Interactive Human-Guided Optimization for Logistics Planning

Mattar N, Kulms P, Kopp S. Interactive Human-Guided Optimization for Logistics Planning. In: Pielot M, ed. Mensch und Computer 2015 – Tagungsband. Walter de Gruyter GmbH & Co KG; 2015: 183-192.Logistics planning is an important problem in industry, where goods have to be parceled appropriately to meet delivery dates or reduce shipping costs. This optimization problem is classically solved offline using standard algorithms and focused heuristics, e.g. bin packing or route planning. However, in practical work environments constraints may change flexibly (e.g. when custom-tailored goods with variable size have to be handled) and it is often not clear what an optimal solution looks like. Further, logistics planning consists of multiple steps that often are handled by different human employees in different departments. In this paper we propose an interactive approach using human-guided optimization, where solution spaces can be interactively explored, manipulated, and constrained at runtime. Based on an analysis of the problem of multi-step logistics planning, we present a system that supports users in solving this optimization problem, and we report first evaluation results obtained in the first two iterations of an user-centered design process

Tuning Tabu Search strategies via visual diagnosis

Abstract: While designing working metaheuristics can be straightforward, tuning them to solve the underlying combinatorial optimization problem well can be tricky. Several tuning methods have been proposed but they do not address the new aspect of our proposed classification of the metaheuristic tuning problem: tuning search strategies. We propose a tuning methodology based on Visual Diagnosis and a generic tool called Visualizer for Metaheuristics Development Framework (V-MDF) to address specifically the problem of tuning search (particularly Tabu Search) strategies. Under V-MDF, we propose the use of a Distance Radar visualizer where the human and computer can collaborate to diagnose the occurrence of negative incidents along the search trajectory on a set of training instances, and to perform remedial actions on the fly. Through capturing and observing the outcomes of actions in a Rule-Base, the user can then decide how to tune the search strategy effectively for subsequent use

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20×20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads

User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems

The optimisation of brass instruments to include wall vibration effects

This thesis focuses on the design optimisation of a brass instrument. The bore profile of such an instrument is known to be the primary influence on the sound of the instrument as it directly controls the shape of the air-column contained within the instruments' walls. It has long been claimed, however, that other factors, such as the wall material and wall vibrations, are also significant, although to a lesser degree. In recent years, it has been proven that wall vibrations do indeed have an audible effect on the sound (Moore et al 2005, Kausel et al 2007, Nachtmann et al 2007, Kausel, Zietlow and Moore 2010). This effect corresponds to a relative increase in the power of upper harmonics of the sound spectrum when vibrations are greatest, and relative increase in the power of the lower harmonics, in particular the fundamental, when vibrations are at their least. The result is a timbral difference where a greater relative power in the upper harmonics results in a 'brighter' sound, and where the opposite results in a 'darker' sound. Studies have also found that the degree of the wall vibration is increased when the resonant frequencies of the air-column and those of the instruments' structure align. It is this principle that this work is based on. The primary objective of this work was to devise a suitable approach for incorporating the wall vibration effect into an optimisation method to investigate the optimum designs for two scenarios: maximum wall vibration and minimum wall vibration. It was also of interest to investigate if there were any design characteristics for each scenario. Two analysis methods were investigated for their suitability, namely free and forced vibration using finite element analysis (FEA). Different approaches to defining the design variables were explored and the suitability of different optimisation algorithms was investigated. The free vibration approach was found to be inadequate for this application due to the inherent omission of valuable magnitude information. The forced vibration approach was found to be more successful, although it was not possible to align a resonance with each frequency of interest

Assessing the performance of human-automation collaborative planning systems

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 215-221).Planning and Resource Allocation (P/RA) Human Supervisory Control (HSC) systems utilize the capabilities of both human operators and automated planning algorithms to schedule tasks for complex systems. In these systems, the human operator and the algorithm work collaboratively to generate new scheduling plans, each providing a unique set of strengths and weaknesses. A systems engineering approach to the design and assessment of these P/RA HSC systems requires examining each of these aspects individually, as well as examining the performance of the system as a whole in accomplishing its tasks. An obstacle in this analysis is the lack of a standardized testing protocol and a standardized set of metric classes that define HSC system performance. An additional issue is the lack of a comparison point for these revolutionary systems, which must be validated with respect to current operations before implementation. This research proposes a method for the development of test metrics and a testing protocol for P/RA HSC systems. A representative P/RA HSC system designed to perform high-level task planning for deck operations on United States Naval aircraft carriers is utilized in this testing program. Human users collaborate with the planning algorithm to generate new schedules for aircraft and crewmembers engaged in carrier deck operations. A metric class hierarchy is developed and used to create a detailed set of metrics for this system, allowing analysts to detect variations in performance between different planning configurations and to depict variations in performance for a single planner across levels of environment complexity. In order to validate this system, these metrics are applied in a testing program that utilizes three different planning conditions, with a focus on validating the performance of the combined Human-Algorithm planning configuration. Experimental result analysis revealed that the experimental protocol was successful in providing points of comparison for planners within a given scenario while also being able to explain the root causes of variations in performance between planning conditions. The testing protocol was also able to provide a description of relative performance across complexity levels. The results demonstrate that the combined Human-Algorithm planning condition performed poorly for simple and complex planning conditions, due to errors in the recognition of a transient state condition and in modeling the effects of certain actions, respectively. The results also demonstrate that Human planning performance was relatively consistent as complexity increased, while combined Human-Algorithm planning was effective only in moderate complexity levels. Although the testing protocol used for these scenarios and this planning algorithm was effective, several limiting factors should be considered. Further research must address how the effectiveness of the defined metrics and the test methodology changes as different types of planning algorithms are utilized and as a larger number of human test subjects are incorporated.by Jason C. Ryan.S.M

An integrated White+Black box approach for designing and tuning stochastic local search algorithms

Ph.DDOCTOR OF PHILOSOPH

Human-Guided Tabu Search

We present a human-guidable and general tabu search algorithm