A Lagrangian relaxation approach for stochastic network capacity expansion with budget constraints

In this paper, we consider capacity expansion for network models subject to uncertainty and budget constraints. We use a scenario tree approach to handle the uncertainty and construct a multi-stage stochastic mixed-integer programming model for the network capacity expansion problem. We assume that...

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Vydané v:Annals of operations research Ročník 284; číslo 2; s. 605 - 621
Hlavní autori: Taghavi, Majid, Huang, Kai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.01.2020
Springer Nature B.V
Predmet:
Budgeting
Budgets
Business and Management
Capacity
Combinatorics
Decision making
Expansion
Heuristic methods
Integer programming
Mathematical programming
Operations research
Operations Research/Decision Theory
Relaxation method (mathematics)
S.I. : Decomposition Methods for Hard Optimization Problems
Theory of Computation
Uncertainty
Upper bounds
Capacity expansion
Scenario tree
Network flow
Lagrangian relaxation
Spot market
Multi-stage stochastic integer programming
Permanent capacity
ISSN:0254-5330, 1572-9338
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Shrnutí:In this paper, we consider capacity expansion for network models subject to uncertainty and budget constraints. We use a scenario tree approach to handle the uncertainty and construct a multi-stage stochastic mixed-integer programming model for the network capacity expansion problem. We assume that permanent capacity and spot market capacity are available, which can be used permanently or temporarily by the decision maker respectively. By relaxing the budget constraints, we propose a heuristic Lagrangian relaxation method to solve the problem. Two algorithms are developed to find tight upper bounds for the Lagrangian relaxation procedure. The experimental results show superior performance of the proposed Lagrangian relaxation method compared with CPLEX.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-018-2862-7