Fix and Relax Heuristic for a Stochastic Lot-Sizing Problem
Computational Optimization and Applications
On a stochastic sequencing and scheduling problem
Computers and Operations Research
Stochastic facility location with general long-run costs and convex short-run costs
Computers and Operations Research
A comparative study of decomposition algorithms for stochastic combinatorial optimization
Computational Optimization and Applications
A two-stage stochastic programming model for transportation network protection
Computers and Operations Research
On stochastic dynamic programming for solving large-scale planning problems under uncertainty
Computers and Operations Research
Cutting Planes for Multistage Stochastic Integer Programs
Operations Research
Models for robust tactical planning in multi-stage production systems with uncertain demands
Computers and Operations Research
A multiobjective metaheuristic for a mean-risk multistage capacity investment problem
Journal of Heuristics
A polynomial time algorithm for the stochastic uncapacitated lot-sizing problem with backlogging
IPCO'08 Proceedings of the 13th international conference on Integer programming and combinatorial optimization
Computers and Industrial Engineering
Stochastic lot-sizing problem with deterministic demands and Wagner-Whitin costs
Operations Research Letters
On stochastic lot-sizing problems with random lead times
Operations Research Letters
On formulations of the stochastic uncapacitated lot-sizing problem
Operations Research Letters
Computers and Operations Research
Fix-and-Relax-Coordination for a multi-period location-allocation problem under uncertainty
Computers and Operations Research
Hi-index | 0.00 |
This paper addresses a multi-period investment model for capacity expansion in an uncertain environment. Using a scenario tree approach to model the evolution of uncertain demand and cost parameters, and fixed-charge cost functions to model the economies of scale in expansion costs, we develop a multi-stage stochastic integer programming formulation for the problem. A reformulation of the problem is proposed using variable disaggregation to exploit the lot-sizing substructure of the problem. The reformulation significantly reduces the LP relaxation gap of this large scale integer program. A heuristic scheme is presented to perturb the LP relaxation solutions to produce good quality integer solutions. Finally, we outline a branch and bound algorithm that makes use of the reformulation strategy as a lower bounding scheme, and the heuristic as an upper bounding scheme, to solve the problem to global optimality. Our preliminary computational results indicate that the proposed strategy has significant advantages over straightforward use of commercial solvers.