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A recent business model, on-demand warehousing, enables warehouse owners with extra distribution capacity to rent it out for short periods, providing firms needing flexible network designs a new type of distribution capacity. In this paper, a heuristic is created to solve large scale instances of dynamic facility location models that optimize distribution networks over a multi-period planning horizon, simultaneously considering the selection of different warehouse types with varying capacity granularity, commitment granularity, access to scale, and cost structures. The heuristic iteratively solves selected single-period problems, creating a set of smaller subproblems that are then solved for multiple periods. Their decisions are combined to achieve feasible low-cost solutions, ensuring each customer’s demand point is covered for each period. A set of computational experiments recommends how heuristic settings should be set by industrial decision makers and illustrates the heuristic can generate high-quality solutions for large scale networks during long planning horizons and many decision periods. The heuristic can solve national-level instances with many customer demand points, candidate locations, different warehouse types and capacity levels and many periods.
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Multistage Stochastic Investment Planning with Multiscale Representation of Uncertainties and Decisions
We propose a multistage multiscale linear stochastic model to optimize electricity generation, storage, and transmission investments over a long planning horizon. The multiscale structure captures ‘large-scale’ uncertainties, such as investment and fuel-cost changes and long-run demand-growth rates, and ‘small-scale’ uncertainties, such as hour-to-hour demand and renewable-availability uncertainty. The model also includes a detailed treatment of operating periods so that the effect of dispatch decisions on long-term investments are captured. The proposed model can be large in size. The progressive hedging algorithm is applied to decompose the model by scenario, greatly reducing computation times. We also derive bounds on the optimal objective-function value, to assess solution quality. We use a case study based on the state of Texas to demonstrate the model and show the benefits of its detailed representation of the operating periods in making investment decisions.
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- Award ID(s):
- 1029337
- PAR ID:
- 10025244
- Date Published:
- Journal Name:
- IEEE Transactions on Power Systems
- ISSN:
- 0885-8950
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TPWRS.2017.2694612
