Jana Ries

Sensor data acquisition and processing parameters for human activity classification.

It is known that parameter selection for data sampling frequency and segmentation techniques (including different methods and window sizes) has an impact on the classification accuracy. For Ambient Assisted Living (AAL), no clear information to select these parameters exists, hence a wide variety and inconsistency across todays literature is observed. This paper presents the empirical investigation of different data sampling rates, segmentation techniques and segmentation window sizes and their effect on the accuracy of Activity of Daily Living (ADL) event classification and computational load for two different accelerometer sensor datasets. The study is conducted using an ANalysis Of VAriance (ANOVA) based on 32 different window sizes, three different segmentation algorithm (with and without overlap, totaling in six different parameters) and six sampling frequencies for nine common classification algorithms. The classification accuracy is based on a feature vector consisting of Root Mean Square (RMS), Mean, Signal Magnitude Area (SMA), Signal Vector Magnitude (here SMV), Energy, Entropy, FFTPeak, Standard Deviation (STD). The results are presented alongside recommendations for the parameter selection on the basis of the best performing parameter combinations that are identified by means of the corresponding Pareto curve.

New Developments in Multiple Objective and Goal Programming

This volume shows the state-of-the-art in both theoretical development and application of multiple objective and goal programming. Applications from the fields of supply chain management, financial portfolio selection, financial risk management, insurance, medical imaging, sustainability, nurse scheduling, project management, water resource management, and the interface with data envelopment analysis give a good reflection of current usage. A pleasing variety of techniques are used including models with fuzzy, group-decision, stochastic, interactive, and binary aspects. Additionally, two papers from the upcoming area of multi-objective evolutionary algorithms are included. The book is based on the papers of the 8th International Conference on Multi-Objective and Goal Programming (MOPGP08) which was held in Portsmouth, UK, in September 2008.

Instance-specific multi-objective parameter tuning based on fuzzy logic

Finding good parameter values for meta-heuristics is known as the parameter setting problem. A new parameter tuning strategy, called IPTS, is proposed that is a novel instance-specific method to take the trade-off between solution quality and computational time into consideration. Two important steps in the method are an a priori statistical analysis to identify the factors that determine heuristic performance in both quality and time for a specific type of problem, and the transformation of these insights into a fuzzy inference system rule base which aims to return parameter values on the Pareto-front with respect to a decision maker’s preference.

A fuzzy logic model for the container stacking problem at container terminals

We address the problem of storage space allocation in a sea port terminal. The problem consists of assigning a block space in the yard of a container terminal to every incoming container while ensuring operational efficiency. The proposed framework uses a 2-stage framework in combination with a fuzzy logic rule-based strategy. The concept is motivated by the problem faced by container terminals in Chile and the aim is to provide real-time decision support to deal with a high degree of uncertainty in the arrival of containers at the yard. In addition, the framework provides a more flexible design to include a set of different criteria as well as different infrastructures and layouts of container ports. Numerical results are presented, comparing the results of the fuzzy framework with respect to algorithms proposed in the literature, considering different scenarios.

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.

The impact of middle management commitment on improvement initiatives in public organisations

Purpose – Top management commitment is considered a significant factor in improvement programmes, and many papers have been written about the role of top management commitment in implementing a quality management system. However, not considering other management levels’ commitment, such as middle management, may lead to issues in achieving organisational development. Public organisations that work through vertical structures may face a lack of middle management commitment, which might have a negative impact on lower and non-management staff commitment to improvement programmes. In this regard, the purpose of this paper is to examine the impact of middle management’s commitment towards improvement initiatives in public organisations. Design/methodology/approach – Empirical research with a mixed-method design used semi-structured interviews and a questionnaire to explore the current practices of continuous improvement (CI) and examine employees’ views from different management levels of the implications of ...

A Semi-Automated Design of Instance-Based Fuzzy Parameter Tuning for Metaheuristics Based on Decision Tree Induction

Two main concepts are established in the literature for the Parameter Setting Problem of metaheuristics: Parameter Tuning Strategies (PTS) and Parameter Control Strategies (PCS). While PTS result in a fixed parameter setting for a set of problem instances, PCS are incorporated into the metaheuristic and adapt parameter values according to instance-specific performance feedback. The idea of Instance-specific Parameter Tuning Strategies (IPTS) is aiming to combine advantages of both tuning and control strategies by enabling the adoption of parameter values tailored to instance-specific characteristics a priori to running the metaheuristic. This requires, however, a significant knowledge about the impact of instance characteristics on heuristic performance. This paper presents an approach that semi-automatically designs the fuzzy logic rule base to obtain instance-specific parameter values by means of decision trees. This enables the user to automate the process of converting insights about instance-specific information and its impact on heuristic performance into a fuzzy rule base IPTS system. The system incorporates the decision maker’s preference about the trade-off between computational time and solution quality.

Simulated annealing approach to solve dual resource constrained job shop scheduling problems: layout impact analysis on solution quality

Real-world manufacturing systems are operating subject to a substantial level of resource constraints. One characteristic model that considers the combination of human and machine resource constraints is called dual resource constrained (DRC). In this context a number of machines nmach is managed by a selection of operators nop, with typically nop ≤ nmach.. A real life case study for an Italian manufacturing company is introduced that uses a set of identical parallel machines being operated by a set of operators. Each job is scheduled to one machine with corresponding loading and unloading process times. A simulated annealing approach is proposed to solve the DRC job shop scheduling problem. A sensitivity analysis is conducted for a selection of algorithm-specific parameters used to solve characteristic DRC layouts. Being characteristic for the just-in-time (JIT) production environment, the high variability in job times has also been taken into account. The results show that the selected layout nmach./nop ratio strongly influences the production system performance. The impact of the ratio of constrained resources has been analysed for different layouts, showing that simulated annealing performs better for single resource constrained problems while also demonstrating that this trend is not symmetrical for different layouts, either operator or machine constrained.

A multi-criteria support system for dynamic aerial vehicle routing problems

The aim of this paper is to implement a decision support system for routing Unmanned Aerial Vehicles (UAV) in the context of maritime surveillance. The sea environment is highly uncertain and dynamic which requires the routing calculation to be highly responsive to changes and to operate in real-time. The approach adopted for this problem combines the multicriteria decision methods AHP, PROMETHEE and mathematical programming to establish a real-time adaptive routing system.

Towards a parameter tuning approach for a map-matching algorithm

Map Matching Algorithms (MMA) are developed to solve spatial ambiguities that arise in the process of assigning GPS measurements onto a digital roadway network. There is a lack of systematic parameter tuning approaches for optimizing the MMA performance. Thus, a novel integrated framework is proposed for a systematic calibration of the parameters of a post-processing MMA. The calibration approach consists of an Instance-specific Parameter Tuning Strategy (IPTS) that employs Fuzzy Logic principles. The proposed fuzzy IPTS tool determines the best algorithm parameter values by using instance-specific information a priori to the execution of the MMA. A preliminary prototype of an IPTS system is designed based on real-world data, which identifies the explanatory variables that condition the MMA performance. The implementation of the fuzzy IPTS tool on real-word data yields an enhanced MMA performance in the solution quality and computational time compared to the results of the execution of the MMA with constant algorithm settings.