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Articles 1 - 30 of 259

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou Jun 2020

Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Information Systems

This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks ...


Flexibly Serving A Finite Number Of Heterogeneous Jobs In A Tandem System, Yun Fong Lim, Bingnan Lu, Rowan Wang, Wenjia Zhang Jun 2020

Flexibly Serving A Finite Number Of Heterogeneous Jobs In A Tandem System, Yun Fong Lim, Bingnan Lu, Rowan Wang, Wenjia Zhang

Research Collection Lee Kong Chian School Of Business

Many manufacturing and service systems require a finite number of heterogeneous jobs to be processed by two stations in tandem. Each station serves at most one job at a time and there is a finite buffer between the two stations. We consider two flexible servers that are cross-trained to work at both stations. The duration for a server to finish a job at a station is exponentially distributed with a rate that depends on the server, the station, and the job. Our goal is to identify an efficient policy to dynamically assign the servers to the stations such that the ...


Vehicle Routing Problem For Multi-Product Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Benjamin Gan, Vincent F. Yu, Panca Jodiawan Mar 2020

Vehicle Routing Problem For Multi-Product Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Benjamin Gan, Vincent F. Yu, Panca Jodiawan

Research Collection School Of Information Systems

Cross-docking is a logistic technique that can reduce costs occurred in a supply chain network while increasing the flow of goods, thus shortening the shipping cycle. Inside a cross-dock facility, the goods are directly transferred from incoming vehicles to outgoing vehicles without storing them in-between. Our research extends and combines this cross-docking technique with a well-known logistic problem, the vehicle routing problem (VRP), for delivering multiple products and addresses it as the VRP for multi-product cross-docking (VRP-MPCD). We developed a mixed integer programming model and generated two sets of VRP-MPCD instances, which are based on VRPCD instances. The instances are ...


Managing Electric Vehicle Charging: An Exponential Cone Programming Approach, Li Chen, Long He, Helen Yangfang Zhou Mar 2020

Managing Electric Vehicle Charging: An Exponential Cone Programming Approach, Li Chen, Long He, Helen Yangfang Zhou

Research Collection Lee Kong Chian School Of Business

A key to the mass adoption of electric vehicles (EVs) is ease of charging, in which public charging will play an increasingly important role. We study the EV charging management of a charging service provider, which faces uncertainty in customer arrivals (e.g., arrival/departure time and charging requirements) and a tariff structure including demand charges (costs related to the highest per-period charging quantity in a finite horizon). We formulate this problem to minimize the total expected costs as a two-stage stochastic program. A common approach to solve this program, sample average approximation, suers from its large- scale nature. Therefore ...


An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics, Chung-Kyun Han, Shih-Fen Cheng Jan 2020

An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics, Chung-Kyun Han, Shih-Fen Cheng

Research Collection School Of Information Systems

The trend of moving online in the retail industry has created great pressure for the logistics industry to catch up both in terms of volume and response time. On one hand, volume is fluctuating at greater magnitude, making peaks higher; on the other hand, customers are also expecting shorter response time. As a result, logistics service providers are pressured to expand and keep up with the demands. Expanding fleet capacity, however, is not sustainable as capacity built for the peak seasons would be mostly vacant during ordinary days. One promising solution is to engage crowdsourced workers, who are not employed ...


We Are On The Way: Analysis Of On-Demand Ride-Hailing Systems, Guiyun Feng, Guangwen Kong, Zizhuo Wang Jan 2020

We Are On The Way: Analysis Of On-Demand Ride-Hailing Systems, Guiyun Feng, Guangwen Kong, Zizhuo Wang

Research Collection Lee Kong Chian School Of Business

Recently, there has been a rapid rise of on-demand ride-hailing platforms, such as Uber and Didi, which allow passengers with smart phones to submit trip requests and match them to drivers based on their locations and drivers' availability. This increased demand has raised questions about how such a new matching mechanism will affect the efficiency of the transportation system, in particular, whether it will help reduce passengers' average waiting time compared to traditional street-hailing systems. In this talk, we shed light on this question by building a stylized model of a circular road and comparing the average waiting time of ...


Data-Driven Surgical Duration Prediction Model For Surgery Scheduling: A Case-Study For A Practice-Feasible Model In A Public Hospital, Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Song Kai Sean Lam Aug 2019

Data-Driven Surgical Duration Prediction Model For Surgery Scheduling: A Case-Study For A Practice-Feasible Model In A Public Hospital, Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Song Kai Sean Lam

Research Collection School Of Information Systems

Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and ...


Correlation-Sensitive Next-Basket Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang Aug 2019

Correlation-Sensitive Next-Basket Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang

Research Collection School Of Information Systems

Items adopted by a user over time are indicative ofthe underlying preferences. We are concerned withlearning such preferences from observed sequencesof adoptions for recommendation. As multipleitems are commonly adopted concurrently, e.g., abasket of grocery items or a sitting of media consumption, we deal with a sequence of baskets asinput, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of relateditems that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating informationon pairwise correlations among items would help toarrive at more coherent basket recommendations.Towards this objective, we ...


Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization Under Uncertainty, Jonathan David Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau Aug 2019

Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization Under Uncertainty, Jonathan David Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau

Research Collection School Of Information Systems

Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. To this end, we ...


Decision Making For Improving Maritime Traffic Safety Using Constraint Programming, Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau Aug 2019

Decision Making For Improving Maritime Traffic Safety Using Constraint Programming, Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Information Systems

Maritime navigational safety is of utmost importance to prevent vessel collisions in heavily trafficked ports, and avoid environmental costs. In case of a likely near miss among vessels, port traffic controllers provide assistance for safely navigating the waters, often at very short lead times. A better strategy is to avoid such situations from even happening. To achieve this, we a) formalize the decision model for traffic hotspot mitigation including realistic maritime navigational features and constraints through consultations with domain experts; and b) develop a constraint programming based scheduling approach to mitigate hotspots. We model the problem as a variant of ...


Model And Analysis Of Labor Supply For Ride-Sharing Platforms In The Presence Of Sample Self-Selection And Endogeneity, Hao Sun, Hai Wang, Zhixi Wan Jul 2019

Model And Analysis Of Labor Supply For Ride-Sharing Platforms In The Presence Of Sample Self-Selection And Endogeneity, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Information Systems

With the popularization of ride-sharing services, drivers working as freelancers on ride-sharing platforms can design their schedules flexibly. They make daily decisions regard- ing whether to participate in work, and if so, how many hours to work. Factors such as hourly income rate affect both the participation decision and working-hour decision, and evaluation of the impacts of hourly income rate on labor supply becomes important. In this paper, we propose an econometric framework with closed-form measures to estimate both the participation elasticity (i.e., extensive margin elasticity) and working-hour elasticity (i.e., intensive margin elasticity) of labor supply. We model ...


Entropy Based Independent Learning In Anonymous Multi-Agent Settings, Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau Jul 2019

Entropy Based Independent Learning In Anonymous Multi-Agent Settings, Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau

Research Collection School Of Information Systems

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the ”right” place at the ”right” time. We are interested in developing approaches that learn to guide individuals to be in the ”right” place at the ”right” time (to maximize revenue) in the presence of ...


Zac: A Zone Path Construction Approach For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet Jul 2019

Zac: A Zone Path Construction Approach For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in ...


Geometric Top-K Processing: Updates Since Mdm'16 [Advanced Seminar], Kyriakos Mouratidis Jun 2019

Geometric Top-K Processing: Updates Since Mdm'16 [Advanced Seminar], Kyriakos Mouratidis

Research Collection School Of Information Systems

The top-k query has been studied extensively, and is considered the norm for multi-criteria decision making in large databases. In recent years, research has considered several complementary operators to the traditional top-k query, drawing inspiration (both in terms of problem formulation and solution design) from the geometric nature of the top-k processing model. In this seminar, we will present advances in that stream of work, focusing on updates since the preliminary seminar on the same topic in MDM'16.


Re-Org: An Online Repositioning Guidance Agent, Muralidhar Konda, Pradeep Varakantham, Aayush Saxena, Meghna Lowalekar May 2019

Re-Org: An Online Repositioning Guidance Agent, Muralidhar Konda, Pradeep Varakantham, Aayush Saxena, Meghna Lowalekar

Research Collection School Of Information Systems

No abstract provided.


Towards Personalized Data-Driven Bundle Design With Qos Constraint, Mustafa Misir, Hoong Chuin Lau May 2019

Towards Personalized Data-Driven Bundle Design With Qos Constraint, Mustafa Misir, Hoong Chuin Lau

Research Collection School Of Information Systems

In this paper, we study the bundle design problem for offering personalized bundles of services using historical consumer redemption data. The problem studied here is for an operator managing multiple service providers, each responsible for an attraction, in a leisure park. Given the specific structure of interactions between service providers, consumers and the operator, a bundle of services is beneficial for the operator when the bundle is underutilized by service consumers. Such revenue structure is commonly seen in the cable television and leisure industries, creating strong incentives for the operator to design bundles containing lots of not-so-popular services. However, as ...


The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau Apr 2019

The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau

Research Collection School Of Information Systems

This paper focuses on a recent variant of the Orienteering Problem (OP), namely the Capacitated Team OP (CTOP) which arises in the logistics industry. In this problem, each node is associated with a demand that needs to be satisfied and a score that need to be collected. Given a set of homogeneous fleet of vehicles, the objective is to find a path for each vehicle in order to maximize the total collected score, without violating the capacity and time budget. We propose an Iterated Local Search (ILS) algorithm for solving the CTOP. Two strategies, either accepting a new solution as ...


Route Planning For A Fleet Of Electric Vehicles With Waiting Times At Charging Stations, Baoxiang Li, Shashi Shekhar Jha, Hoong Chuin Lau Apr 2019

Route Planning For A Fleet Of Electric Vehicles With Waiting Times At Charging Stations, Baoxiang Li, Shashi Shekhar Jha, Hoong Chuin Lau

Research Collection School Of Information Systems

Electric Vehicles (EVs) are the next wave of technology in the transportation industry. EVs are increasingly becoming common for personal transport and pushing the boundaries to become the mainstream mode of transportation. Use of such EVs in logistic fleets for delivering customer goods is not far from becoming reality. However, managing such fleet of EVs bring new challenges in terms of battery capacities and charging infrastructure for efficient route planning. Researchers have addressed such issues considering different aspects of the EVs such as linear battery charging/discharging rate, fixed travel times, etc. In this paper, we address the issue of ...


An Artificial Bee Colony-Based Hybrid Approach For Waste Collection Problem With Midway Disposal Pattern, Qu Wei, Zhaoxia Guo, Hoong Chuin Lau, Zhenggang He Mar 2019

An Artificial Bee Colony-Based Hybrid Approach For Waste Collection Problem With Midway Disposal Pattern, Qu Wei, Zhaoxia Guo, Hoong Chuin Lau, Zhenggang He

Research Collection School Of Information Systems

This paper investigates a waste collection problem with the consideration of midway disposal pattern. An artificial bee colony (ABC)-based hybrid approach is developed to handle this problem, in which the hybrid ABC algorithm is proposed to generate the better optimum-seeking performance while a heuristic procedure is proposed to select the disposal trip dynamically and calculate the carbon emissions in waste collection process. The effectiveness of the proposed approach is validated by numerical experiments. Experimental results show that the proposed hybrid approach can solve the investigated problem effectively. The proposed hybrid ABC algorithm exhibits a better optimum-seeking performance than four ...


Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Feb 2019

Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Information Systems

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale ...


Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Feb 2019

Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Information Systems

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale ...


Routing And Scheduling For A Last-Mile Transportation System, Hai Wang Jan 2019

Routing And Scheduling For A Last-Mile Transportation System, Hai Wang

Research Collection School Of Information Systems

The last-mile problem concerns the provision of travel services from the nearest public transportation node to a passenger’s home or other destination. We study the operation of an emerging last-mile transportation system (LMTS) with batch demands that result from the arrival of groups of passengers who desire last-mile service at urban metro stations or bus stops. Routes and schedules are determined for a multivehicle fleet of delivery vehicles, with the objective of minimizing passenger waiting time and riding time. An exact mixed-integer programming (MIP) model for LMTS operations is presented first, which is difficult to solve optimally within acceptable ...


A State Aggregation Approach For Stochastic Multiperiod Last-Mile Ride-Sharing Problems, Lucas Agussurja, Shih-Fen Cheng, Hoong Chuin Lau Jan 2019

A State Aggregation Approach For Stochastic Multiperiod Last-Mile Ride-Sharing Problems, Lucas Agussurja, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Information Systems

The arrangement of last-mile services is playing an increasingly important role in making public transport more accessible. We study the use of ridesharing in satisfying last-mile demands with the assumption that demands are uncertain and come in batches. The most important contribution of our paper is a two-level Markov decision process framework that is capable of generating a vehicle-dispatching policy for the aforementioned service. We introduce state summarization, representative states, and sample-based cost estimation as major approximation techniques in making our approach scalable. We show that our approach converges and solution quality improves as sample size increases. We also apply ...


Integrated Reward Scheme And Surge Pricing In A Ride-Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye Dec 2018

Integrated Reward Scheme And Surge Pricing In A Ride-Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye

Research Collection School Of Information Systems

Surge pricing is commonly used in on-demand ride-sourcing platforms (e.g., Uber, Lyft and Didi) to dynamically balance demand and supply. However, since the price for ride service cannot be unlimited, there is usually a reasonable or legitimate range of prices in practice. Such a constrained surge pricing strategy fails to balance demand and supply in certain cases, e.g., even adopting the maximum allowed price cannot reduce the demand to an affordable level during peak hours. In addition, the practice of surge pricing is controversial and has stimulated long debate regarding its pros and cons. To address the limitation ...


Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Dec 2018

Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Information Systems

Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment. We focus on a multiagent planning model subclass, relevant to urban settings, where agent interactions are dependent on their collective influence'' on each other, rather than their identities. Unlike previous work, we address a general setting where system reward is not decomposable among agents. We develop collective actor-critic RL approaches for this setting, and address the problem of multiagent credit assignment, and computing low variance policy gradient estimates that result in faster convergence to high quality solutions. We also develop difference ...


Design And Implementation Of Decision Support For Traffic Management At Multipurpose Port Gates, Ketki Kulkarni, Hoong Chuin Lau, Hai Wang, Sathyavarathan Sivabalasingam, Trong Khiem Tran Dec 2018

Design And Implementation Of Decision Support For Traffic Management At Multipurpose Port Gates, Ketki Kulkarni, Hoong Chuin Lau, Hai Wang, Sathyavarathan Sivabalasingam, Trong Khiem Tran

Research Collection School Of Information Systems

Effective traffic management can help port operators gain a competitive edge in service level and efficient use of limited resources. One critical aspect of traffic management is gate operations management, ensuring a good customer experience to logistic carriers and considering the impact of congestion in and around the port. In this paper, we describe the design and implementation of a decision support tool to help gate operators plan for future scenarios with fluctuating demand and limited resources. We propose a simulation optimization framework which incorporates theoretical results from queuing theory to approximate complex multi-lane multi-server systems. Our major contribution in ...


Iterated Local Search Algorithm For The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau Aug 2018

Iterated Local Search Algorithm For The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau

Research Collection School Of Information Systems

This paper focuses on a recent variant of the Orienteering Problem (OP), namely the Capacitated Team Orienteering Problem (CTOP). In this problem, each node is associated with a demand that needs to be satisfied and a score that need to be collected. Given a set of homogeneous fleet of vehicles, the main objective is to find a path for each available vehicle in order to maximize the total score, without violating the capacity and time budget of each vehicle. We propose an Iterated Local Search algorithm that has been applied in solving various variants of the OP. We propose two ...


Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu Jul 2018

Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Information Systems

Two fundamental challenges in local search based metaheuristics are how to determine parameter configurations and design the underlying Local Search (LS) procedure. In this paper, we propose a framework in order to handle both challenges, called ADaptive OPeraTor Ordering (ADOPT). In this paper, The ADOPT framework is applied to two metaheuristics, namely Iterated Local Search (ILS) and a hybridization of Simulated Annealing and ILS (SAILS) for solving two variants of the Orienteering Problem: the Team Dependent Orienteering Problem (TDOP) and the Team Orienteering Problem with Time Windows (TOPTW). This framework consists of two main processes. The Design of Experiment (DOE ...


Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan Jun 2018

Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan

Research Collection School Of Information Systems

Solving combinatorial optimization problems using a fixed set of operators has been known to produce poor quality solutions. Thus, adaptive operator selection (AOS) methods have been proposed. But, despite such effort, challenges such as the choice of suitable AOS method and configuring it correctly for given specific problem instances remain. To overcome these challenges, this work proposes a novel approach known as I-AOS-DOE to perform Instance-specific selection of AOS methods prior to evolutionary search. Furthermore, to configure the AOS methods for the respective problem instances, we apply a Design of Experiment (DOE) technique to determine promising regions of parameter values ...


Coordinating Supply And Demand On An On-Demand Service Platform With Impatient Customers, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang Jun 2018

Coordinating Supply And Demand On An On-Demand Service Platform With Impatient Customers, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang

Research Collection School Of Information Systems

We consider an on-demand service platform using earning sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price sensitive customers with heterogeneous valuation of the service. As such, both the supply and demand are "endogenously'' dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a ...