Heuristics for the multi-period orienteering problem with multiple time windows

We present the multi-period orienteering problem with multiple time windows (MuPOPTW), a new routing problem combining objective and constraints of the orienteering problem (OP) and team orienteering problem (TOP), constraints from standard vehicle routing problems, and original constraints from a real-world application. The problem itself comes from a real industrial case. Specific route duration constraints result in a route feasibility subproblem. We propose an exact algorithm for this subproblem, and we embed it in a variable neighborhood search method to solve the whole routing problem. We then provide experimental results for this method. We compare them to a commercial solver. We also adapt our method to standard benchmark OP and TOP instances, and provide comparative tables with state-of-the-art algorithms.     

Variable neighborhood search for the dial-a-ride problem

In dial-a-ride problems passengers have to be transported between pre-specified pickup and delivery locations under user inconvenience considerations. The problem variant considered in this paper aims at minimizing total routing costs while respecting maximum route duration limits, time windows, and maximum user ride time limits. We propose a competitive variable neighborhood search-based heuristic, using three classes of neighborhoods. The first neighborhood class uses simple swap operations tailored to the dial-a-ride problem; the second neighborhood class is based on the ejection chain idea; and the third neighborhood class exploits the existence of arcs where the vehicle load is zero, giving rise to natural sequences of requests. We report new best results for 16 out of 20 benchmark instances.     

Polynomial regression under arbitrary product distributions

In recent work, Kalai, Klivans, Mansour, and Servedio (2005) studied a variant of the “Low-Degree (Fourier) Algorithm” for learning under the uniform probability distribution on {0,1} ⁿ . They showed that the L ₁ polynomial regression algorithm yields agnostic (tolerant to arbitrary noise) learning algorithms with respect to the class of threshold functions—under certain restricted instance distributions, including uniform on {0,1} ⁿ and Gaussian on ? ⁿ . In this work we show how all learning results based on the Low-Degree Algorithm can be generalized to give almost identical agnostic guarantees under arbitrary product distributions on instance spaces X ₁×???×X _n . We also extend these results to learning under mixtures of product distributions. The main technical innovation is the use of (Hoeffding) orthogonal decomposition and the extension of the “noise sensitivity method” to arbitrary product spaces. In particular, we give a very simple proof that threshold functions over arbitrary product spaces have δ-noise sensitivity $O(\sqrt{\delta})$ , resolving an open problem suggested by Peres (2004).     

Method for analysis and dynamism of factory structure in automotive manufacturing

A turbulent environment characterized by unsteady economic cycles, customized products, a growing bandwidth of products, an exploding number of variants and shorter product life cycles force manufacturers to permanent adaptation of their factories. Flexible and changeable structures will be required to enable factories dealing with the technological challenges and economic pressure of the future competitively. In order to achieve changeability objectives in manufacturing, a detailed analysis of existing structures and its representative attributes is essential. It is the basis for systematic structure planning of factories. In this paper a method for analyzing the capacitive and technological structure of a factory embedded in a network of manufacturing and its network of suppliers is presented. The synchronization of product and production development under the influence of change is intended. Therefore, the structural views of product and production are specifically in focus of the method. Based on the results of the analysis models an approach of a tool for giving product and production structure dynamism is suggested to investigate the effects and dependencies of change drivers in manufacturing.     

A modern workflow for force-field development – Bridging quantum mechanics and atomistic computational models

In this article we present our recent efforts in designing a comprehensive consistent scientific workflow, nicknamed Wolf₂ Pack, for force-field optimization in the field of computational chemistry. Atomistic force fields represent a multiscale bridge that connects high-resolution quantum mechanics knowledge to coarser molecular mechanics-based models. Force-field optimization has so far been a time-consuming and error-prone process, and is a topic where the use of a scientific workflow can provide obvious great benefits. As a case study we generate a gas-phase force field for methanol using Wolf₂ Pack, with special attention given toward deriving partial atomic charges.