A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice

Bakker, Hannah; Dunke, Fabian; Nickel, Stefan

doi:10.1016/j.omega.2019.06.006

Cited by 74 publications

(36 citation statements)

References 224 publications

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“…Finally, another line of research could be directed toward the development of a multi-stage stochastic framework to enable the revision of the planning decisions as more information regarding the uncertain data is revealed. Even though a multi-stage model yields a better characterization of the dynamic planning process, solution techniques remain computationally challenging (Bakker et al 2020). However, recent developments in stochastic nested decomposition techniques seem to be a promising venue for future research (Zou et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Integrated facility location and capacity planning under uncertainty

Correia

Melo

2021

Comp. Appl. Math.

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We address a multi-period facility location problem with two customer segments having distinct service requirements. While customers in one segment receive preferred service, customers in the other segment accept delayed deliveries as long as lateness does not exceed a pre-specified threshold. The objective is to define a schedule for facility deployment and capacity scalability that satisfies all customer demands at minimum cost. Facilities can have their capacities adjusted over the planning horizon through incrementally increasing or reducing the number of modular units they hold. These two features, capacity expansion and capacity contraction, can help substantially improve the flexibility in responding to demand changes. Future customer demands are assumed to be unknown. We propose two different frameworks for planning capacity decisions and present a two-stage stochastic model for each one of them. While in the first model decisions related to capacity scalability are modeled as first-stage decisions, in the second model, capacity adjustments are deferred to the second stage. We develop the extensive forms of the associated stochastic programs for the case of demand uncertainty being captured by a finite set of scenarios. Additional inequalities are proposed to enhance the original formulations. An extensive computational study with randomly generated instances shows that the proposed enhancements are very useful. Specifically, 97.5% of the instances can be solved to optimality in much shorter computing times. Important insights are also provided into the impact of the two different frameworks for planning capacity adjustments on the facility network configuration and its total cost.

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Section: Discussionmentioning

confidence: 99%

Integrated facility location and capacity planning under uncertainty

Correia

Melo

2021

Comp. Appl. Math.

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“…Possible ways of addressing the dimensionality problem were proposed in the literature, such as approximate the future cost function by an analytical function rather than a set of discrete values 176 . More detailed explanations of the corresponded methodologies can be found in the recent review 177 …”

Section: Perspectives and Other Challenges In Formulation Designmentioning

confidence: 99%

Automated robotic platforms in design and development of formulations

2021

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Product design for formulations is an active and challenging area of research. The new challenges of a fast-paced market, products of increasing complexity, and practical translation of sustainability paradigms require to reexamine the existing theoretical frameworks to include the advantages deriving from the new reality of digitalization of business and research. In this work, we review the existing approaches, clearly stating the role of automation and machine-learning-guided optimization in the broader framework. Moving from this, we review the state of the art of automated hardware and software for formulated product design, and identify the open challenges for future research. Perspectives are given on the emerging fields of automated discovery, scale-up, and multistage optimization, and a unitary picture of the existing connections is provided, in the general context of a completely digital R&D workflow.

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“…Randomly as: Method 1: CC, VUF, VD with P-V2G (both system and customer objectives) [29,30] Method 2: CC, BC (only customer objective) [26][27][28] Method 3: VUF, VD with Q-V2G (only system objective) [31][32][33][34] All the results are only shown for bus 846 and in the 5 p.m.-10 a.m. time period, which represents the worst-case scenarios for this study. Tables 3 and 4 show the simulation parameters for the constraints and PSO used in this study, respectively.…”

Section: Case Study Under Different Ev Penetration Levelsmentioning

confidence: 99%

A New Two-Stage Approach to Coordinate Electrical Vehicles for Satisfaction of Grid and Customer Requirements

et al. 2021

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Recently due to air pollution concerns, a large number of electric vehicles have been integrated into the electric distribution system. However, the uncoordinated charging of this technology can cause different voltage issues. This paper proposes a two-stage optimization approach with active and reactive power control to coordinate electric vehicles with both grid-to-vehicle and vehicle-to-grid capabilities to satisfy both grid requirements and electric vehicle prosumer requirements. The system requirements considered are voltage deviation and unbalance and the electric vehicle prosumer requirements considered are minimization of charging and battery degradation costs. The coordination problem is formulated as an optimization problem, where the first stage objectives are: minimization of voltage unbalance, customer charging and battery degradation costs. The first stage optimization problem is solved using the meta-heuristic optimization algorithm known as particle swarm optimization to obtain an optimized real power schedule for the electric vehicles. The second stage is then solved of which the objective is to minimize the bus voltage deviation and provides the reactive power schedule for electric vehicles. All the analyses were carried out on the IEEE 34 bus distribution system and the study results show that the proposed method allows prosumers to charge at a minimum cost without any grid voltage unbalance factors and under/over voltage problems under different scenarios. Thus, this work can be beneficial for system operators or electric vehicle aggregators to create a day-ahead schedule.

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A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice

Cited by 74 publications

References 224 publications

Integrated facility location and capacity planning under uncertainty

Integrated facility location and capacity planning under uncertainty

Automated robotic platforms in design and development of formulations

A New Two-Stage Approach to Coordinate Electrical Vehicles for Satisfaction of Grid and Customer Requirements

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