Matching Policy Research Articles

Multi-level analyses on the nearest-first matching policy of on-demand chauffeured ride-hailing service

On-demand ride-hailing services have received widespread interest from industry and academic. However, very few researches have empirically investigated the matching policy of ride-hailing platforms with real data. This study contributes in developing multi-level regression models to evaluate the nearest-first matching policy with real-world city-wide data collected from a ride-hailing platform UCAR in China. Specifically, after observing spatial-temporal patterns and the inherently hierarchical structure of the data, we propose multi-level logistics regression models for separately predicting the matching probabilities of passengers’ trip requests in the off-peak, eve-peak, and morning-peak hours, where trip requests are grouped by the origins and destinations with cross-classification. The intercepts and slopes in the models can vary with the groups. The multi-level models are verified to be more appropriate and accurate than the traditional logistics regression models. The multi-level models can identify heterogeneous effects of the predicting variables on the matching policy among the groups and allow the discovery of many new findings on matching policy. These findings provide valuable policy implications for ride-hailing platforms to design an improving matching policy.

Dynamic Pricing and Matching for Two-Sided Queues

Motivated by diverse applications in sharing economy and online marketplaces, we consider optimal pricing and matching control in a two-sided queueing system. We assume that heterogeneous customers and servers arrive to the system with price-dependent arrival rates. The compatibility between servers and customers is specified by a bipartite graph. Once a pair of customer and server are matched, they depart from the system instantaneously. The objective is to maximize the long-run average profits of the system while minimizing average waiting time. We first propose a static pricing and max-weight matching policy, which achieves O(√ n) optimality rate when all of the arrival rates are scaled by n. We further show that a dynamic pricing and modified max-weight matching policy achieves an improved O(n1/3) optimality rate. In addition, we propose a constraint generation algorithm that solves value function approximation of the MDP and demonstrate strong numerical performance of this algorithm.

Mapping the implementation of higher education funding reform in Kazakhstan: policy resources perspective

ABSTRACT Policy implementation research suggests that the likelihood of policy success, i.e. matching policy outcomes with expectations, can be best assessed by engaging the bottom-up perspectives of those who will implement the policy. This paper works from this premise and examines the views of university leaders in Kazakhstan on the potential impact of a per capita per credit higher education funding model proposed for implementation at their universities. The policy expectation is that such a higher education funding model will promote student choice, university quality and competitiveness. Applying resources for a policy implementation perspective, this article discusses concerns associated with the implementation of the proposed model. The findings suggest that the match between the policy expectations and the goal of strengthened student choice will be contingent upon the ability of universities to introduce organisational processes that enable such a choice. Advancing the quality and competitiveness of universities will be conditioned by university access to funding and the acceptance of personnel of both structural and cultural changes associated with the implementation of the new policy. This article contributes to policy implementation studies of higher education systems in transition.

Resource Allocation in One-dimensional Distributed Service Networks with Applications

We consider assignment policies that allocate resources to users, where both resources and users are located on a one-dimensional line [0,∞). First, we consider unidirectional assignment policies that allocate resources only to users located to their left. We propose the Move to Right (MTR) policy, which scans from left to right assigning nearest rightmost available resource to a user, and contrast it to the Unidirectional Gale–Shapley (UGS) matching policy. While both policies among all unidirectional policies, minimize the expected distance traveled by a request (request distance), MTR is fairer. Moreover, we show that when user and resource locations are modeled by statistical point processes, and resources are allowed to satisfy more than one user, the spatial system under unidirectional policies can be mapped into bulk service queueing systems, thus allowing the application of many queueing theory results that yield closed form expressions. As we consider a case where different resources can satisfy different numbers of users, we also generate new results for bulk service queues. We also consider bidirectional policies where there are no directional restrictions on resource allocation and develop an algorithm for computing the optimal assignment which is more efficient than known algorithms in the literature when there are more resources than users. Numerical evaluation of performance of unidirectional and bidirectional allocation schemes yields design guidelines beneficial for resource placement. Finally, we present a heuristic algorithm, which applies the optimal dynamic programming scheme for one-dimensional inputs to obtain approximate solutions to the optimal assignment problem for the two-dimensional scenario and empirically yields request distances within a constant factor of the optimal solution.

Data science in the design of public policies: dispelling the obscurity in matching policy demand and data offer

Data Science (DS) is expected to deliver value for public governance. In a number of studies, strong claims have been made about the potential of big data and data analytics and there are now several cases showing their application in areas such as service delivery and organizational administration. The role of DS in policy-making has, on the contrary, still been explored only marginally, but it is clear that there is the need for greater investigation because of its greater complexity and its distinctive inter-organizational boundaries. In this paper, we have investigated how DS can contribute to the policy definition process, endorsing a socio-technical perspective. This exploration has addressed the technical elements of DS - data and processes - as well as the social aspects surrounding the actors’ interaction within the definition process. Three action research cases are presented in the paper, lifting the veil of obscurity from how DS can support policy-making in practice. The findings highlight the importance of a new role, here defined as that of a translator, who can provide clarity and understanding of policy needs, assess whether data-driven results fit the legislative setting to be addressed, and become the junction point between data scientists and policy-makers. The three cases and their different achievements make it possible to draw attention to the enabling and inhibiting factors in the application of DS.

Joint pricing and matching in ride-sharing 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.

Faktor Kemenangan Partai Barisan Nasional Malaysia dalam Pemilu Selama 60 Tahun (1957-2018)

This research aims to analyze the National Front Party’s (Barisan Nasional) strategies in carrying out their function as a political party in Malaysia. The problem focused on this paper is the victory factors of the Malaysian National Front Party in the Election for 60 years. To approach this problem, the theoretical framework will be used in this paper from Prof. Miriam Budiardjo’s concept, that the concept of a political party. The concept of political party is a tools and instruments for the people to participate and contribute in managing a country. Data from this paper has collected through secondary data and literature review and analyzed qualitatively. This research concluded that there were several factors that led to the National Front always winning in the election for 60 years, they are: First, the polarization and fragmentation politics in Malaysia, it’s called by Bumiputera policy, income gap and the Chinese Tsunami. Second, the influence of the New Economic Policy (NEP) as an affirmative action effort and there is a mix and match policy. Third, Gerrymandering and delineation in the form of an electoral system that is designed, so that the National Front always wins. Fourth, the instability and weakness of the Pakatan Rakyat as coalition opposition party.

Dynamic Pricing and Matching for Two-Sided Queues

Motivated by diverse applications in sharing economy and online marketplaces, we consider optimal pricing and matching control in a two-sided queueing system. We assume that heterogeneous customers and servers arrive to the system with price-dependent arrival rates. The compatibility between servers and customers is specified by a bipartite graph. Once a pair of customer and server are matched, they depart from the system instantaneously. The objective is to maximize long-run average profits of the system while minimizing average waiting time. We first propose a static pricing and max-weight matching policy, which achieves $O(\sqrt{\eta})$ optimality rate when all of the arrival rates are scaled by $\eta$. We further show that a dynamic pricing and modified max-weight matching policy achieves an improved $O(\eta^{1/3})$ optimality rate. Under a broad class of pricing policies, we prove that any matching policy has an optimality rate that is lower bounded by $\Omega(\eta^{1/3})$. Thus, the dynamic pricing policy and modified max-weight matching policy achieves the optimal rate. In addition, we propose a constraint generation algorithm that solves an approximation of the MDP and demonstrate strong numerical performance of this algorithm.

Structural Estimation of Kidney Transplant Candidates' Quality of Life Scores: Improving National Kidney Allocation Policy Under Endogenous Patient Choice and Geographical Sharing

We develop a data-driven framework for assessing the impact of changes to the deceased-donor kidney allocation policy taking into account the transplant candidates' (endogenous) organ acceptance behavior and sharing among different locations under UNOS' geographically tiered allocation structure. To be specific, we develop a dynamic structural model of the transplant candidates' accept/reject decisions for organ offers. Our model of the national list (and its geographic structure) can be incorporated into the Kidney Pancreas Simulation Allocation Model (KPSAM) that is used to inform policy makers. Moreover, it allows important features of the transplant system such as the degree of tissue matching, changes in the health status of the transplant candidates as they wait on the list, organ quality, geographical sharing and cold-ischemia time of the organs as well as the heterogeneity in transplant candidates' quality of life scores. Using United Network of Organ Sharing (UNOS) data on transplant candidates, donors, organ offers, and follow up results on transplant outcomes, we estimate the transplant candidates' quality of life scores on dialysis versus post transplant. Our estimates yield similar results on average to what is typically assumed in the medical literature. However, they differ significantly when patient and donor characteristics are considered. We then consider policy proposals that have been considered by UNOS. This counterfactual analysis helps assess the (unintended) consequences of such policies. We find that although the current policy increases the total number of transplants by 2.63% and total life years by 4.45%, it decreases total quality adjusted life years by 1.68%. Moreover, it increases the disparity in probability of getting a transplant for patients of different health scores by 69.3% whereas it decreases disparity across patients with different sensitization (CPRA) scores. These happen due to the current prioritization of healthier patients for kidneys of better quality. Our results reveal that geographical redistricting of the transplant system, as done for the liver allocation system, does not change the system performance significantly. Hence, it should not be implemented. However, the brevity matching policy, currently being considered by UNOS, can further increase the total number of transplants by 1.50%, and is worth considering further.

A comparison of matching algorithms for kidney exchange programs addressing waiting time

Kidney exchange programs (KEP) allow an incompatible patient–donor pair, whose donor cannot provide a kidney to the respective patient, to have a transplant exchange with another in a similar situation if there is compatibility. Exchanges can be performed via cycles or chains initiated by non-directed donors (NDD), i.e., donors that do not have an associated patient. The objective for optimization in KEP is generally to maximize the number of possible transplants. Following the course of recent approaches that consider a dynamic matching (exchanges are decided every time a pair or a NDD joins the pool), in this paper we explore two matching policies to find feasible exchanges: periodic, where the algorithm runs within some period (e.g each 3 month); and greedy, in which a matching run is done as soon as the pool is updated with a new pair or NDD. For each policy, we propose a matching algorithm that addresses the waiting times of pairs in a pool. We conduct computational experiments with the proposed algorithms and compare the results with those obtained when periodic and greedy matching aim at maximizing the number of transplants.

Dynamic Matching for Real-Time Ride Sharing

In a ride-sharing system, arriving customers must be matched with available drivers. These decisions affect the overall number of customers matched, because they impact whether future available drivers will be close to the locations of arriving customers. A common policy used in practice is the closest driver policy, which offers an arriving customer the closest driver. This is an attractive policy because it is simple and easy to implement. However, we expect that parameter-based policies can achieve better performance. We propose matching policies based on a continuous linear program (CLP) that accounts for (i) the differing arrival rates of customers and drivers in different areas of the city, (ii) how long customers are willing to wait for driver pickup, (iii) how long drivers are willing to wait for a customer, and (iv) the time-varying nature of all the aforementioned parameters. We prove asymptotic optimality of a forward-looking CLP-based policy in a large market regime and of a myopic linear program–based matching policy when drivers are fully utilized. When pricing affects customer and driver arrival rates and parameters are time homogeneous, we show that asymptotically optimal joint pricing and matching decisions lead to fully utilized drivers under mild conditions.

Optimal Price Strategy under Price-Matching Policy

The paper explores the optimal price strategy under the price-matching policy. First, the paper formulates the demand function under the price match policy and then discovers the retailer’s best response facing the price-matching pressure. From the theoretical analysis, we discover how the number of retailers plays an important role during the competition. When only two retailers are involved, the final prices may not converge to a single value. However, when more retailers are involved, the final price will converge to a single value. We also use numerical studies to illuminate the change of the prices over the time period, the sensitivity of the final price to the increment/decrement of initial prices. Finally, we provide managerial suggestions to both producers and retailers.

Matching Supply and Demand with Mismatch-Sensitive Players

We study matching over time with short- and long-lived players who are very sensitive to mismatch. To characterize the mismatch, we model players' preferences as uniformly distributed on a circle, so the mismatch between two players is characterized by the one-dimensional circular angle between them. This framework allows us to capture matching processes in applications ranging from ride sharing to job hunting. Our analytical framework relies on threshold matching policies. If the matching process is controlled by a central planner (e.g. an online matching platform), the matching threshold reflects the trade-off between matching rate and matching quality. We further compare the centralized system with decentralized systems, where players choose their matching partners. We find that matching controlled by either side of the market may achieve optimal or near optimal social welfare, but have great impact on welfare allocation. In particular, letting long-lived players choose their matching partner leaves short-lived players with zero surplus. Moreover, we extend our model with player heterogeneity. Letting long-lived players choose their matching partner leads to better social welfare when the market of short-lived players is thick and the level of heterogeneity is significant. Otherwise, letting short-lived players choose matching partners is better.

A Rule-Based System for Explainable Donor-Patient Matching in Liver Transplantation

In this paper we present web-liver, a rule-based system for decision support in the medical domain, focusing on its application in a liver transplantation unit for implementing policies for donor-patient matching. The rule-based system is built on top of an interpreter for logic programs with partial functions, called lppf, that extends the paradigm of Answer Set Programming (ASP) adding two main features: (1) the inclusion of partial functions and (2) the computation of causal explanations for the obtained solutions. The final goal of web-liver is assisting the medical experts in the design of new donor-patient matching policies that take into account not only the patient severity but also the transplantation utility. As an example, we illustrate the tool behaviour with a set of rules that implement the utility index called SOFT.

Pricing and Matching with Forward-Looking Buyers and Sellers

Problem definition: We study a dynamic market over a finite horizon for a single product or service in which buyers with private valuations and sellers with private supply costs arrive following Poisson processes. A single market-making intermediary decides dynamically on the ask and bid prices that will be posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell. Buyers and sellers can wait strategically for better prices after they arrive. Academic/practical relevance: This problem is motivated by the emerging sharing economy and directly speaks to the core of operations management that is about matching supply with demand. Methodology: The dynamic, stochastic, and game-theoretic nature makes the problem intractable. We employ the mechanism-design methodology to establish a tractable upper bound on the optimal profit, which motivates a simple heuristic policy. Results: Our heuristic policy is: fixed ask and bid prices plus price adjustments as compensation for waiting costs, 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-compensated price processes are time-dependent and tend to have opposite trends at the beginning and end of the horizon. Under this heuristic policy, forward-looking buyers and sellers behave myopically. This policy is shown to be asymptotically optimal. Managerial implications: Our results suggest that the intermediary might not lose much optimality by maintaining stable prices unless the underlying market conditions have significantly changed, not to mention that frequent surge pricing may antagonize riders and induce riders and drivers to behave strategically in ways that are hard to account for with traditional pricing models.

Voluntary Adoption of an All In Match Policy in the Medical Specialties Matching Program: Advantages and Disadvantages.

Voluntary Adoption of an All In Match Policy in the Medical Specialties Matching Program: Advantages and Disadvantages.

Asymptotics of Replication and Matching in Large Caching Systems

We consider a generic model of distributed caching systems, where the cache servers are constrained by two main resources: memory size and bandwidth. Content distribution networks (CDNs) providing video contents and peer-to-peer video-on-demand services are a few examples of such systems. The throughput of these systems crucially depends on how these resources are managed, i.e., how contents are replicated across servers and how requests of specific contents are matched to servers storing the contents. In this paper, we formulate the problem of computing the replication policy and the matching policy, which jointly maximizes the throughput of the caching system. It is shown that computing the optimal replication policy for a given finite system is an NP-hard problem. A greedy replication scheme is then proposed and is shown to achieve a constant factor approximation guarantee when combined with the optimal matching policy. We note that the optimal matching policy has the problem of interruption in service of the ongoing requests due to re-assignment or repacking of the existing requests. To avoid this problem, we propose a simple randomized online matching scheme and analyze its performance in conjunction with the proposed replication scheme. We consider a limiting regime, where the number of servers is large and the arrival rates of the contents are scaled proportionally, and show that the proposed policies achieve asymptotic optimality. Extensive simulation results are presented to evaluate the performance of different policies and study the behavior of the caching system under different service time distributions of the requests.

Preference-Aware Task Assignment in On-Demand Taxi Dispatching: An Online Stable Matching Approach

A central issue in on-demand taxi dispatching platforms is task assignment, which designs matching policies among dynamically arrived drivers (workers) and passengers (tasks). Previous matching policies maximize the profit of the platform without considering the preferences of workers and tasks (e.g., workers may prefer high-rewarding tasks while tasks may prefer nearby workers). Such ignorance of preferences impairs user experience and will decrease the profit of the platform in the long run. To address this problem, we propose preference-aware task assignment using online stable matching. Specifically, we define a new model, Online Stable Matching under Known Identical Independent Distributions (OSM-KIID). It not only maximizes the expected total profits (OBJ-1), but also tries to satisfy the preferences among workers and tasks by minimizing the expected total number of blocking pairs (OBJ-2). The model also features a practical arrival assumption validated on real-world dataset. Furthermore, we present a linear program based online algorithm LP-ALG, which achieves an online ratio of at least 1−1/e on OBJ-1 and has at most 0.6·|E| blocking pairs expectedly, where |E| is the total number of edges in the compatible graph. We also show that a natural Greedy can have an arbitrarily bad performance on OBJ-1 while maintaining around 0.5·|E| blocking pairs. Evaluations on both synthetic and real datasets confirm our theoretical analysis and demonstrate that LP-ALG strictly dominates all the baselines on both objectives when tasks notably outnumber workers.

Preventing Infant Maltreatment with Predictive Analytics: Applying Ethical Principles to Evidence-Based Child Welfare Policy

Infant maltreatment is a devastating social and public health problem. Birth Match is an innovative policy solution to prevent infant maltreatment that leverages existing data systems to rapidly predict future risk through linkage of birth certificate and child welfare data then initiate a child protection response. Birth Match is one example of child welfare policy that capitalizes on recent advances in computing technology, predictive analytics, and algorithmic decision making. We apply frameworks from business and computer science as a case study in ethical decision-making in child welfare policy. Current Birth Match policy applications appear to lack key aspects of transparency and accountability identified in the frameworks. Although technology holds promise to help solve intractable social problems such as fatal infant maltreatment, the decision to deploy such policy innovations must consider ethical questions and tradeoffs. Technological advances hold great promise for prevention of fatal infant maltreatment, but numerous ethical considerations are lacking in current implementation and should be considered in future applications.

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.