Fix and Relax Heuristic for a Stochastic Lot-Sizing Problem
Computational Optimization and Applications
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
Retail expansion decision based on improved marginal analysis
Expert Systems with Applications: An International Journal
Stochastic lot-sizing with backlogging: computational complexity analysis
Journal of Global Optimization
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Planning for capacity expansion forms a crucial part of the strategic-level decision making in many applications. Consequently, quantitative models for economic capacity expansion planning have been the subject of intense research. However, much of the work in this area has been restricted to linear cost models and/or limited degree of uncertainty to make the problems analytically tractable. This paper addresses a stochastic capacity expansion problem where the economies-of-scale in expansion costs are handled via fixed-charge cost functions, and forecast uncertainties in the problem parameters are explicitly considered by specifying a set of scenarios. The resulting formulation is a multistage stochastic integer program. We develop a fast, linear-programming-based, approximation scheme that exploits the decomposable structure and is guaranteed to produce feasible solutions for this problem. Through a probabilistic analysis, we prove that the optimality gap of the heuristic solution almost surely vanishes asymptotically as the problem size increases.