Problem complexity and narratives in Moscow's waste controversy

2021 ◽  
Author(s):  
Caroline Schlaufer ◽  
Tatiana Khaynatskaya ◽  
Marina Pilkina ◽  
Victoria Loseva ◽  
Sanjay Kumar Rajhans
Keyword(s):  
Problem Complexity

Exploiting Soft Constraints within Decomposition and Coordination Methods for Sub-hourly Unit Commitment

2020 ◽  
Author(s):  
Niranjan Raghunathan ◽  
Mikhail Bragin ◽  
Bing Yan ◽  
Peter Luh ◽  
Khosrow Moslehi ◽  
...  
Keyword(s):  
Unit Commitment ◽  
Daily Basis ◽  
Time Limit ◽  
Soft Constraints ◽  
Problem Complexity ◽  
Surrogate Optimization ◽  
Parallel Version ◽  
Improved Performance ◽  
Coordination Methods ◽  
Relaxed Problem

Unit commitment (UC) is an important problem solved on a daily basis within a strict time limit. While hourly UC problems are currently considered, they may not be flexible enough with the fast-changing demand and the increased penetration of intermittent renewables. Sub-hourly UC is therefore recommended. This, however, will significantly increase problem complexity even under the deterministic setting, and current methods may not be able to obtain good solutions within the time limit. In this paper, deterministic sub-hourly UC is considered, with the innovative exploitation of soft constraints – constraints that do not need to be strictly satisfied, but with predetermined penalty coefficients for their violations. The key idea is the “surrogate optimization” concept that ensures multiplier convergence within “surrogate” Lagrangian relaxation as long as the “surrogate optimality condition” is satisfied without the need to optimally solve the “relaxed problem.” Consequently, subproblems can still be formed and optimized when soft constraints are not relaxed, leading to a drastically reduced number of multipliers and improved performance. To further enhance the method, a parallel version is developed. Testing results on the Polish system demonstrate the effectiveness and robustness of both the sequential and parallel versions at finding high-quality solutions within the time limit.

The Minimum -Storage Problem: Complexity, Approximation, and Experimental Analysis

2016 ◽  
Vol 15 (7) ◽  
pp. 1797-1811 ◽  
Author(s):  
Gianlorenzo DAngelo ◽  
Daniele Diodati ◽  
Alfredo Navarra ◽  
Cristina M. Pinotti
Keyword(s):  
Experimental Analysis ◽  
Problem Complexity ◽  
Storage Problem

The Next Release Problem: Complexity, Exact Algorithms and Computations

2018 ◽  
pp. 26-38 ◽  
Author(s):  
José Carlos Almeida ◽  
Felipe de C. Pereira ◽  
Marina V. A. Reis ◽  
Breno Piva
Keyword(s):  
Exact Algorithms ◽  
Problem Complexity ◽  
Next Release Problem

The Guarding Problem – Complexity and Approximation

2009 ◽  
pp. 460-470 ◽  
Author(s):  
T. V. Thirumala Reddy ◽  
D. Sai Krishna ◽  
C. Pandu Rangan
Keyword(s):  
Problem Complexity

A Fuzzy Simulated Evolution Algorithm for Hard Problems

2014 ◽  
pp. 892-912
Author(s):  
Michael Mutingi
Keyword(s):  
Set Theory ◽  
Large Scale ◽  
Cell Formation ◽  
Computational Experiments ◽  
Benchmark Problems ◽  
Problem Complexity ◽  
Simulated Evolution ◽  
Solution Methods ◽  
Hard Problems ◽  
Evolution Algorithm

As problem complexity continues to increase in industry, developing efficient solution methods for solving hard problems, such as heterogeneous vehicle routing and integrated cell formation problems, is imperative. The focus of this chapter is to develop from the classical simulated evolution algorithm, a Fuzzy Simulated Evolution Algorithm (FSEA) that incorporates the concepts of fuzzy set theory, evolution, and constructive perturbation. The aim is to improve the search efficiency of the algorithm by enhancing the major phases of the algorithm through initialization, evaluation, selection, and reconstruction. Illustrative examples are provided to demonstrate the candidate application areas and to show the strength of the algorithm. Computational experiments are conducted based on benchmark problems in the literature. Results from the computational experiments demonstrate the strength of the algorithm. It is anticipated that the application of the FSEA metaheuristic can be extended to other hard large scale problems.

scholarly journals A web-based decision support tool for groundwater remediation technologies selection

2006 ◽  
Vol 8 (2) ◽  
pp. 91-100 ◽  
Author(s):  
Olfa Khelifi ◽  
Andrea Lodolo ◽  
Sanja Vranes ◽  
Gabriele Centi ◽  
Stanislav Miertus
Keyword(s):  
Decision Support ◽  
Groundwater Remediation ◽  
Local Community ◽  
Decision Support Tool ◽  
Decision Makers ◽  
Relative Importance ◽  
Problem Complexity ◽  
Web Based ◽  
Support Tool ◽  
Promethee Ii

Groundwater remediation operation involves several considerations in terms of environmental, technological and socio-economic aspects. A decision support tool (DST) becomes therefore necessary in order to manage problem complexity and to define effective groundwater remediation interventions. CCR (Credence Clearwater Revival), a decision support tool for groundwater remediation technologies assessment and selection, has been developed to help decision-makers (site owners, investors, local community representatives, environmentalists, regulators, etc.) to assess the available technologies and select the preferred remedial options. The analysis is based on technical, economical, environmental and social criteria. These criteria are ranked by all involved parties to determine their relative importance for a particular groundwater remediation project. The Multi-Criteria Decision Making (MCDM) is the core of the CCR using the PROMETHEE II algorithm.

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