No abstract

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. To demonstrate the breadth and power of our platform, we describe a study that builds and evaluates algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.• A human-readable README file explains the origin and meaning of the scenario, as well as the process of data generation.Optional Data.• The feature costs file contains the costs of the feature groups, i.e., sets of features computed together.• The ground truth file specifies information on the instances and their respective solutions (e.g., SAT or UNSAT).• The literature references file in BibTeX format includes information on the context in which the data set was generated and previous studies in which it was used. Algorithm Selection Scenarios Provided in ASlib Release 2.0The set of algorithm selection scenarios in release version 2.0 of our library, shown in Table 2, has been assembled to represent a diverse set of selection problem settings that covers a wide range of problem domains, types of algorithms, 7 http://www.satcompetition.org/ 8 http://www.qbflib.org/index_eval.php 9 http://qbf.satisfiability.org/gallery/

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • Comprehensive overview of OR approaches for collaborative urban transportation • Analyzes real-world collaborative transportation systems, assessing pros and cons • Research outlook for the emerging field of collaborative transport optimization

W e solve a central problem in the liner shipping industry called the liner shipping fleet repositioning problem (LSFRP). The LSFRP poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between routes in a liner shipping network. Liner carriers wish to reposition vessels as cheaply as possible without disrupting cargo flows. The LSFRP is characterized by chains of interacting activities with a multicommodity flow over paths defined by the activities chosen. Despite its industrial importance, the LSFRP has received little attention in the literature. We introduce a novel mathematical model and a simulated annealing algorithm for the LSFRP with cargo flows that makes use of a carefully constructed graph; we evaluate these approaches using real-world data from our industrial collaborator. Additionally, we compare the performance of our approach against an actual repositioning scenario, one of many undertaken by our industrial collaborator in 2011. Our simulated annealing algorithm is able to increase the profit from $18.1 to $31.8 million using only a few minutes of CPU time. This shows that our algorithm could be used in a decision support system to solve the LSFRP.