Authors:
Pablo Adasme
1
;
Rafael Andrade
2
;
Janny Leung
3
and
Abdel Lisser
4
Affiliations:
1
Universidad de Santiago de Chile, Chile
;
2
Universidade Federal do Ceará, Brazil
;
3
Chinese University of Hong Kong, Hong Kong
;
4
Université de Paris-Sud 11, France
Keyword(s):
Two-stage Stochastic Programming, Traveling Salesman Problem, Compact Formulations, Iterative Algorithmic Approach.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Linear Programming
;
Mathematical Modeling
;
Methodologies and Technologies
;
Operational Research
;
Optimization
;
Simulation
;
Stochastic Optimization
;
Symbolic Systems
Abstract:
In the context of combinatorial optimization, recently some efforts have been made by extending classical optimization problems under the two-stage stochastic programming framework. In this paper, we introduce the two-stage stochastic traveling salesman problem (STSP). Let G = (V,ED ∪ES) be a non directed complete graph with set of nodes V and set of weighted edges ED ∪ES where ED ∩ES = 0/. The edges in ED and ES have deterministic and uncertain weights, respectively. Let K = {1,2,··· ,|K|} be a given set of scenarios referred to the uncertain weights of the edges in ES. The STSP consists in determining Hamiltonian cycles of G, one for each scenario s ∈ K, sharing the same deterministic edges while minimizing the sum of the deterministic weights plus the expected weight over all scenarios associated with the uncertain edges. We propose two compact models and a formulation with an exponential number of constraints which are adapted from the classic TSP. One of the compact models allow
s to solve instances with up to 40 nodes and 5 scenarios to optimality. Finally, we propose an iterative procedure that allows to compute optimal solutions and tight lower bounds within very small CPU time.
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