Matching Policy Research Articles

Multi-Objective Online Ride-Matching

We study the following multi-period multi-objective online ride-matching problem. A ride-sourcing platform needs to match passengers and drivers in real time without observing future information, considering multiple objectives such as platform revenue, pick-up distance, and service quality. We develop an efficient online matching policy that adaptively balances the trade-offs among multiple objectives in a dynamic setting, and provide theoretical performance guarantee for the policy. We prove that the proposed adaptive matching policy can achieve a “target-based optimal solution”, i.e., a solution that minimizes the Euclidean distance to any pre-determined multi-objective target. Specifically, the outcome under our policy converges to the “compromise solution” if we set the utopia point as the target. Through numerical experiments and industrial testing using real data from a ride-sourcing platform, we demonstrate that our approach indeed obtains solutions that are closest to the pre-determined targets under various settings, in comparison to existing approaches. The policy presents solutions with delicate balance among multiple objectives and brings value to all the stakeholders in the ride-sourcing ecosystem comparing to benchmark policies: (1) drivers with higher service scores are dispatched with more orders and receive higher incomes; (2) passengers are more likely to be served by drivers with higher service scores, and passengers with higher order revenues are served with higher answer rates, at the expense of a small increase in pick-up distance; (3) the platform obtains a higher total revenue.

Information hiding based on Augmented Reality.

Information hiding aims to achieve secret communication via certain carrier. However, these carrier-based methods often have different kinds of deficiencies. In order to solve the problems addressed by the traditional information hiding methods such as the difficult balance between secret embedding rate and detection rate, this paper proposes a novel approach which utilizes Augmented Reality (AR) to achieve secret communication. In this paper, we present an AR based information hiding architecture which combines information hiding, augmented reality, and deep learning methods altogether. The proposed architecture basically follows the idea of secret-key matching policy. The secret sender first maps the secret message to objects, images or coordinates, etc. The mapped objects, images or coordinates then serve as the secret key for further secret revealing. The secret key and concealing model are shared between two communication parties instead of direct transmitting the secret messages. Different secret keys can be combined in order to generate more mapping sequences. Also, deep learning based models are integrated in the architecture to extend the mapping varieties. By taking advantage of the augmented reality technique, the secret messages can be transmitted in various formats which results in higher secret embedding rate in potential. Furthermore, the proposed architecture can be seen as a useful application of coverless information hiding scheme. The experimental system realizes the proposed architecture by implementing convolutional neural network (CNN) based real-time object detection, image recognition, augmented reality and secret-key matching altogether which shows great promise in practice.

Price Match Guarantees in the Age of Showrooming: An Empirical Analysis

Consumer showrooming – the behavior of examining a product in a brick-andmortar store and later buying it from an online retailer – is seen as a major threat to brick-and-mortar retailers. To combat showrooming, Best Buy announced a price-matching policy in 2012 to compete with major online retailers. In this paper, we examine the impact of Best Buy’s price-matching policy on the price competition between Best Buy and Amazon across a wide variety of product categories. We empirically explore Best Buy’s and Amazon’s pricing patterns using unique datasets collected from different sources, and find robust results that the competitive effect of the price-matching policy depends on the showrooming value of a product. For those products that offer consumers large value from physical store experiences – i.e., the “showrooming products” – the policy led to more intense price competition. Moreover, Amazon cut prices more aggressively than Best Buy. For those products that offer relatively small showrooming value – i.e., the “non-showrooming products” – it alleviated price competition. We also provide theoretical explanations for the findings and illustrate why the price matching policy did not reduce the price gaps between Amazon and Best Buy.

Impact of Workforce Flexibility on Customer Satisfaction: Empirical Framework & Evidence from a Cleaning Services Platform

Problem definition: Contrary to classic applications of matching theory, in most contemporary on-demand service platforms, matches can not be enforced because workers are flexible – they choose their tasks. Such flexibility makes it difficult to manage workers while keeping customers satisfied. We build a framework to compare platform matching policies with less flexible and more flexible workers, and empirically quantify by how much worker flexibility hurts customer satisfaction and customer equity. Academic/Practical relevance: In academic literature, there is no established framework that allows for the comparison of matching policies in on-demand platforms. Further, the link between worker flexibility and customer satisfaction is understudied. Methodology: We propose a tripartite framework for empirical evaluation and comparison of the operational policies with different degrees of worker flexibility. Step 1: Predictive modeling of customer satisfaction based on estimation of individual unobservable characteristics: customer difficulty and worker ability (item-response theory model). Step 2: Evaluation of the effect of matching policy (under a given level of flexibility) on customer satisfaction (bipartite matching). Step 3: Quantification of the associated monetary impact (customer lifetime value model). Results: We apply our framework to the dataset of one of the world's largest on-demand platforms for residential cleanings. We find that customer difficulty and cleaner ability are good predictors of customer satisfaction. Granting full flexibility to workers reduces customer satisfaction by 3% and customer lifetime revenue by 0.2%. We propose a family of matching policies that provide sufficient flexibility to workers, while alleviating 75% of the detrimental effect of worker flexibility on customer satisfaction. Managerial implications: Our results suggest that, in platforms with flexible workforce, the presence of worker and customer heterogeneity reduces customer satisfaction through matching inefficiency. Our empirical framework helps practitioners to decide on the right level of worker flexibility and the means for achieving it.

Power Sharing Between Microgrids Based on a Secondary Market

Microgrids are popular for severing local electricity loads. This paper considers the power-sharing problem between the microgrids. First, it proposes a secondary market (SM), which allows the users to submit their bids for buying or selling the power. The SM should be managed by an authoritative agent, so we let the main grid (MG) act as the administrator. In such a market, the buyers, sellers, and MG should get certain rewards. Otherwise, they have no incentive to join in the sharing process. For this reason, this paper first studies the optimal matching policy of the MG to maximize its rewards. Then, it explores the advantage of SM and shows that both the users and MG can gain certain benefit. In addition, the secondary market can encourage the users to consume more necessary power to improve their life quality. Finally, this paper proposes a multiple-biding policy, where the users can adjust the future bids based on the past transaction. Numerical results show that this policy can improve the satisfactory degree. When the utility by consuming the power is bigger, this policy is good for the users.

Editorial Note

Editorial Note

A double-ended queueing model for dynamic allocation of live organs based on a best-fit criterion

We propose a novel approach, based on a Human Leukocyte Antigen (HLA) best-fit criterion, to dynamically allocate live organs (specifically, kidneys) to candidates needing transplantation. A ‘reward’ is assigned to each level of HLA fit, such that higher rewards are attributed to transplants between better-matched candidates and kidneys. We also envision future technologies by which it will be possible to store organs so that two queues may form: waiting candidates or stored kidneys. Consequently, a double-ended queue of candidates and kidneys is constructed, where the lifetime of a stored kidney is random, and candidates queueing for transplantation may die (‘renege’) while waiting. We derive expressions for the probability that a candidate gets a kidney before reneging; for the mean numbers of waiting candidates and of stored kidneys; and for a candidate's or kidney's mean sojourn time. Assuming a best-HLA-fit matching policy, we study three measures of effectiveness: (i) Rate of Reward from Transplantation (RRT); (ii) Expected Reward per Transplantation (ERT), calculated as RRT divided by the rate of performed transplantations, and (iii) Gained rate of reward per one dollar of expenditure. The optimal fraction of kidneys that should be stored so as to maximize the rate of reward per one dollar of expenditure is numerically determined.

Joint Pricing and Matching in Ridesharing Systems

Ride-sharing firms use pricing and matching decisions to control the ride-sharing platforms. Those decisions can be made jointly or interchangeably, which raises the following questions: Is matching optimization necessary? Specifically, is fixing the matching decisions to a simple rule and optimizing only the pricing decisions enough to achieve the optimal performance? In order to answer these questions, we study the interplay between pricing and matching decisions of a ride-sharing firm. There are many studies in the ride-sharing literature that optimize the pricing decisions under an assumed matching policy. However, we show that ignoring matching optimization can result in subpar overall performance. We formulate a stylized ride-sharing model that captures customer and driver behaviors and geospatial nature of the system. Customers are both price and delay sensitive, and drivers are strategic and self-scheduling. We prove that optimizing the matching decisions have first-order effect on the system performance. We show that fixing the matching decisions and optimizing only the pricing decisions does not maximize the number of matchings in general. Similarly, we show that fixing the pricing decisions and optimizing only the matching decisions is not optimal in general. Finally, we show that optimizing in only one dimension (either pricing or matching) has no benefit to the firm under some conditions, whereas joint pricing and matching optimization can lead to a significant performance increase.

On the instability of matching queues

A matching queue is described via a graph, an arrival process and a matching policy. Specifically, to each node in the graph there is a corresponding arrival process of items, which can either be queued or matched with queued items in neighboring nodes. The matching policy specifies how items are matched whenever more than one matching is possible. Given the matching graph and the matching policy, the stability region of the system is the set of intensities of the arrival processes rendering the underlying Markov process positive recurrent. In a recent paper, a condition on the arrival intensities, which was named NCOND, was shown to be necessary for the stability of a matching queue. That condition can be thought of as an analogue to the usual traffic condition for traditional queueing networks, and it is thus natural to study whether it is also sufficient. In this paper, we show that this is not the case in general. Specifically, we prove that, except for a particular class of graphs, there always exists a matching policy rendering the stability region strictly smaller than the set of arrival intensities satisfying NCOND. Our proof combines graph- and queueing-theoretic techniques: After showing explicitly, via fluid-limit arguments that the stability regions of two basic models is strictly included in NCOND, we generalize this result to any graph inducing either one of those two basic graphs.

Kidney exchange simulation and optimization

One of the challenges in a kidney exchange program (KEP) is to choose policies that ensure an effective and fair management of all participating patients. In order to understand the implications of different policies of patient allocation and pool management, decision makers should be supported by a simulation tool capable of tackling realistic exchange pools and modeling their dynamic behavior. In this paper, we propose a KEP simulator that takes into consideration the wide typology of actors found in practice (incompatible pairs, altruistic donors, and compatible pairs) and handles different matching policies. Additionally, it includes the possibility of evaluating the impact of positive crossmatch of a selected transplant, and of dropouts, in a dynamic environment. Results are compared to those obtained with a complete information model, with knowledge of future events, which provides an upper bound to the objective values. Final results show that shorter time intervals between matches lead to higher number of effective transplants and to shorter waiting times for patients. Furthermore, the inclusion of compatible pairs is essential to match pairs of specific patient–donor blood type. In particular, O-blood type patients benefit greatly from this inclusion.

Efficient Dynamic Barter Exchange

We study dynamic matching policies in a stochastic marketplace for barter, with agents arriving over time. Each agent is endowed with an item and is interested in an item possessed by another agent homogeneously with probability p, independently for all pairs of agents. Three settings are considered with respect to the types of allowed exchanges: (a) only two-way cycles, in which two agents swap items, (b) two-way or three-way cycles, (c) (unbounded) chains initiated by an agent who provides an item but expects nothing in return. We consider the average waiting time as a measure of efficiency and find that the cost outweighs the benefit from waiting to thicken the market. In particular, in each of the above settings, a policy that conducts exchanges in a greedy fashion is near optimal. Further, for small p, we find that allowing three-way cycles greatly reduces the waiting time over just two-way cycles, and conducting exchanges through a chain further reduces the waiting time significantly. Thus, a centralized planner can achieve the smallest waiting times by using a greedy policy, and by facilitating three-way cycles and chains, if possible. The online appendix is available at https://doi.org/10.1287/opre.2017.1644 .

Novel dynamic formulations for real-time ride-sharing systems

This paper proposes new objective functions for the matching problem arising in ride-sharing systems based on trips’ spatial attributes. Novel dynamic matching policies are then proposed to solve the problem dynamically in a rolling horizon framework. Finally, we present a new clustering heuristic to tackle instances with a large number of participants efficiently. We find that the proposed models maximize the matching rate while maintaining distance-savings at an acceptable level, which is an appealing achievement for ride-sharing systems. Further, our solution method is capable of solving large-scale instances in real-time.

Assessment of the incentives on electric vehicle promotion in China

New Electric vehicles (NEVs) will not only solve the energy and environmental problems, but also promote reform and transformation of Chinese automotive industry. To promote the market acceptance of NEVs, Chinese government has launched NEV demonstration projects and issued numerous policies since 2009, which absolutely promoted NEV sales, but also resulted in a subsidies-oriented NEV market. Therefore, Chinese government decided to decrease NEV subsidies gradually in next 5years. In order to explore the key factors that promote NEV sales, and based on which to offer suggestions on designing and formulating matching policies when subsidies decrease, we utilized multiple linear regression method to analyze electric vehicle (EV) sales with incentive measures and socio-demographic data of 41 pilot cities for year 2013–2014. The result shows that chargers’ density, license fee exemption, no driving restriction, and give priority to charging infrastructure construction lands are the four most important factors, which could be continued and strengthened in next few years to keep EV market still present a perfect performance.

Red blood cell alloimmunization in 184 patients with myeloid neoplasms treated with azacitidine – A retrospective single center experience

Alloimmunization to Red Blood Cell (RBC) antigens frequently occurs in patients with myeloid neoplasms (AML, MDS and CMML) and potentially poses the patient at risk for delayed hemolytic transfusion reactions and limited supply of compatible RBC-units. However, there is comparatively little data on transfusion associated characteristics in this patient cohort. We therefore retrospectively analyzed transfusion requirements and clinical outcomes of 184 patients with myloid neoplasms treated with azacitidine at the Paracelsus Medical University Salzburg, which were included in the Austrian Registry of Hypomethylating Agents.The mean blood component requirements for AML, MDS and CMML were 39.8, 67.4 and 31.4 RBC units and 31.7, 27.6 and 19.1 platelet (PLT) units respectively. In spite of an extended and stringent RBC unit matching policy (ABO, RhD, RhCcEe and K antigens), 20 (11%) patients formed at least one alloantibody (“allo-group”), whereas 164 patients (89%) did not (“non-allo-group”). The most frequent antibody specificity was anti-E, followed by anti-Wra −Lua, −D, −C and −Jka. Alloimmunization was associated with higher numbers of transfused RBC units (68 vs. 38; p=0.001), as well as with longer time under transfusion (16.7 vs. 9.4 months; p=0.014). Median overall survival (OS) did not differ significantly between the “allo”- and “non-allo-group”.

Stability of the stochastic matching model

Abstract We introduce and study a new model that we call the matching model. Items arrive one by one in a buffer and depart from it as soon as possible but by pairs. The items of a departing pair are said to be matched. There is a finite set of classes 𝒱 for the items, and the allowed matchings depend on the classes, according to a matching graph on 𝒱. Upon arrival, an item may find several possible matches in the buffer. This indeterminacy is resolved by a matching policy. When the sequence of classes of the arriving items is independent and identically distributed, the sequence of buffer-content is a Markov chain, whose stability is investigated. In particular, we prove that the model may be stable if and only if the matching graph is nonbipartite.

Threshold Power Based UE Admittance and Contention Free Resource Allocation for Interference Mitigation in Cognitive Femtocells

Femtocells are aimed at providing strong coverage in the indoor area where typical macrocell coverage is very poor. It has hugely attracted network operators and stakeholders mainly due to its simple plug and play operation and low cost. Femtocells operate on a much lower power compared to macrocell and thus provide a number of benefits including energy efficiency and frequent spatial reuse of the spectrum. Femtocells are overlaid on macrocells and designed to co-exist with them sharing the same spectrum pool. However, since they are deployed by the end user, no pre-deployment resource planning is possible. So, interference among the femtocells as well as between femtocells and macrocells remain a major bottleneck for successful operation of femtocell networks. This paper proposes a novel threshold power based admittance and contention free resource allocation for interference mitigation in cognitive femtocell networks. In our proposed scheme, a Femtocell Access Point with Cognitive radio capability known as Cognitive Femtocells (CF), sets a threshold value on the mutual interference between itself and a close by macrocell user equipment (MUE). To mitigate cross-tier interference, a CF classifies MUEs with higher than threshold interference value as Undesirable MUEs (UMUEs) and subsequently admits it as one of its user equipment. MUEs with lower than threshold interference values are classified as Desirable MUEs (DMUEs). To mitigate co-tier interference, proposed scheme introduces a scheduling engine which employs matching policy attributes and assigns resource blocks (RBs) of unique DMUEs to CFs to avoid any possible contention problems, thus providing improved co-tier interference. System level simulations have been performed to demonstrate effectiveness of scheme and significant performance improvement in terms of SINR, throughput and spectrum efficiency.

Pricing and Matching with Forward-Looking Buyers and Sellers

Motivated by the emerging sharing economy, we study a dynamic market of a single product or service, to which buyers with heterogeneous private valuations and sellers with heterogeneous private supply costs sequentially arrive following Poisson processes. A single market making intermediary dynamically decides on the ask and bid prices posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell, respectively. Buyers and sellers can strategically wait for better prices after their arrivals. The intermediary’s problem is a multi-dimensional mechanism design problem that is hard to analyze. We propose a simple heuristic policy, that is, a fixed ask price charged to the buyers and a fixed bid price offered to the sellers plus a price adjustment as compensation for expected waiting cost, in conjunction with the greedy matching policy on a first-come first-served basis. These fixed base prices balance demand and supply in expectation, and can be computed efficiently. The waiting adjusted ask and bid price processes are time dependent and tend to have opposite trends in the beginning and end of the horizon. Under this heuristic policy, forward-looking buyers and sellers behave myopically without wait. We show that this heuristic policy is asymptotically optimal for the intermediary and the waiting time compensation becomes negligible, as the volumes of demand and supply arrivals proportionally grow. Our results suggest that the random arrivals of heterogeneous buyers and sellers, alone, may not be sufficient to justify contingent pricing. Given Uber changes its prices every five minutes to rapidly micro-match supply with demand, our result suggests Uber may not lose much optimality by reducing the frequency of price changes, unless the underlying market conditions have significantly changed. This is because our fixed pricing has captured the first-order effect and the benefit of surge pricing diminishes as the volumes of the platform grow, not to mention frequent surge pricing may agonize riders and induce strategic behaviors of riders and drivers that are hard to account for.

Prophylactic RhCE and Kell antigen matching: impact on alloimmunization in transfusion‐dependent patients with myelodysplastic syndromes

Thirty to 80 per cent of patients with myelodysplastic syndromes (MDS) become transfusion-dependent and are at risk for red blood cell (RBC) alloimmunization. This study compared alloimmunization rates in transfusion-dependent patients with MDS at an institution with a policy of prophylactic antigen matching for RhCE and K (PAM) with those transfused at institutions without such a policy (non-PAM). Transfusion records were retrospectively reviewed to determine total number of RBC transfusions received, whether RBC phenotyping was performed, the type and date of first alloantibody development and receipt of prophylactic antigen matching for RhCE and K. In 176 transfusion-dependent patients with MDS, the overall rate of new alloimmunization was 17%; the majority of patients (87%) developed at least one alloantibody to Rh or Kell antigens. The alloimmunization rate at the institution with a PAM policy was 11% compared with 23% at non-PAM institutions (P = 0·06). The rate of Rh/K alloimmunization was 7 vs. 22%, respectively (P = 0·008). No patient who received PAM developed a Rh/K alloantibody. The rate of alloimmunization was 11% at an institution with a PAM policy which was non-significantly lower than 23% at institutions without a PAM policy. However, rates of Rh/K alloimmunization were significantly lower. Such a policy should be considered in transfusion-dependent patients with MDS, although further studies on cost-effectiveness and careful consideration of resource availability in the local context are required.

Towards efficient sharing of encrypted data in cloud-based mobile social network

Mobile social network is becoming more and more popular with respect to the development and popularity of mobile devices and interpersonal sociality. As the amount of social data increases in a great deal and cloud computing techniques become developed, the architecture of mobile social network is evolved into cloud-based that mobile clients send data to the cloud and make data accessible from clients. The data in the cloud should be stored in a secure fashion to protect user privacy and restrict data sharing defined by users. Ciphertext-policy attribute-based encryption (CP-ABE) is currently considered to be a promising security solution for cloud-based mobile social network to encrypt the sensitive data. However, its ciphertext size and decryption time grow linearly with the attribute numbers in the access structure. In order to reduce the computing overhead held by the mobile devices, in this paper we propose a new Outsourcing decryption and Match-then-decrypt CP-ABE algorithm (OM-CP-ABE) which firstly outsources the computation-intensive bilinear pairing operations to a proxy, and secondly performs the decryption test on the attributes set matching access policy in ciphertexts. The experimental performance assessments show the security strength and efficiency of the proposed solution in terms of computation, communication, and storage. Also, our construction is proven to be replayable choosen-ciphertext attacks (RCCA) secure based on the decisional bilinear Diffie-Hellman (DBDH) assumption in the standard model.

The SemSPM approach: fine integration of WS-SecurityPolicy semantics to enhance matching security policies in SOA

The lack of semantics in WS-SecurityPolicy standard hampers the effectiveness of matching security policies. To resolve this problem, we present a semantic approach for matching Web service security policies. The approach consists in the transformation of WS-SP into an OWL-DL ontology and the definition of a set of rules which automatically generate semantic relations that can exist between the provider and requestor security requirements. We show how these relations lead to more correct and refined matching of security policies. We also describe the implementation details of our approach and its validation through a real-world use case.