Online Matching Research Articles

Day-Ahead Dynamic Assessment of Consumption Service Reserve Based on Morphological Filter

With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the historical actual power into typical scenarios. In addition, the online matching between the typical scenario and the day-ahead forecast power is conducted. In order to realize the hierarchical quantification of reserve demand, the reserve resources in the whole power system are classified according to their response time. Furthermore, the mathematical morphology filter based on the structural elements that are consistent with the response time of the hierarchical reserve resources is initially applied to decompose the historical forecast error of the matched scenarios. The simulation results verify that the proposed dynamic assessment effectively reduces the reserve cost on the basis of being able to cope with multi-time-scale power fluctuations.

Technical Note—Online Matching with Bayesian Rewards

In This IssueNavigating Dynamic Resource Allocation: A Bayesian ApproachIn “Online Matching with Bayesian Rewards,” D. Simchi-Levi, R. Sun, and X. Wang address an online matching problem where a central platform must allocate limited resources to user groups arriving sequentially over time. The paper innovatively considers the variability in the reward for each matching option, which depends on both the resource type and the user’s arrival time. The challenge lies in the fact that these matching rewards are initially unknown but are assumed to be drawn from known probability distributions. The platform is then tasked with learning these true rewards in real time based on the observed matching results. This intriguing exploration of online Bayesian matching techniques provides valuable insights for improving resource allocation in dynamic environments.

Matching on the Line Admits no \(o(\sqrt {\log n})\) -Competitive Algorithm

We present a simple proof that no randomized online matching algorithm for the line can be \((\sqrt {\log _2(n+1)}/15)\) -competitive against an oblivious adversary for any n = 2 i - 1 : i ∈ ℕ. This is the first super-constant lower bound for the problem, and disproves as a corollary a recent conjecture on the topology-parametrized competitiveness achievable on generic spaces.

Class Fairness in Online Matching

We initiate the study of fairness among classes of agents in online bipartite matching where there is a given set of offline vertices (aka agents) and another set of vertices (aka items) that arrive online and must be matched irrevocably upon arrival. In this setting, agents are partitioned into a set of classes and the matching is required to be fair with respect to the classes. We adopt popular fairness notions (e.g. envy-freeness, proportionality, and maximin share) and their relaxations to this setting and study deterministic and randomized algorithms for matching indivisible items (leading to integral matchings) and for matching divisible items (leading to fractional matchings). For matching indivisible items, we propose an adaptive-priority-based algorithm, MATCH-AND-SHIFT, prove that it achieves (1/2)-approximation of both class envy-freeness up to one item and class maximin share fairness, and show that each guarantee is tight. For matching divisible items, we design a water-filling-based algorithm, EQUAL-FILLING, that achieves (1-1/e)-approximation of class envy-freeness and class proportionality; we prove (1-1/e) to be tight for class proportionality and establish a 3/4 upper bound on class envy-freeness.

Fully Dynamic Online Selection through Online Contention Resolution Schemes

We study fully dynamic online selection problems in an adversarial/stochastic setting that includes Bayesian online selection, prophet inequalities, posted price mechanisms, and stochastic probing problems subject to combinatorial constraints. In the classical ``incremental'' version of the problem, selected elements remain active until the end of the input sequence. On the other hand, in the fully dynamic version of the problem, elements stay active for a limited time interval, and then leave. This models, for example, the online matching of tasks to workers with task/worker-dependent working times, and sequential posted pricing of perishable goods. A successful approach to online selection problems in the adversarial setting is given by the notion of Online Contention Resolution Scheme (OCRS), that uses a priori information to formulate a linear relaxation of the underlying optimization problem, whose optimal fractional solution is rounded online for any adversarial order of the input sequence. Our main contribution is providing a general method for constructing an OCRS for fully dynamic online selection problems. Then, we show how to employ such OCRS to construct no-regret algorithms in a partial information model with semi-bandit feedback and adversarial inputs.

Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual

Online bipartite-matching platforms are ubiquitous and find applications in important areas such as crowdsourcing and ridesharing. In the most general form, the platform consists of three entities: two sides to be matched and a platform operator that decides the matching. The design of algorithms for such platforms has traditionally focused on the operator’s (expected) profit. Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator’s profit. In this paper, we generalize the existing work to offer fair treatment guarantees to both sides of the market simultaneously, at a calculated worst case drop to operator profit. We consider group and individual Rawlsian fairness criteria. Moreover, our algorithms have theoretical guarantees and have adjustable parameters that can be tuned as desired to balance the trade-off between the utilities of the three sides. We also derive hardness results that give clear upper bounds over the performance of any algorithm.

Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes

An online problem called dynamic resource allocation with capacity constraints (DRACC) is introduced and studied in the realm of posted price mechanisms. This problem subsumes several applications of stateful pricing, including but not limited to posted prices for online job scheduling and matching over a dynamic bipartite graph. Because existing online learning techniques do not yield vanishing regret for this problem, we develop a novel online learning framework over deterministic Markov decision processes with dynamic state transition and reward functions. Following that, we prove, based on a reduction to the well-studied problem of online learning with switching costs, that if the Markov decision process admits a chasing oracle (i.e., an oracle that simulates any given policy from any initial state with bounded loss), then the online learning problem can be solved with vanishing regret. Our results for the DRACC problem and its applications are then obtained by devising (randomized and deterministic) chasing oracles that exploit the particular structure of these problems. Funding: The work of Y. Emek was supported in part by the Israel Science Foundation [Grant 1016/17]. The work of R. Lavi was partially supported by the Israel Science Foundation [Grant 2560/17] and the National Natural Science Foundation of China [Grant 2560/17]. The work of R. Niazadeh was supported by the University of Chicago Booth School of Business. The work of Y. Shi was partially supported at the Technion by the Council for Higher Education [fellowship], and at Shandong University by the Science Fund Program of Shandong Province for Distinguished Oversea Young Scholars [Grant 2023HWYQ-006].

Imperfect-Information Game AI Agent Based on Reinforcement Learning Using Tree Search and a Deep Neural Network

In the field of computer intelligence, it has always been a challenge to construct an agent model that can be adapted to various complex tasks. In recent years, based on the planning algorithm of Monte Carlo tree search (MCTS), a new idea has been proposed to solve the AI problems of two-player zero-sum games such as chess and Go. However, most of the games in the real environment rely on imperfect information, so it is impossible to directly use the normal tree search planning algorithm to construct a decision-making model. Mahjong, which is a popular multiplayer game with a long history in China, attracts great attention from AI researchers because it contains a large game state space and a lot of hidden information. In this paper, we utilize an agent learning approach that leverages deep learning, reinforcement learning, and dropout learning techniques to implement a Mahjong AI game agent. First, we improve the state transition of the tree search based on the learned MDP model, the player position variable and transition information are introduced into the tree search algorithm to construct a multiplayer search tree. Then, the model training based on a deep reinforcement learning method ensures the stable and sustainable training process of the learned MDP model. Finally, we utilize the strategy data generated by the tree search and use the dropout learning method to train the normal decision-making agent. The experimental results demonstrate the efficiency and stability performance of the agent trained by our proposed method compared with existing agents in terms of test data accuracy, tournament ranking performance, and online match performance. The agent plays against human players and acts like real humans.

Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis

Dynamic resource allocation problems arise under a variety of settings. In “Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis,” Balseiro, Besbes, and Pizarro introduce a unifying model for a large class of dynamic optimization problems dubbed dynamic resource-constrained reward collection (DRC2) problems. Surveying the literature, they show that this class encompasses a variety of disparate and classical problems typically studied separately, such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, or order fulfillment. Furthermore, they establish that the DRC2 class is amenable to analysis by characterizing the performance of a central, certainty-equivalent heuristic. Notably, they provide a novel unifying analysis that isolates the drivers of performance, recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and yields new results in specialized settings for which no such characterization was available.

Matching while learning: Wireless scheduling for age of information optimization at the edge

In this paper, we investigate the minimization of age of information (AoI), a metric that measures the information freshness, at the network edge with unreliable wireless communications. Particularly, we consider a set of users transmitting status updates, which are collected by the user randomly over time, to an edge server through unreliable orthogonal channels. It begs a natural question: with random status update arrivals and obscure channel conditions, can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI? To give an adequate answer, we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints. Then, we propose an online matching while learning algorithm (MatL) and discuss its implementation for wireless scheduling. Finally, simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users' buffers for fresher information at the edge.

Optimization of ride-sharing with passenger transfer via deep reinforcement learning

With the emergence of the sharing economy and the rapid growth of mobile communications technologies, many novel sharing service models have been developed stemming from ride-hailing. Urban traffic congestion, coupled with energy conservation and emissions reduction, has prompted research on enhancing vehicle seat utilization in taxi service. To offer more effective and reliable ride-hailing, we consider ride-sharing problem with passenger transfer that allows passegers to transfer between vehicles at transfer stations. The problem requires simultaneous addressing the issues of request dispatching, transfer scheduling, and vehicle rebalancing. Studying such a ride-hailing model, we propose a novel joint decision framework combining deep reinforcement learning (DRL) with integer-linear programming (ILP) to solve the problem. We use ILP to obtain the optimal online dispatching and matching strategy in each decision stage, and DRL to learn the approximate state value of each vehicle that incorporates with some strategies to limit the state space and reduce the computational complexity. Performing numerical studies on the real-world trip dataset in Chengdu, we demonstrate that the proposed method outperforms several state-of-the-art methods, and that ride-sharing with passenger transfer is more beneficial than traditional ride-sharing.

Optional Verification and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment

Online matching platforms could lack common informational mechanisms, such as ratings and reviews, that serve to reduce information asymmetry in transactional platforms. The lack of verified information about participants further exacerbates issues of information asymmetry in such markets. Our study focuses on a novel role of verification in such matching markets—its ability to serve as a credible signal for a user, when such verification is made optional and visible to other users. In collaboration with a leading online dating platform with no reputation mechanisms and where most of the information is self-disclosed, we design and conduct a randomized field experiment to examine not only who chooses to verify but also, the effectiveness of such optional verification for different types of users. We identify that a simple-to-implement mechanism, such as phone verification, when made optional can take on additional significance in platforms that lack alternate reputation and transaction-assurance mechanisms, especially for those in early years or those that lack other credible mechanisms to verify important information about participants. Our findings also provide insights into how optional verification has heterogeneous impacts on different platform users and can also facilitate desirable matching and benefit the platform as a whole, paving the way for examining other similar verification mechanisms.

Assortment Planning for Recommendations at Checkout Under Inventory Constraints

In this paper, we consider a personalized assortment planning problem under inventory constraints, where each arriving customer type is defined by a primary item of interest. As long as that item is in stock, the customer adds it to the shopping cart, at which point the retailer can recommend to the customer an assortment of add-ons to go along with the primary item. This problem is motivated by the new “recommendation at checkout” systems that have been deployed at many online retailers, and it also serves as a framework that unifies many existing problems in online algorithms (e.g., personalized assortment planning, single-leg booking, and online matching with stochastic rewards). In our problem, add-on recommendation opportunities are eluded when primary items go out of stock, which poses additional challenges for the development of an online policy. We overcome these challenges by introducing the notion of an inventory protection level in expectation and derive an algorithm with a 1/4-competitive ratio guarantee under adversarial arrivals. Funding: This work was supported by the Adobe Data Science Research Award and the Alibaba Innovation Research Award. L. Xin was partly supported by the National Science Foundation (NSF) [Award CMMI-1635160], X. Chen was supported by the NSF [CAREER Award IIS-1845444]. W. Ma and D. Simchi-Levi were supported by the Accenture and MIT Alliance in Business Analytics.

Molecular disparity of HLA-DPB1 is associated with the development of subsequent solid cancer after allogeneic hematopoietic stem cell transplantation.

An increased incidence of subsequent solid cancers (SSCs) has been reported in long-term survivors of allogeneic hematopoietic stem cell transplantation (allo-HSCT), and SSC is associated with inferior mortality and morbidity. Previous studies showed that the incidence of SSC is significantly higher in those who underwent allo-HSCT from HLA-mismatched donors, suggesting that persistent alloimmunity may predispose patients to SSCs. It was recently reported that, in a cohort of patients who received allo-HSCT from an unrelated donor matched at HLA-A, -B, -C, -DRB1/3/4/5, and -DQB1 loci, HLA-DPB1 alloimmunity determined by high mismatched eplets (MEs) and Predicted Indirectly Recognizable HLA Epitopes (PIRCHE) score (PS), was associated with relapse protection and increased risk of acute graft-versus-host disease (GVHD). In the present study, the impact of HLA-DPB1 alloimmunity assessed by molecular mismatch algorithms on the development of SSCs in a cohort of 1514 patients who underwent allo-HSCT for hematologic malignancies was further investigated. ME load at the HLA-DPB1 locus was measured using the HLAMatchmaker module incorporated in HLA Fusion software, and the PS for mismatched HLA-DPB1 was calculated using the HSCT module from the PIRCHE online matching service. In multivariable analysis after adjusting for baseline risk factors, higher ME, PS-I, and PS-II in the GVH direction, but not in the HVG direction, were associated with an increased risk of SSCs (ME: subdistribution hazard ratio [SHR] 1.58, p= .01; PS-I: SHR 1.59, p= .009; PS-II: SHR 1.71, p= .003). In contrast, nonpermissive HLA-DPB1 mismatches defined by the conventional T-cell epitope algorithm were not predictive of the risk of SSCs. Moreover, posttransplant cyclophosphamide-based GVHD prophylaxis was associated with a reduced risk of subsequent solid cancer (SHR 0.34, p= .021). These results indicate for the first time that increased GVH alloreactivity could contribute to the development of SSCs in allo-HSCT survivors.

Long-Term Matching Optimization With Federated Neural Temporal Difference Learning in Mobility-on-Demand Systems

Efficient bipartite graph matching of orders and drivers is a central operational problem in industrial mobility-on-demand (MOD) systems. Traditional studies adopt pure combinatorial optimization models for order-and-driver matching, which do not consider the long-term rewards of the dynamic MOD decision making process. Toward long-term optimization, this article presents a systemic paradigm of online matching with federated neural temporal difference learning (FedTDLearning), which encompasses learning and matching phases. During the learning phase, the long-term matching process is modeled as a Markov decision process, which is typically solved by employing data-driven reinforcement learning in an offline central training scheme. Massive amounts of data would be generated on the network by industrial MOD systems. It is impossible to send all the large-scale industrial data to the cloud server for centralized model training due to network bandwidth limitations and safety concerns. Therefore, a generic and innovative FedTDLearning is proposed to achieve long-term matching in a distributed manner. During the matching phase, a real-time bipartite matching optimization problem is formulated to maximize the learned spatiotemporal value and minimize the pickup distance, which is reducible to the minimum-cost maximum weight bipartite graph matching problem. A distance-learned-value ratio algorithm is proposed to find an optimal matching in the bipartite graph based on the joint optimization of FedTDLearning and the combinatorial fractional programming approach. Furthermore, to pursue optimal computation efficiency, we solve the real-time matching problem by constructing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k$ </tex-math></inline-formula> NN) bipartite graph where each order is connected with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN drivers. Using openly available real-world data, the prototype system and experimental evaluations show that our proposed algorithms have effective problem-solving capability in practice.

Online merchant resource allocation and matching for open community collaborative manufacturing (OCCM) in mass personalization model

In order to meet the requirements of customers' pursuit of personalized and diversified needs, enterprises need to change from the traditional mass manufacturing model to mass personalization manufacturing model. On the other hand, collaborative manufacturing based on stakeholders will effectively solve the problems of information asymmetry, low operational efficiency and insufficient resource utilization in the process of manufacturing. Moreover, the new model of open community manufacturing is conducive to the planning, allocation, optimization and management of various networked and social resources. Therefore, combined with above three innovative models of mass personalization manufacturing, collaborative manufacturing and open community manufacturing, this paper studies the online merchant resource allocation and matching of open community collaborative manufacturing (OCCM) for mass personalization (MP) model to realize the innovation of design model, improve design efficiency and save design resource cost. Firstly, the workflow for OCCM in MP model based on system engineering analysis method is studied. Secondly, online merchant resource allocation for OCCM based on social network analysis (SNA) and online merchant resource matching for OCCM based on fuzzy clustering are proposed. At last, a case study for OCCM in the automotive industry is given to verify the effectiveness of the proposed model and methods.

Development of an Online Matching System (OMS) for Studying in the Graduate Program

Database management systems are essential for achieving competitiveness, enabling organizations to formulate proactive strategies matching their target audience effectively. Thus, this study aimed to develop an online matching system (OMS) for studying in the Graduate Program at Srinakharinwirot University. In this case, we used the System Development Life Cycle (SDLC), a conceptual model that includes system planning, analysis, design, development, and implementation as well as operation. System efficiency evaluation by three experts and satisfaction with system evaluation by 60 users evaluated the system performance of the research instruments. According to the findings, the OMS for studying in the Graduate Program received an excellent rating (X̅ = 4.72), and the overall satisfaction of using this database system was high (X̅ = 4.28). The OMS for studying in the Graduate Program is critical toward value creation that suits the requirements of the students and enhances marketability to reach the target effectively.

Fairness Maximization among Offline Agents in Online-Matching Markets

Online matching markets (OMMs) are commonly used in today’s world to pair agents from two parties (whom we will call offline and online agents) for mutual benefit. However, studies have shown that the algorithms making decisions in these OMMs often leave disparities in matching rates, especially for offline agents. In this article, we propose online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fairness maximization. We derive further bounds based on fairness parameters, demonstrating conditions under which the competitive ratio can increase to 100%. There are two key ideas helping us break the barrier of 1-1/𝖾~ 63.2% for competitive ratio in online matching. One is boosting , which is to adaptively re-distribute all sampling probabilities among only the available neighbors for every arriving online agent. The other is attenuation , which aims to balance the matching probabilities among offline agents with different mass allocated by the benchmark LP. We conduct extensive numerical experiments and results show that our boosted version of sampling algorithms are not only conceptually easy to implement but also highly effective in practical instances of OMMs where fairness is a concern.

Multi-spectral template matching based object detection in a few-shot learning manner

Multi-spectral template matching (MSTM) based object detection approaches can be widely used in robotics and aerospace systems for fine-grained object discovery. However, the performance of existing nearest neighbor search based nonparametric paradigms (e.g., correlation coefficient and lp-norm) turns out to be unsatisfactory. These paradigms tend to suffer from two defects: 1) they fail to select from the raw features the discriminative ones that can help distinguish between the target and background; 2) the domain shift between the template and search spectra has not been well addressed within the feature space. In this work, we propose a data-driven MSTM method to address these two issues. First, Exemplar-SVM (E-SVM) is applied to execute feature selection and target/background categorization jointly, which is facilitated by its max-margin mechanism. To enable the learning process where the template is regarded as a single positive sample, knowledge transfer is executed to attain negative samples from other domains, e.g., large-scale public datasets. Then, the hard negative samples are mined to help train a discriminative classifier. Concerning practical applications, we also augment the template with different image degradations and extend E-SVM from the original one-shot learning approach to its few-shot version. Second, a multi-domain adaptation approach via unsupervised multi-domain subspace alignment is proposed to tackle multi-domain shift problem. Here the multiple domains relate to template, search, and negative ones considering both offline learning and online matching. The wide-range experimental results on two multi-spectral datasets demonstrate the effectiveness of our method. The tailored dataset and code will be released publicly.

Correction to: Markovian Online Matching Algorithms on Large Bipartite Random Graphs

Correction to: Markovian Online Matching Algorithms on Large Bipartite Random Graphs