Social Media, Gender and the Mediatisation of War: Exploring the German Armed Forces’ Visual Representation of the Afghanistan Operation on Facebook

Studies on the mediatisation of war point to attempts of governments to regulate the visual perspective of their involvements in armed conflict – the most notable example being the practice of ‘embedded reporting’ in Iraq and Afghanistan. This paper focuses on a different strategy of visual meaning-making, namely, the publication of images on social media by armed forces themselves. Specifically, we argue that the mediatisation of war literature could profit from an increased engagement with feminist research, both within Critical Security/Critical Military Studies and within Science and Technology Studies that highlight the close connection between masculinity, technology and control. The article examines the German military mission in Afghanistan as represented on the German armed forces’ official Facebook page. Germany constitutes an interesting, and largely neglected, case for the growing literature on the mediatisation of war: its strong antimilitarist political culture makes the representation of war particularly delicate. The paper examines specific representational patterns of Germany’s involvement in Afghanistan and discusses the implications which arise from what is placed inside the frame of visibility and what remains out of its view

Inadequate family history assessment by oncologists is an important physician barrier to referral for hereditary breast cancer evaluation

10.1016/j.clon.2013.11.029Clinical Oncology263174-175CLIO

A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization

A new approach for solving permutation scheduling problems with Ant Colony Optimization is proposed in this paper. The approach assumes that no precedence constraints between the jobs have to be fulfilled. It is tested with an ant algorithm for the Single Machine Total Weighted Deviation Problem. The new approach uses ants that allocate the places in the schedule not sequentially, as the standard approach, but in random order. This leads to a better utilization of the pheromone information

Decision support for packing in warehouses

Packing problems deal with loading of a set of items (objects) into a set of boxes (containers) in order to optimize a performance cri- teria under various constraints. With the advance of RFID technologies and investments in IT infrastructures companies now have access to the necessary data that can be utilized in cost reduction of packing processes. Therefore bin packing and container loading problems are becoming to be more popular in recent years. In this research we will propose a meta- heuristic based bin packing algorithm which was motivated from a real case. We will present the performance of the proposed techniques both in terms of cost and computational time. To our knowledge this paper is the …first attempt that proposes a solution to 3D-MBSBPP problem

Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows

Selected papers at Learning and Intelligent Optimization: Third International Conference, LION 3, Trento, Italy, January 14-18, 2009Beam-ACO algorithms are hybrid methods that combine the metaheuristic ant colony optimization with beam search. They heavily rely on accurate and computationally inexpensive bounding information for choosing between different partial solutions during the solution construction process. In this work we present the use of stochastic sampling as a useful alternative to bounding information in cases were computing accurate bounding information is too expensive. As a case study we choose the well-known travelling salesman problem with time windows. Our results clearly demonstrate that Beam-ACO, even when bounding information is replaced by stochastic sampling, may have important advantages over standard ACO algorithms.Peer ReviewedPostprint (published version

A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem ⋆

Abstract. The Shortest Common Supersequence Problem (SCSP) is a well-known hard combinatorial optimization problem that formalizes many real world problems. This paper presents a novel randomized search strategy, called probabilistic beam search (PBS), based on the hybridization between beam search and greedy constructive heuristics. PBS is competitive (and sometimes better than) previous state-of-the-art algorithms for solving the SCSP. The paper describes PBS and provides an experimental analysis (including comparisons with previous approaches) that demonstrate its usefulness.

Beam-ACO Applied to Assembly Line Balancing

Assembly line balancing concerns the design of assembly lines for the manufacturing of products. In this paper we consider the time and space constrained simple assembly line balancing problem with the objective of minimizing the number of necessary work stations. This problem is denoted by TSALBP-1 in the literature. For tackling this problem we propose a Beam-ACO approach, which is an algorithm that results from hybridizing ant colony optimization with beam search. The experimental results show that our algorithm is a state-of-the-art metaheuristic for this problem