize low-level heuristic selection through reward-driven experience accumulation. The method incorporates a mixed encoding method for solution representation, four specialized low-level heuristic operators, a linear population decline mechanism, and an elite preservation strategy to balance exploration–exploitation. The Q-Learning controller dynamically selects operators by updating the Q-table based on Bellman’s equation. The proposed algorithm is compared to some advanced algorithms in general datasets. The results show that our method has better performance and applicability.

启发式方法为解决二维不规则包装问题提供了一个计算效率高的框架，特别是在资源受限的工业环境中。不规则填充是一个典型的组合优化问题，其计算复杂度随着工件数量的增加呈指数增长，且求解空间由于工件之间的几何变化而动态重构。虽然启发式算法可以在可接受的时间范围内生成可行的布局，但它们对固定搜索规则的依赖限制了对不同场景的适应性，需要更灵活的方法。本文提出了一种基于q -学习的超启发式算法来解决开放维包装问题，包括一维和二维问题。Q-Learning能够通过奖励驱动的经验积累来优化低级启发式选择，因此被用作高级策略。该方法采用混合编码方法表示解，四个专门的低级启发式算子，线性种群下降机制和精英保存策略来平衡探索-开发。Q-Learning控制器根据Bellman方程，通过更新q表来动态选择算子。在一般数据集上与一些先进的算法进行了比较。结果表明，该方法具有较好的性能和适用性。

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引用次数: 0

Hybrid modelling using simulation and machine learning in healthcare 在医疗保健中使用仿真和机器学习的混合建模

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-10 DOI: 10.1016/j.cor.2025.107278

Ali Ahmadi , Masoud Fakhimi , Carin Magnusson

Modelling & Simulation (M&S) and Machine Learning (ML) methodologies have undergone significant advancements, enabling transformative applications across various industries. The integration of M&S and ML into a Hybrid M&S-ML approach leverages the unique strengths of both fields, offering enhanced model precision, improved efficiency, and more effective decision support. This review explores the increasing convergence of ML algorithms with traditional M&S methods- namely Agent-Based Modelling & Simulation, Discrete Event Simulation, and System Dynamics- in healthcare applications. Through a systematic review of 90 relevant studies, this article provides a comprehensive synthesis of the current state-of-the-art Hybrid M&S-ML in healthcare. Specifically, it examines the M&S and ML methodologies employed, associated software tools and programming languages, analyses integration patterns and data exchange mechanisms, and explores application domains, as well as the types and motivations for hybridisation. Key findings highlight prominent methodological and technical trends, as well as opportunities for combining M&S with ML to address healthcare challenges. These insights provide direction for modellers and data scientists in developing hybrid M&S–ML approaches that more effectively combine simulation capabilities with data-driven learning. The review also demonstrates the potential of such approaches to advance methodological innovation and support evidence-based decision-making in diverse healthcare contexts.

建模和仿真（M&；S）和机器学习（ML）方法已经取得了重大进展，使各种行业的变革性应用成为可能。将M&；S和ML集成到混合M&；S-ML方法中，利用了两个领域的独特优势，提供了更高的模型精度、更高的效率和更有效的决策支持。这篇综述探讨了ML算法与传统的M&；S方法（即基于agent的建模和仿真、离散事件仿真和系统动力学）在医疗保健应用中的日益融合。通过对90项相关研究的系统回顾，本文提供了当前最先进的混合M&；S-ML在医疗保健中的综合。具体来说，它检查了所采用的M&；S和ML方法，相关的软件工具和编程语言，分析了集成模式和数据交换机制，并探索了应用领域，以及混合的类型和动机。主要发现强调了突出的方法和技术趋势，以及将M&；S与ML相结合以应对医疗保健挑战的机会。这些见解为建模人员和数据科学家开发混合M& - ml方法提供了方向，这些方法可以更有效地将模拟功能与数据驱动的学习相结合。该综述还证明了这些方法在推进方法学创新和支持在不同医疗保健环境中基于证据的决策方面的潜力。

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引用次数: 0

A note on battery swapping policies in the electric vehicle routing problem with time windows and battery swapping vehicles 带时间窗和换电池车辆的电动汽车路径问题换电池策略研究

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-09 DOI: 10.1016/j.cor.2025.107277

Bülent Çatay , İhsan Sadati

Çatay and Sadati [An improved matheuristic for solving the electric vehicle routing problem with time windows and synchronized mobile charging/battery swapping. Computers & Operations Research 159, 106310, 2023] explores a variant of the Electric Vehicle Routing Problem with Time Windows that incorporates mobile chargers for recharging electric vehicles (EVs) at selected locations while serving customers. The authors propose a matheuristic method to address this problem and its special case, where EV batteries are swapped in constant time instead of being recharged over variable durations. While comparing their results with those in the literature, the authors overlook a critical assumption regarding the swapping policy, potentially causing confusion in interpreting the findings. This note addresses the issue, clarifies the overlooked assumption, and updates the results that do not align with the actual scenario in the literature. Furthermore, it introduces two new battery swapping policies and presents an extensive computational study to offer new insights on synchronized mobile battery swapping.

Çatay和Sadati[一种改进的数学方法，用于解决具有时间窗和同步移动充电/电池交换的电动汽车路径问题。]计算机与运运学[159,106310,2023]探讨了一种带有时间窗的电动汽车路线问题的变体，该问题结合了移动充电器，以便在为客户服务的同时在选定的地点为电动汽车充电。作者提出了一种数学方法来解决这个问题及其特殊情况，即电动汽车电池在固定时间内更换，而不是在可变时间内充电。在将他们的结果与文献中的结果进行比较时，作者忽略了一个关于交换策略的关键假设，这可能会导致对研究结果的解释混乱。本文解决了这个问题，澄清了被忽视的假设，并更新了与文献中实际场景不一致的结果。此外，本文还引入了两种新的电池交换策略，并进行了广泛的计算研究，为同步移动电池交换提供了新的见解。

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引用次数: 0

Grouping strategies on two-phase methods for bi-objective combinatorial optimization 双目标组合优化的两阶段分组策略

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-08 DOI: 10.1016/j.cor.2025.107254

Felipe O. Mota , Luís Paquete , Daniel Vanderpooten

Two-phase methods are commonly used to solve bi-objective combinatorial optimization problems. In the first phase, all extreme supported nondominated points are generated through a dichotomic search. This phase also allows the identification of search zones that may contain other nondominated points. The second phase focuses on exploring these search zones to locate the remaining points, which typically accounts for most of the computational cost. Ranking algorithms are frequently employed to explore each zone individually, but this approach leads to redundancies, causing multiple visits to the same solutions. To mitigate these redundancies, we propose several strategies that group adjacent zones, allowing a single run of the ranking algorithm for the entire group. Additionally, we explore an implicit grouping approach based on a new concept of coverage. Our experiments on the Bi-Objective Spanning Tree Problem demonstrate the beneficial impact of these grouping strategies when combined with coverage.

两阶段法是求解双目标组合优化问题的常用方法。在第一阶段，通过二分类搜索生成所有极端支持的非支配点。这一阶段还允许识别可能包含其他非主导点的搜索区域。第二阶段的重点是探索这些搜索区域以定位剩余的点，这通常占计算成本的大部分。排名算法经常用于单独探索每个区域，但这种方法会导致冗余，导致多次访问相同的解决方案。为了减轻这些冗余，我们提出了几种对相邻区域进行分组的策略，允许对整个组进行一次排序算法的运行。此外，我们还探索了一种基于覆盖新概念的隐式分组方法。我们在双目标生成树问题上的实验证明了这些分组策略在与覆盖相结合时的有益影响。

引用次数: 0

Learning-guided iterated local search for the minmax multiple traveling salesman problem 最小最大多旅行商问题的学习引导迭代局部搜索

IF 4.3 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-09-07 DOI: 10.1016/j.cor.2025.107255

Pengfei He , Jin-Kao Hao , Jinhui Xia

The minmax multiple traveling salesman problem involves minimizing the costs of a longest tour among a set of tours. The problem is of great practical interest because it can be used to formulate several real-life applications. To solve this computationally challenging problem, we propose a learning-driven iterated local search approach that combines an effective local search procedure to find high-quality local optimal solutions and a multi-armed bandit algorithm to select removal and insertion operators to escape local optimal traps. Extensive experiments on 77 commonly used benchmark instances show that the algorithm achieves excellent results in terms of solution quality and running time. In particular, it achieves 32 new best results (improved upper bounds) and matches the best-known results for 35 other instances. Additional experiments shed light on the understanding of the algorithm’s constituent elements. Multi-armed bandit selection can be used advantageously in other multi-operator local search algorithms.

最小最大多重旅行商问题涉及在一组旅行中最小化最长旅行的成本。这个问题具有很大的实际意义，因为它可以用来制定几个实际应用。为了解决这一具有计算挑战性的问题，我们提出了一种学习驱动的迭代局部搜索方法，该方法结合了有效的局部搜索过程来寻找高质量的局部最优解和多臂强盗算法来选择移除和插入算子以逃避局部最优陷阱。在77个常用的基准实例上进行的大量实验表明，该算法在求解质量和运行时间方面都取得了很好的效果。特别是，它实现了32个新的最佳结果（改进的上界），并与35个其他实例的最知名结果相匹配。更多的实验揭示了对算法组成要素的理解。多臂强盗选择在其他多算子局部搜索算法中具有优势。

引用次数: 0