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A collaboration between Ford University Research Program and University of Minnesota to develop a broker that identifies real-time opportunities for commerce among mobile consumers and service providers, maximizing the supply-demand ratio.
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On-demand Service Propositions: A Supply-Demand Ratio Aware Broker for On-demand Services A collaboration between Ford University Research Program and University of Minnesota University PI: Shashi Shekhar Ford PI: Shounak Athavale
Outline • Motivation • Problem Definition • Toy Example • Challenges • Related Work • Proposed Approach • Contributions • Experimental Setup and Preliminary Results
Motivation • Increasing proliferation of mobile technologies (e.g., smart phones) led to emerging on-demand and sharing economy • resources to satisfy peak demand but are otherwise poorly utilized • Several success stories (e.g. Uber, Lyft, AirBnB) • Benefits of Sharing Economy/Collaborative Consumption: • increasing access while reducing investments in resources • e.g. reduction in road and parking infrastructure, freeing up land • improving consumer welfare (new on-demand services) while reducing societal costs (e.g. emissions, fuel consumption) • Need to investigate a broker that can identify real-time opportunities for commerce among (mobile) consumers and service providers • Satisfies consumers constraints, meets larger demand with a better supply management
Problem Definition: On-demand Service Propositions • Input: • A set of service providers. Each provider is defined using: • Location coordinates • Service rate over the day (e.g. 5/hr in rush hours, 10/hr in non-rush hours) • A set R of consumer requests arriving dynamically R = {ri= (time, current location li, direction of motion, max. acceptable travel distance, max. acceptable waiting time before service)} • k: number of required propositions • ttimeout : timeout interval length • Output: k service provider(s) propositions and estimated service/pick-up time(s) for each riϵ R • Objective: • Maximize number of matched requests • Constraints: • Service provider propositions matched to a consumer request should satisfy his max..time before service and the max. travel distance constraint along his direction of motion. • Propositions from each service provider should not exceed its supply rate. • Keeping the eco-system alive by engaging many service providers and balancing their matched requests. • Providing a real-time response to consumers
Toy Example Input: Output:
Challenges • Need to satisfy conflicting requirements: • Broker needs to maximize the number of matched requests while keeping the eco-system alive by engaging many service providers. • Conflicting requirements of consumers (minimizing travel distance and waiting times) and service providers (maximizing the number of consumers assign • Ratio of demand to supply exhibits spatio-temporal heterogeneity • Hence, a matching strategy that works well for a given time and/or location may not work as well for other times or locations with different supply-demand ratios. • Given a number of consumer requests and their candidate propositions at time t, finding the set of K-propositions that maximizes the matching size is an NP-Hard problem [GeoTruCrowd, 2013]
Limitations of Related Work Matching accounts for unbalanced supply and demand No Yes Least Travel Cost (spatial crowdsourcing, ridesharing) [11,10, 5,17,1,3, 6, 8,13] Least Location Entropy Priority (spatial crowdsourcing) [10] Proposed work
Modeling consumer and broker interactions: An Agent-based Simulation Framework Event types: Consumer arrival, Proposition acceptance Acceptance events time <= current clock Arrival events E with time <= current clock Output Simulation Statistics Find all candidate propositions satisfying consumers of events E Update availability of producers accordingly Match consumers with candidate propositions Spatio-temporal Specific Matching Policies Update availability of matched producers & generate acceptance events
Proposed Approach • We propose a greedy approximation algorithm with novel matching heuristics: • Service providers-favoring policy • Least_Accepted_First: Prioritize providers with least number of completed transactions so far • for balancing requests among providers • Least_Appearance_As_Candidate: Prioritize providers with least number of occurrences in all candidate lists • favors providers newly entering the system and providers in less populated regions. • Broker-favoring Heuristics: • Least_Service_Time_First: Prioritize providers with less service time (i.e. more available capacity) • to maximize matching size for future incoming requests • Consumer-favoring Heuristics: • Highest_Dominating _First : Prioritize near providers with smaller waiting times. • Propositions for each consumer are given scores equal to the number of (travel distance, waiting time) pairs they dominate
Execution Trace (1/4) Input: Output:
Execution Trace (2/4) Input: Output:
Execution Trace (3/4) Input: Output:
Execution Trace (4/4) Input: Output:
Experimental Setup (1/3) • Experimental Goals: • Self analysis:What is the effect of varying the different parameters on the performance of the proposed approach? • Comparative Analysis: How does the performance of the proposed heuristics compare to the Least Travel Cost and Least Location Entropy strategies? • Datasets: • Synthetic Data generation with real-data characteristics: • Generate supply rates, consumer distances & time constraints • Simulate a fixed demand-supply ratio r: For each hour, use real service provider locations (120 restaurants in Minneapolis) and generate a number of requests that satisfies the ratio. • For lunch hours: demand/requests generated from locations proportional to nodes day population. • For dinner hours: demand locations are generated in proportion to nodes night population.
Experimental Setup (2/3) • Candidate Algorithms: • Least Travel Cost • Least Location Entropy (Consumers, Providers) • Least Accepted First • Least Candidate First • Least Service Time First • Most Dominating First • Parameters (default value): • Demand/Supply ratio (1) • K: number of required propositions (3) • tTimeout : timeout interval (1 min) • Service rates [5 req/hr : 15 req/hr] • Travel distance constraint [4000m:12000]m • Maximal time before service constraint [5min:15min] • grid cell length (100m) • Metrics: (simulation statistics)
Experimental Setup: Simulation Statistics (3/3) • Broker centric: • % of completed transactions • % of matched requests • Throughput (i.e. handled requests per unit time) • Total execution time • Consumer centric: • Average query response time (until propositions are displayed) • Average waiting time per request • Average distance detour per request • Average time before service per request • Producer centric: • % of producers with completed transactions • % of matched producers • Avg. number of assigned/accepted consumers per provider • Standard deviation of the number of assigned/accepted consumers per provider
Preliminary Results (3/5): Average No. of matched Requests Per Provider
Preliminary Results (4/5): STDEV of matched requests per provider
Preliminary Results (5/5): Avg. travel distance/waiting time before service
Contributions • Formally define the problem of On-demand Service propositions. • Proposed novel heuristics for balancing the conflicting requirements of the broker, consumers and service providers using a greedy approach. • Employed an agent-based simulation framework for modeling the interactions between consumers and the broker. Using this framework, we simulated complete transactions involving the arrival of a consumer request, receiving service propositions and the acceptance of a specific proposition within a timeout interval. • Evaluated the proposed approach using dataset with real-world characteristics. Our results showed that. • Performance and dominance zones of the proposed matching heuristics vary with the variation of the supply-demand ratio • A supply-demand ratio-aware broker is needed to select the best matching policy