We consider the problem of expanding arc capacities in a network subject to demand and travel time uncertainty. We propose a robust optimization approach to obtain capacity expansion solutions that are insensitive to this uncertainty. Our results show that, under reasonable assumptions for network flow applications, such robust solutions can be computed by solving tractable conic linear problem...

We consider the problem of determining the optimal investment level that a firm should maintain in the presence of random price and/or demand fluctuations. We model market uncertainty by means of a geometric Brownian motion, and we consider general running payoff functions. Our model allows for capacity expansion as well as for capacity reduction, with each of these actions being associated wit...

This paper examines a multi-period capacity expansion problem for rapid transit network design. The capacity expansion is realized through the location of train alignments and stations in an urban traffic context by selecting the time periods. The model maximizes the public transportation demand using a limited budget and designing lines for each period. The location problem incorporates the us...

For a service provider facing stochastic demand growth, expansion lead times and economies of scale complicate the expansion timing and sizing decisions. We formulate a model to minimize the infinite horizon expected discounted expansion cost under a service level constraint. The service level is defined as the proportion of demand over an expansion cycle that is satisfied by available capacity...

In critical energy infrastructure sectors (e.g. electric power generation, natural gas transportation, oil refining capacity) maintaining certain level of excess capacity is socially valuable (as it serves to protect against unexpected market conditions) but not necessarily compatible with the incentives for individual firms in the market. In this paper, we develop a dynamic oligopoly model wit...

We develop multi-dimensional balancing algorithms to compute provably near-optimal capacity-expansion policies. These are the first approximation algorithms for multi-machine, multi-product systems facing stochastic, non-stationary and correlated demands. Our approach is computationally efficient and guaranteed to produce a policy with total expected cost no more than twice that of an optimal p...

In this paper we present a methodology to decide capacity expansions for a transit network that finds a robust solution with respect to the uncertainty in demands and travel times. We show that solving for a robust solution is a computationally tractable problem under conditions that are reasonable for a transportation system. For example, the robust problem is tractable for a multicommodity fl...

This paper considers the general capacity expansion path problem (GCEP) for the telecommunication operators. We investigate the polynomial equivalence between the GCEP problem and the constrained shortest path problem (CSP) and present a pseudopolynomial algorithm for the GCEP problem, no matter the graph is acyclic or not. Furthermore, we investigate two special versions of the GCEP problem. F...

Distributed energy resources (DERs) can serve as non-wire alternatives to capacity expansion by managing peak load to avoid or defer traditional expansion projects. In this paper, we study a planning problem that co-optimizes DERs investment and operation (e.g., energy efficiency, energy storage, demand response, solar photovoltaic) and the timing of capacity expansion. We formulate the problem...

This paper presents a novel approach to compute optimal machine, shop oor and shell space expansion times under uncertain customer demand. It considers multiple machine types and allows for positive lead times for each type. Demand is assumed to be nondecreasing in a \weak" sense. A polynomial time algorithm (FIFEX) is developed and shown to be eeective and eecient with an example from the Semi...