A branch-and-price algorithm for the temporal bin packing problem

We study an extension of the classical Bin Packing Problem, where each item consumes the bin capacity during a given time window that depends on the item itself. The problem asks for finding the minimum number of bins to pack all the items while respecting the bin capacity at any time instant. A polynomial-size formulation, an exponential-size formulation, and a number of lower and upper bounds are studied. A branch-and-price algorithm for solving the exponential-size formulation is introduced. An overall algorithm combining the different methods is then proposed and tested through extensive computational experiments

Learning the Quality of Machine Permutations in Job Shop Scheduling

In recent years, the power demonstrated by Machine Learning (ML) has increasingly attracted the interest of the optimization community that is starting to leverage ML for enhancing and automating the design of algorithms. One combinatorial optimization problem recently tackled with ML is the Job Shop scheduling Problem (JSP). Most of the works on the JSP using ML focus on Deep Reinforcement Learning (DRL), and only a few of them leverage supervised learning techniques. The recurrent reasons for avoiding supervised learning seem to be the difficulty in casting the right learning task, i.e., what is meaningful to predict, and how to obtain labels. Therefore, we first propose a novel supervised learning task that aims at predicting the quality of machine permutations. Then, we design an original methodology to estimate this quality, and we use these estimations to create an accurate sequential deep learning model (binary accuracy above 95%). Finally, we empirically demonstrate the value of predicting the quality of machine permutations by enhancing the performance of a simple Tabu Search algorithm inspired by the works in the literature

Journey to the Center of the Cookie Ecosystem: Unraveling Actors' Roles and Relationships

Web pages have been steadily increasing in complexity over time, including code snippets from several distinct origins and organizations. While this may be a known phenomenon, its implications on the panorama of cookie tracking received little attention until now. Our study focuses on filling this gap, through the analysis of crawl results that are both large-scale and fine-grained, encompassing the whole set of events that lead to the creation and sharing of around 138 million cookies from crawling more than 6 million webpages. Our analysis lets us paint a highly detailed picture of the cookie ecosystem, discovering an intricate network of connections between players that reciprocally exchange information and include each other's content in web pages whose owners may not even be aware. We discover that, in most webpages, tracking cookies are set and shared by organizations at the end of complex chains that involve several middlemen. We also study the impact of cookie ghostwriting, i.e., a common practice where an entity creates cookies in the name of another party, or the webpage. We attribute and define a set of roles in the cookie ecosystem, related to cookie creation and sharing. We see that organizations can and do follow different patterns, including behaviors that previous studies could not uncover: for example, many cookie ghostwriters send cookies they create to themselves, which makes them able to perform cross-site tracking even for users that deleted third-party cookies in their browsers. While some organizations concentrate the flow of information on themselves, others behave as dispatchers, allowing other organizations to perform tracking on the pages that include their content

Tetra­kis(N,N-diethyl­carbamato)titanium(IV)

The mononuclear title compound, [Ti(C5H10NO2)4], is a rare example of an eight-coordinate TiIV compound in which all donor atoms are O atoms. The coordination geometry around TiIV is pseudo-dodeca­hedral and the O—C—O angles of the carbamate ligands are slightly compressed [range 115.3 (2)–116.7 (2)°], apparently on account of the high coordination number. One ethyl group is disordered over two positions; the site occupancy factors are 0.64 and 0.36

catena-Poly[zinc-tris­(μ-dimethyl­carbamato-κ2 O:O′)-zinc-μ-(2-phenyl­benzimidazolido-κ2 N:N′]

The crystal structure of the title compound, [Zn2(C13H9N2)(C3H6NO2)3]n, displays a long chiral chain. This is composed of zinc-dimer clusters capped by dimethyl­carbamate ligands, which lie on crystallographic twofold rotation axes and are polymerically linked in one dimension by 2-phenyl­benzimidadole (2–PBImi) organic ligands. The two Zn2+ ions defining the dimetal cluster are crystallographically independent, but display very similar coordination modes and tetra­hedral geometry. As such, each Zn2+ ion is coordinated on one side by the N-donor imidazole linker, while the other three available coordination sites are fully occupied by the O atoms from the capping dimethyl­carbamates. The chirality of the chain extends along the c axis, generating a rather long 52.470 (11) Å cell axis. Inter­estingly, the chiral material crystallizes from completely achiral precursors. A twofold axis and 31 screw axis serve to generate the long asymmetric unit