Testing facility location and dynamic capacity planning for pandemics with demand uncertainty

AbstractWe address a multi-period facility location problem with two customer segments having distinct service requirements. While customers in one segment receive preferred service, customers in the other segment accept delayed deliveries as long as lateness does not exceed a pre-specified threshold. The objective is to define a schedule for facility deployment and capacity scalability that satisfies all customer demands at minimum cost. Facilities can have their capacities adjusted over the planning horizon through incrementally increasing or reducing the number of modular units they hold. These two features, capacity expansion and capacity contraction, can help substantially improve the flexibility in responding to demand changes. Future customer demands are assumed to be unknown. We propose two different frameworks for planning capacity decisions and present a two-stage stochastic model for each one of them. While in the first model decisions related to capacity scalability are modeled as first-stage decisions, in the second model, capacity adjustments are deferred to the second stage. We develop the extensive forms of the associated stochastic programs for the case of demand uncertainty being captured by a finite set of scenarios. Additional inequalities are proposed to enhance the original formulations. An extensive computational study with randomly generated instances shows that the proposed enhancements are very useful. Specifically, 97.5% of the instances can be solved to optimality in much shorter computing times. Important insights are also provided into the impact of the two different frameworks for planning capacity adjustments on the facility network configuration and its total cost.

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A two-stage dynamic capacity planning approach for agricultural machinery maintenance service with demand uncertainty

Biosystems Engineering ◽

10.1016/j.biosystemseng.2019.12.005 ◽

2020 ◽

Vol 190 ◽

pp. 201-217 ◽

Cited By ~ 5

Author(s):

Yaoguang Hu ◽

Yu Liu ◽

Zhe Wang ◽

Jingqian Wen ◽

Jinliang Li ◽

...

Keyword(s):

Capacity Planning ◽

Demand Uncertainty ◽

Agricultural Machinery ◽

Two Stage ◽

Dynamic Capacity ◽

Planning Approach ◽

Maintenance Service

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An analytical approach to the facility location and capacity acquisition problem under demand uncertainty

Journal of the Operational Research Society ◽

10.1057/palgrave.jors.2601826 ◽

2005 ◽

Vol 56 (4) ◽

pp. 397-405 ◽

Cited By ~ 14

Author(s):

A Dasci ◽

G Laporte

Keyword(s):

Facility Location ◽

Analytical Approach ◽

Demand Uncertainty ◽

Capacity Acquisition

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Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran

Applied Mathematical Modelling ◽

10.1016/j.apm.2013.03.059 ◽

2013 ◽

Vol 37 (18-19) ◽

pp. 8467-8483 ◽

Cited By ~ 27

Author(s):

Javid Jouzdani ◽

Seyed Jafar Sadjadi ◽

Mohammad Fathian

Keyword(s):

Supply Chain ◽

Facility Location ◽

Traffic Congestion ◽

Demand Uncertainty ◽

Supply Chain Planning

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Two-Facility Location Problem with Infinite Retrial Queue

Management Theories and Strategic Practices for Decision Making ◽

10.4018/978-1-4666-2473-3.ch016 ◽

2012 ◽

pp. 294-310

Author(s):

Ebrahim Teimoury ◽

Mohammad Modarres Yazdi ◽

Iman Ghaleh Khondabi ◽

Mahdi Fathi

Keyword(s):

Steady State ◽

Facility Location ◽

Demand Uncertainty ◽

Location Problem ◽

Retrial Queue ◽

Facility Location Problem ◽

Fixed Cost ◽

Performance Indexes ◽

Matrix Geometric Method ◽

Primary Service

This paper analyzes a two-facility location problem under demand uncertainty. The maximum server for the ith facility is It is assumed that primary service demand arrivals for the ith facility follow a Poisson process. Each customer chooses one of the facilities with a probability which depends on his or her distance to each facility. The service times are assumed to be exponential and there is no vacation or failure in the system. Both facilities are assumed to be substitutable which means that if a facility has no free server, the other facility is used to fulfill the demand. When there is no idle server in both facilities, each arriving primary demand goes into an orbit of unlimited size. The orbiting demands retry to get service following an exponential distribution. In this paper, the authors give a stability condition of the demand satisfying process, and then obtain the steady-state distribution by applying matrix geometric method in order to calculation of some key performance indexes. By considering the fixed cost of opening a facility and the steady state service costs, the best locations for two facilities are derived. The result is illustrated by a numerical example.

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