Joint capacity allocation and job assignment under uncertainty

In this paper, we consider the multi-period joint capacity allocation and job assignment problem. The goal of the planner is to simultaneously decide on allocating resources across the J different supply nodes, and assigning of jobs of I different demand origins to these J nodes, so as to maximize t...

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Main Authors: WANG, Peng, LIM, Yun Fong, LOKE, Gar Goei
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/7022
https://ink.library.smu.edu.sg/context/lkcsb_research/article/8021/viewcontent/SSRN_id4054332.pdf
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spelling sg-smu-ink.lkcsb_research-80212022-06-15T06:30:50Z Joint capacity allocation and job assignment under uncertainty WANG, Peng LIM, Yun Fong LOKE, Gar Goei In this paper, we consider the multi-period joint capacity allocation and job assignment problem. The goal of the planner is to simultaneously decide on allocating resources across the J different supply nodes, and assigning of jobs of I different demand origins to these J nodes, so as to maximize the reward for matching or minimize the cost of failure to match. We furthermore consider three features: (i) supply is replenishable after random time, (ii) demand is random; and (iii) demand can wait and need not be fully fulfilled immediately. Such problems emerge in many service management settings such as ride-sharing fleet re-positioning, and patient management in healthcare. We introduce a distributive decision rule, which decides on the proportion of jobs to be served by each of the supply nodes. We borrow ideas from the pipeline queues framework Bandi and Loke (2018), which cannot be directly applied to our setting, and hence requires the development of new reformulation techniques. Our model has a convex reformulation and can be solved by a sequence of linear programs, in practice. We test our model against state-of-the-art models that focus solely on the capacity allocation or job assignment decisions, in the setting of nurse scheduling and patient overflow respectively. Our model performs strongly against the benchmarks, recording 1-15% reductions in costs, and shorter computation times. Our model opens the door to consider new problems in platform operations and online services where the planner is able to influence the supply of services or resources partially. 2022-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/7022 info:doi/10.2139/ssrn.4054332 https://ink.library.smu.edu.sg/context/lkcsb_research/article/8021/viewcontent/SSRN_id4054332.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Programming Convex optimization Robust Optimization Resource allocation Service Management Platform Operations Operations and Supply Chain Management
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Programming
Convex optimization
Robust Optimization
Resource allocation
Service Management
Platform Operations
Operations and Supply Chain Management
spellingShingle Programming
Convex optimization
Robust Optimization
Resource allocation
Service Management
Platform Operations
Operations and Supply Chain Management
WANG, Peng
LIM, Yun Fong
LOKE, Gar Goei
Joint capacity allocation and job assignment under uncertainty
description In this paper, we consider the multi-period joint capacity allocation and job assignment problem. The goal of the planner is to simultaneously decide on allocating resources across the J different supply nodes, and assigning of jobs of I different demand origins to these J nodes, so as to maximize the reward for matching or minimize the cost of failure to match. We furthermore consider three features: (i) supply is replenishable after random time, (ii) demand is random; and (iii) demand can wait and need not be fully fulfilled immediately. Such problems emerge in many service management settings such as ride-sharing fleet re-positioning, and patient management in healthcare. We introduce a distributive decision rule, which decides on the proportion of jobs to be served by each of the supply nodes. We borrow ideas from the pipeline queues framework Bandi and Loke (2018), which cannot be directly applied to our setting, and hence requires the development of new reformulation techniques. Our model has a convex reformulation and can be solved by a sequence of linear programs, in practice. We test our model against state-of-the-art models that focus solely on the capacity allocation or job assignment decisions, in the setting of nurse scheduling and patient overflow respectively. Our model performs strongly against the benchmarks, recording 1-15% reductions in costs, and shorter computation times. Our model opens the door to consider new problems in platform operations and online services where the planner is able to influence the supply of services or resources partially.
format text
author WANG, Peng
LIM, Yun Fong
LOKE, Gar Goei
author_facet WANG, Peng
LIM, Yun Fong
LOKE, Gar Goei
author_sort WANG, Peng
title Joint capacity allocation and job assignment under uncertainty
title_short Joint capacity allocation and job assignment under uncertainty
title_full Joint capacity allocation and job assignment under uncertainty
title_fullStr Joint capacity allocation and job assignment under uncertainty
title_full_unstemmed Joint capacity allocation and job assignment under uncertainty
title_sort joint capacity allocation and job assignment under uncertainty
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/lkcsb_research/7022
https://ink.library.smu.edu.sg/context/lkcsb_research/article/8021/viewcontent/SSRN_id4054332.pdf
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