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Book
Advances and Novel Approaches in Discrete Optimization
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Year: 2020 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms.

Keywords

forgotten index --- balaban index --- reclassified the zagreb indices --- ABC4 index --- GA5 index --- HDN3(m) --- THDN3(m) --- RHDN3(m) --- degree of vertex --- extended adjacency index --- scheduling with rejection --- machine non-availability --- operator non-availability --- dynamic programming --- FPTAS --- Transportation --- batching scheduling --- total weighted completion time --- unary NP-hard --- approximation algorithm --- bi-criteria scheduling --- online algorithm --- makespan --- maximum machine cost --- competitive ratio --- network optimization --- dynamic flow --- evacuation planning --- contraflow configuration --- partial lane reversals, algorithms and complexity --- logistic supports --- scheduling algorithm --- release-time --- due-date --- divisible numbers --- lateness --- bin packing --- time complexity --- batch scheduling --- linear deterioration --- job families --- Max-cut problem --- combinatorial optimization --- deep learning --- pointer network --- supervised learning --- reinforcement learning --- capacitated lot sizing --- mixed integer formulation --- retail --- inventory --- shortages --- graph --- join product --- crossing number --- cyclic permutation --- arithmetic mean --- combinatorial generation --- method --- algorithm --- AND/OR tree --- Euler–Catalan’s triangle --- labeled Dyck path --- ranking algorithm --- unranking algorithm --- Harris hawks optimizer --- load frequency control --- sensitivity analysis --- smart grid --- particle swarm optimization --- genetic algorithm --- meta-heuristics --- packing --- irregular 3D objects --- quasi-phi-function s --- nonlinear optimization --- single-machine scheduling --- minimization of maximum penalty --- dual problem --- inverse problem --- branch and bound --- LNS --- numerical conversion --- RISC --- FPGA --- embedded systems --- scheduling --- job-shop --- makespan criterion --- uncertain processing times --- n/a --- Euler-Catalan's triangle


Book
Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems
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Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book.


Book
Graph-Theoretic Problems and Their New Applications
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ISBN: 3039287990 3039287982 Year: 2020 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Graph theory is an important area of applied mathematics with a broad spectrum of applications in many fields. This book results from aSpecialIssue in the journal Mathematics entitled “Graph-Theoretic Problems and Their New Applications”. It contains 20 articles covering a broad spectrum of graph-theoretic works that were selected from 151 submitted papers after a thorough refereeing process. Among others, it includes a deep survey on mixed graphs and their use for solutions ti scheduling problems. Other subjects include topological indices, domination numbers of graphs, domination games, contraction mappings, and neutrosophic graphs. Several applications of graph theory are discussed, e.g., the use of graph theory in the context of molecular processes.

Keywords

Zagreb indices --- n/a --- generating function --- mitotic cell cycle --- Mycielskian graph --- evolution theory --- grids --- “partitions” of wheel graph --- generalized hypertree --- connectivity --- single-valued neutrosophic graph --- degree of a vertex --- domination game --- interval-valued intuitionistic fuzzy graph --- directed cycle --- makespan criterion --- total-colored graph --- bipartite matching extendable graph --- stochastic convergence --- bipartite neutrosophic graph --- signless Laplacian --- complete neutrosophic graph --- k-trees --- enhanced hypercube --- b-metric space --- resistance distance --- Wiener index --- mixed graph --- line graph --- NP-hard --- generalized first Zagreb index --- inverse degree index --- sum lordeg index --- Edge Wiener --- chromatic polynomial --- degree of vertex --- complement neutrosophic graph --- graphic contraction mappings --- embedding --- Cartesian product --- k-rainbow domination number --- distance between two vertices --- evolution algebra --- k-rainbow dominating function --- PI index --- subtree --- component --- competition-independence game --- interval-valued fuzzy graph --- b-metric-like space --- induced matching extendable --- edge coloring --- degree of edge --- approximation methods --- chromatic index --- join of graphs --- genetic algorithm --- hypergraph --- edge congestion --- complement --- polynomials in graphs --- vertex coloring --- interval-valued neutrosophic graph --- spanning tree --- Kempe chain --- general contractive mappings --- DD index --- wireless multihop network and social network --- distance --- evolutionary approach --- complexity analysis --- neutrosophic graph --- Kempe-locking --- wheel graph --- Birkhoff diamond --- domination number --- k-extendable --- degree-Kirchhoff index --- adjacent matrix --- perfect matching --- spectral radius --- normalized Laplacian --- corona product --- road transport network --- extremal values --- bound --- chromatic number --- graph coloring --- combinatorial optimization --- reformulated Zagreb indices --- wirelength --- intuitionistic fuzzy graph --- unit-time scheduling --- fan graph --- "partitions" of wheel graph


Book
Evolutionary Computation 2020
Authors: ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.

Keywords

global optimization --- cuckoo search algorithm --- Q-learning --- mutation --- self-adaptive step size --- evolutionary computation --- playtesting --- game feature --- game simulation --- game trees --- playtesting metric --- validation --- Pareto optimality --- h-index --- ranking --- dominance --- Pareto-front --- multi-indicators --- multi-metric --- multi-resources --- citation --- universities ranking --- swarm intelligence --- simulated annealing --- krill herd --- particle swarm optimization --- quantum --- elephant herding optimization --- engineering optimization --- metaheuristic --- constrained optimization --- multi-objective optimization --- single objective optimization --- differential evolution --- success-history --- premature convergence --- turning-based mutation --- opposition-based learning --- ant colony optimization --- opposite path --- traveling salesman problems --- whale optimization algorithm --- WOA --- binary whale optimization algorithm --- bWOA-S --- bWOA-V --- feature selection --- classification --- dimensionality reduction --- menu planning problem --- evolutionary algorithm --- decomposition-based multi-objective optimisation --- memetic algorithm --- iterated local search --- diversity preservation --- single-objective optimization --- knapsack problem --- travelling salesman problem --- seed schedule --- many-objective optimization --- fuzzing --- bug detection --- path discovery --- evolutionary algorithms (EAs) --- coevolution --- dynamic learning --- performance indicators --- magnetotelluric --- one-dimensional inversions --- geoelectric model --- optimization problem --- multi-task optimization --- multi-task evolutionary computation --- knowledge transfer --- assortative mating --- unified search space --- quantum computing --- grey wolf optimizer --- 0-1 knapsack problem --- green shop scheduling --- fuzzy hybrid flow shop scheduling --- discrete artificial bee colony algorithm --- minimize makespan --- minimize total energy consumption

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