Publication Type

Working Paper

Version

publishedVersion

Publication Date

4-2022

Abstract

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.

Keywords

Programming, Convex optimization, Robust Optimization, Resource allocation, Service Management, Platform Operations

Discipline

Operations and Supply Chain Management

Research Areas

Operations Management

First Page

1

Last Page

55

Identifier

10.2139/ssrn.4054332

Publisher

Singapore Management University Lee Kong Chian School of Business Research Paper Seriesess

City or Country

Singapore

External URL

https://doi.org/10.2139/ssrn.4054332

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