Development and Optimization of Mathematical Models for Operations Research
The development of mathematical models and their optimization are fundamental for the effective resolution of many problems in operational research. In recent years, increased insights into real-world problems have led to the development of new mathematical models and optimization algorithms, contri...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Rocha, Humberto |4 edt | |
700 | 1 | |a Rocha, Ana Maria |4 edt | |
700 | 1 | |a Rocha, Humberto |4 oth | |
700 | 1 | |a Rocha, Ana Maria |4 oth | |
245 | 1 | 0 | |a Development and Optimization of Mathematical Models for Operations Research |
260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2023 | ||
300 | |a 1 electronic resource (260 p.) | ||
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506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a The development of mathematical models and their optimization are fundamental for the effective resolution of many problems in operational research. In recent years, increased insights into real-world problems have led to the development of new mathematical models and optimization algorithms, contributing to the development of a research area with increasing practical relevance. This Special Issue is dedicated to works at the interface of mathematical modeling, optimization, and operations research, with a special focus on their real-world applications. The interest of the scientific community was significant, with submissions from authors from different countries from five continents, including Australia, China, Egypt, India, Israel, Portugal, Russia, Saudi Arabia, and the United States of America. Ten papers were accepted for publication after thorough peer review by dedicated reviewers with expertise in the relevant fields. We are confident that the papers selected for this Special Issue will attract a significant audience in the scientific community and will further stimulate research involving the development of mathematical models and their optimization. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Research & information: general |2 bicssc | |
650 | 7 | |a Mathematics & science |2 bicssc | |
653 | |a multi-time generalized Nash equilibrium problem | ||
653 | |a projected dynamical system | ||
653 | |a river basin pollution problem | ||
653 | |a traffic network equilibrium problem | ||
653 | |a variational inequality problem | ||
653 | |a freight transportation | ||
653 | |a heavy-haul railway | ||
653 | |a combination scheme | ||
653 | |a train timetable | ||
653 | |a genetic algorithm | ||
653 | |a trapezoidal type demand | ||
653 | |a interval-valued inventory costs | ||
653 | |a deterioration | ||
653 | |a preservation technology | ||
653 | |a QPSO algorithms | ||
653 | |a mixed integer nonlinear programming | ||
653 | |a piecewise linear approximation | ||
653 | |a branch and bound | ||
653 | |a pairwise comparison | ||
653 | |a matrix approximation | ||
653 | |a log-Chebyshev metric | ||
653 | |a tropical optimization | ||
653 | |a consumer preference | ||
653 | |a hotel selection | ||
653 | |a partial trade credit | ||
653 | |a cash discount | ||
653 | |a deteriorating items | ||
653 | |a EOQ | ||
653 | |a COVID-19 | ||
653 | |a metaheuristics | ||
653 | |a project selection | ||
653 | |a portfolio management | ||
653 | |a resource | ||
653 | |a R&D | ||
653 | |a roadmap | ||
653 | |a program management | ||
653 | |a scheduling | ||
653 | |a unrelated parallel machines | ||
653 | |a sequence-dependent tasks | ||
653 | |a makespan | ||
653 | |a statistical analysis | ||
653 | |a global optimization | ||
653 | |a unconstrained minimization | ||
653 | |a numerical approximations of gradients | ||
653 | |a meta-heuristics | ||
653 | |a stochastic parameters | ||
653 | |a conjugate gradient methods | ||
653 | |a efficient algorithm | ||
653 | |a performance profiles | ||
653 | |a comparisons | ||
653 | |a testing | ||
653 | |a discrete optimization | ||
653 | |a dragonfly algorithm | ||
653 | |a optimization | ||
653 | |a swarm intelligence algorithms | ||
653 | |a traveling salesman problem | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/6948 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/98895 |7 0 |z DOAB: description of the publication |