Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Capacity allocation in service systems with preferred delivery times and multiple customer classes
Date
2023-01-01
Author
Boran, Melis
Çavdar, Bahar
Işık, Tuğçe
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
118
views
0
downloads
Cite This
Motivated by operational problems in click-and-collect systems, such as curbside pickup programs, we study a joint admission control and capacity allocation problem. We consider systems where customers have preferred service delivery times and can be of different priority classes. The service provider can reject customers upon arrival or serve jobs via overtime when service capacity is insufficient. The service provider’s goal is to find the minimum-cost admission and capacity allocation policy to dynamically decide when to serve and whom to serve. We model this problem as a Markov Decision Process and present structural results to partially characterize suboptimal solutions. We then develop a linear programming-based exact solution method using these results. We also present a problem-specific approximation method using a new state aggregation rule to address computational challenges faced due to large state and action spaces. Finally, we develop heuristic policies for large instances based on the behavior of optimal policies in small problems. We evaluate our methods through extensive computational experiments where we vary the service capacity, arrivals, associated service costs, customer segmentation, and order patterns. Our solution methods perform significantly better than several benchmarks in managing the tradeoff between the computation time and solution quality.
Subject Keywords
Capacity allocation
,
curbside pickup
,
customer preferences
,
Markov decision process
,
order fulfillment
,
state aggregation
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85166952042&origin=inward
https://hdl.handle.net/11511/105001
Journal
IISE Transactions
DOI
https://doi.org/10.1080/24725854.2023.2227666
Collections
Department of Industrial Engineering, Article
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. Boran, B. Çavdar, and T. Işık, “Capacity allocation in service systems with preferred delivery times and multiple customer classes,”
IISE Transactions
, pp. 0–0, 2023, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85166952042&origin=inward.