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Convexification of Queueing Formulas by Mixed-Integer Second-Order Cone Programming: An Application to a Discrete Location Problem with Congestion
Mixed-Integer Second-Order Cone Programs (MISOCPs) form a nice class of ...
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An Integer Programming Model for the Dynamic Location and Relocation of Emergency Vehicles: A Case Study
In this paper, we address the dynamic Emergency Medical Service (EMS) sy...
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Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization
The limousine service in luxury hotels is an integral component of the w...
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Estimating customer impatience in a service system with balking
This paper studies a service system in which arriving customers are prov...
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An online learning approach to dynamic pricing and capacity sizing in service systems
We study a dynamic pricing and capacity sizing problem in a GI/GI/1 queu...
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Service Network Design Problem with Capacity-Demand Balancing
This paper addresses developing cost-effective strategies to respond to ...
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Minimal-Variance Distributed Deadline Scheduling
Many modern schedulers can dynamically adjust their service capacity to ...
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Location and Capacity Planning of Facilities with General Service-Time Distributions Using Conic Optimization
This paper studies a stochastic congested location problem in the network of a service system that consists of facilities to be established in a finite number of candidate locations. Population zones allocated to each open service facility together creates a stream of demand that follows a Poisson process and may cause congestion at the facility. The service time at each facility is stochastic and depends on the service capacity and follows a general distribution that can differ for each facility. The service capacity is selected from a given (bounded or unbounded) interval. The objective of our problem is to optimize a balanced performance measure that compromises between facility-induced and customer-related costs. Service times are represented by a flexible location-scale stochastic model. The problem is formulated using quadratic conic optimization. Valid inequalities and a cut-generation procedure are developed to increase computational efficiency. A comprehensive numerical study is carried out to show the efficiency and effectiveness of the solution procedure. Moreover, our numerical experiments using real data of a preventive healthcare system in Toronto show that the optimal service network configuration is highly sensitive to the service-time distribution. Our method for convexifying the waiting-time formulas of M/G/1 queues is general and extends the existing convexity results in queueing theory such that they can be used in optimization problems where the service rates are continuous.
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