Capacity expansion under stochastic demands
Operations Research - Supplement to Operations Research: stochastic processes
A scenario-based stochastic programming approach for technology and capacity planning
Computers and Operations Research
Capacity Optimization Planning System (Caps)
Interfaces
Coordinating Strategic Capacity Planning in the Semiconductor Industry
Operations Research
Capacity Expansion for Random Exponential Demand Growth with Lead Times
Management Science
Approximate Solutions of a Dynamic Forecast-Inventory Model
Manufacturing & Service Operations Management
Approximation Algorithms for Stochastic Inventory Control Models
Mathematics of Operations Research
A 2-Approximation Algorithm for Stochastic Inventory Control Models with Lost Sales
Mathematics of Operations Research
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We develop multidimensional balancing algorithms to compute provably near-optimal capacity-expansion policies. Our approach is computationally efficient and guaranteed to produce a policy with total expected cost of no more than twice that of an optimal policy. We overcome the curse of dimensionality by introducing novel cost-separation schemes to separate the lost-sales cost of the system into exact monotonic subparts. This is the first approximation technique for multimachine, multiproduct systems facing stochastic, nonstationary, and correlated demands. To show the generality of this separation technique, we apply it to the capacity-expansion problem under two different production planning models: monotone production and revenue-maximizing production. We make the assumptions of minimal inventory and lost sales.