Dynamic Capacity Acquisition and Assignment under Uncertainty

Ahmed, Shabbir; Garcia, R.

doi:10.1023/b:anor.0000004773.66339.df

Cited by 51 publications

(28 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the second stage problem, the operational decisions will be made, which can adapt to the different realizations of the uncertainty. Works exploring this possibility include those by Ahmed and Garcia (2004) and Aghezzaf (2005).…”

Section: Multi-stage Stochastic Programming Modelsmentioning

confidence: 99%

Facility Location Under Uncertainty

Correia

Saldanha-da-Gama²

2015

Location Science

View full text Add to dashboard Cite

In this chapter, we cover some essential knowledge on facility location under uncertainty. We put a major emphasis on modeling aspects related with discrete facility location problems. Different modeling frameworks are discussed. In particular, we distinguish between robust optimization, stochastic programming and chance-constrained models. We also discuss relevant aspects such as solution techniques, multi-stage stochastic programming models, scenario generation, and extensions of basic problems.

show abstract

Section: Multi-stage Stochastic Programming Modelsmentioning

confidence: 99%

Facility Location Under Uncertainty

Correia

Saldanha-da-Gama²

2015

Location Science

View full text Add to dashboard Cite

show abstract

“…The objective function (1) (7) define the domain of the discrete and continuous variables in the model. Notice that the model with reduced number of scenario pairs (s,s') that are sufficient to represent the non-anticipativity constraints can be obtained from model (MD) after applying the three properties presented in the paper by Gupta and Grossmann (2011).…”

Section: Modelmentioning

confidence: 99%

A new decomposition algorithm for multistage stochastic programs with endogenous uncertainties

Gupta

Grossmann

2014

Computers & Chemical Engineering

View full text Add to dashboard Cite

In this paper, we present a new decomposition algorithm for solving large-scale multistage stochastic programs (MSSP) with endogenous uncertainties. Instead of dualizing all the initial non-anticipativity constraints (NACs) and removing all the conditional non-anticipativity constraints to decompose the problem into scenario subproblems, the basic idea relies on keeping a subset of NACs as explicit constraints in the scenario group subproblems while dualizing or relaxing the rest of the NACs. It is proved that the algorithm provides a dual bound that is at least as tight as the standard approach. Numerical results for process network examples and oilfield development planning problem are presented to illustrate that the proposed decomposition approach yields significant improvement in the dual bound at the root node and reduction in the total computational expense for closing the gap.

show abstract

“…Moreover, the study still focuses on solving a series of capacitated lot-sizing problems discretely, causing the computational complexity to increase exponentially with the number of planning periods and demands. Additionally, Ahmed and Garcia (2004) study a dynamic capacity-acquisition and assignment problem in a simplified operations setting to determine the resource capacity and allocation of the resources to tasks. The study actually proposes a capacity-expansion and planning approach without considering inventory carryover and the determination of production plans.…”

Section: Literature Reviewmentioning

confidence: 99%

Competition under capacitated dynamic lot-sizing with capacity acquisition

Meissner

2011

International Journal of Production Economics

View full text Add to dashboard Cite

Lot-sizing and capacity planning are important supply chain decisions, and competition and cooperation affect the performance of these decisions. In this paper, we look into the dynamic lot sizing and resource competition problem of an industry consisting of multiple firms. A capacity competition model combining the complexity of time-varying demand with cost functions and economies of scale arising from dynamic lot-sizing costs is developed. Each firm can replenish inventory at the beginning of each period in a finite planning horizon. Fixed as well as variable production costs incur for each production setup, along with inventory carrying costs. The individual production lots of each firm are limited by a constant capacity restriction, which is purchased up front for the planning horizon. The capacity can be purchased from a spot market, and the capacity acquisition cost fluctuates with the total capacity demand of all the competing firms. We solve the competition model and establish the existence of a capacity equilibrium over the firms and the associated optimal dynamic lot-sizing plan for each firm under mild conditions.

show abstract

Dynamic Capacity Acquisition and Assignment under Uncertainty

Cited by 51 publications

References 25 publications

Facility Location Under Uncertainty

Facility Location Under Uncertainty

A new decomposition algorithm for multistage stochastic programs with endogenous uncertainties

Competition under capacitated dynamic lot-sizing with capacity acquisition

Contact Info

Product

Resources

About