This book highlights new and original contributions on Graph Theory and Combinatorial Optimization both from the theoretical point of view and from applications in all fields. The book chapters describe models and methods based on graphs, structural properties, discrete optimization, network optimization, mixed-integer programming, heuristics, meta-heuristics, math-heuristics, and exact
methods as well as applications.

The book collects selected contributions from the CTW2020 international conference (18th Cologne-Twente Workshop on Graphs and Combinatorial Optimization), held online on September 14-16, 2020. The conference was organized by IASI-CNR with the contribution of University of Roma Tre, University Roma Tor Vergata, and CNRS-LIX and with the support of AIRO. It is addressed to researchers, PhD students, and practitioners in the fields of Graph Theory, Discrete Mathematics, Combinatorial Optimization, and Operations Research.

Presents the latest results in Combinatorial Optimization and in Graph Theory Emphasizes the contribution of young researchers in these fields Provides new insights on the connection of theory and applications of Operations Research

Autorentext
Claudio Gentile is a Research Director at the Institute of Systems Analysis and Computer Science "Antonio Ruberti" of the Italian National Research Council (CNR-IASI). From 2006 to 2015 he directed the CNRIASI research unit "Control and Optimization of Complex Systems" and since 2016 he directs the CNR-IASI research unit "OPTIMA: Optimization and Discrete Mathematics". His main research interests are in Combinatorial Optimization, Polyhedral Theory for Linear and Nonlinear Mixed-Integer Programming problems, Interior Point Methods with applications in Power Energy Production and Distribution, Logistics, Network Design, Stä Management, and Ship Scheduling. He is author of many scienti c publications among journal papers, book chapters, and articles in conference proceedings.

Giuseppe Stecca is a Research Scientist at the Institute of Systems Analysis and Computer Science "Antonio Ruberti" of the Italian National Research Council (CNR-IASI). He holds the Chair of Supply Chain Management at the University of Rome "Tor Vergata", where he also teaches Operations Research. He is a member of the board of the Italian Association for Operations Research (AIRO). His main research interests are related to the optimization of sustainable production and logistic systems. He works actively in research projects and also as an evaluator for the Italian Ministry of Economic Development in the area of logistics and industry 4.0.

Paolo Ventura is a Research Scientist at the Institute of Systems Analysis and Computer Science "Antonio Ruberti" of the Italian National Research Council (CNR-IASI). His main research interests are Integer Programming and Combinatorial Optimization with applications in logistics and transportation. He is author of many articles in the most relevant international journals of the area. Since 2004, he teaches Operations Research at the University of Rome "Tor Vergata". He is member of the organizing committee of the yearly "Cargese Workshop of Combinatorial Optimization" and, in the odd years, of the "Aussois Combinatorial Optimization Workshop".

Inhalt
W. Hochstattler and J. Wiehe, The Chromatic Polynomial of a Digraph.- J. Dìaz et al., On List k-Coloring Convex Bipartite Graphs.- E. Kubicka et al., Total chromatic sum for trees.- S. Ghosal and S. C. Ghosh, An incremental search heuristic for coloring vertices of a graph.- S. Bandopadhyay et al., Improved Bounds on the Span of L (1,2)-edge Labeling of Some Innite Regular Grids.- E. Althaus and S. Ziegler, Optimal Tree Decompositions Revisited: A Simpler Linear-Time FPT Algorithm.- H. Kerivin and A. Wagler, On superperfection of edge intersection graphs of paths.- L. Liberti et al., A cycle-based formulation for the Distance Geometry Problem.- P. Samer and D. Haugland, The unsuitable neighbourhood inequalities for the xed cardinality stable set polytope.- Lucas L. S. Portugal et al., Relating hypergraph parameters of generalized power graphs.- A. Nixon, Assur decompositions of direction-length frameworks.- M. Hiller et al., On the Burning Number of p-Caterpillars.- J. Boeckmann and C. Thielen, An Approximation Algorithm for Network Flow Interdiction with Unit Costs and Two Capacities.- T. Bacci and S. Nicoloso, On the benchmark instances for the Bin Packing Problem with Conicts.- Barbara M. Anthony and Alison M. Marr, Directed Zagreb Indices.- F. Couto et al., Edge Tree Spanners.- S. Khalife, Sequence graphs: characterization and counting of admissible elements.- L. Burahem Martins et al., On solving the time window assignment vehicle routing problem via iterated local search.- M. Barbato et al., Synchronized Pickup and Delivery Problems with Connecting FIFO Stack.- A. Teymourifar et al., A Comparison Between Simultaneous and Hierarchical Approaches to Solve a Multi-Objective Location-Routing Problem.- M. Bodirsky et al., Piecewise Linear Valued Constraint Satisfaction Problems with Fixed Number of Variables.- M. Cacciola et al., A Lagrangian approach to Chance Constrained Routing with Local Broadcast.- P. Detti et al., A metaheuristic approach for biological sample transportation in healthcare.- Diego M. Pinto and G. Stecca, Optimal Planning of Waste Sorting Operations through Mixed Integer Linear Programming.- G. Micheli et al., Selecting and Initializing Representative Days for Generation and Transmission Expansion Planning with High Shares of Renewables.- T. Bacci et al., Start-up/Shut-down MINLP formulations for the Unit Commitment with Ramp Constraints.- J. Lee et al., Gaining or Losing Perspective for Piecewise-Linear Under-Estimators of Convex Univariate Functions.- M. Aprile et al., Recognizing Cartesian products of matrices and polytopes.- A. Frank, Special subclass of Generalized Semi-Markov Decision Processes with discrete time.- R. Seccia et al., Coupling Machine Learning and Integer Programming for Optimal TV Promo Scheduling.- F. Mendoza-Granada and M. Villagra, A Distributed Algorithm for Spectral Sparsication of Graphs with Applications to Data Clustering.