Task assignment in home health care : a fuzzy group genetic algorithm approach

The assignment of home care tasks to nursing staff is a complex problem for decision makers concerned with optimizing home healthcare operations scheduling and logistics. Motivated by the ever-increasing home-based care needs, the design of high quality task assignments is highly essential for maintaining or improving worker moral, job satisfaction, service efficiency, service quality, and to ensure that business competitiveness remains momentous. To achieve high quality task assignments, the assigned workloads should be balanced or fair among the care givers. Therefore, the desired goal is to balance the workload of care givers while avoiding long distance travels in visiting the patients. However, the desired goal is often subjective as it involves the care givers, the management, and the patients. As such, the goal tends to be imprecise in the real world. This paper develops a fuzzy group genetic algorithm (FGGA) for task assignment in home healthcare services. The FGGA approach uses fuzzy evaluation based on fuzzy set theory. Results from illustrative examples show that the approach is promising

A signed distance for (γ,δ) interval-valued fuzzy numbers to solve multi objective assignment problems with fuzzy parameters

In this paper, a multi-objective assignment problem with fuzzy parameters (FMOASP) is introduced. These fuzzy parameters are characterized by an  interval-valued fuzzy numbers instead of fuzzy numbers. The signed distance ranking of  interval- valued fuzzy numbers of the parameters are not random but bear well-defined relationship to one another. A new approach namely, optimal flowing method is proposed to obtain the ideal and the set of all fuzzy efficient solutions for the problem. A numerical example is given to demonstrate the computational efficiency of the proposed approach

Fuzzy-Rough Set based Semi-Supervised Learning

Abstract—Much work has been carried out in the area of fuzzy-rough sets for supervised learning. However, very little has been accomplished for the unsupervised or semi-supervised tasks. For many real-word applications, it is often expensive, time-consuming and difficult to obtain labels for all data objects. This often results in large quantities of data which may only have very few labelled data objects. This paper proposes a novel fuzzy-rough based semi-supervised self-learning or self-training approach for the assignment of labels to unlabelled data. Unlike other semi-supervised approaches, the proposed technique requires no subjective thresholding or domain information. An experimental evaluation is performed on artificial data and also applied to a real-world mammographic risk assessment problem with encouraging results. Index Terms—Rough sets, fuzzy sets, mammographic analysis, semi-supervised learning I

Fuzzy encoding with hybrid pooling for visual dictionary in food recognition

Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous descriptions of local feature. Ironically, the ambiguous descriptions of local feature have triggered information loss in visual dictionary constructions from the hard assignment practices. The current method based on hard assignment and Fisher vector approach to construct visual dictionary have unexpectedly cause errors from the uncertainty problem during visual word assignation. This research proposes a method of combination in soft assignment technique by using fuzzy encoding approach and maximum pooling technique to aggregate the features to produce a highly discriminative and robust visual dictionary across various local features and machine learning classifiers. The local features by using MSER detector with SURF descriptor was encoded by using fuzzy encoding approach. Support vector machine (SVM) with linear kernel was employed to evaluate the effect of fuzzy encoding. The results of the experiments have demonstrated a noteworthy classification performance of fuzzy encoding approach compared to the traditional approach based on hard assignment and Fisher vector technique. The effects of uncertainty and plausibility were minimized along with more discriminative and compact visual dictionary representation

Fuzzy linear assignment problem: an approach to vehicle fleet deployment

This paper proposes and examines a new approach using fuzzy logic to vehicle fleet deployment. Fleet deployment is viewed as a fuzzy linear assignment problem. It assigns each travel request to an available service vehicle through solving a linear assignment matrix of defuzzied cost entries. Each cost entry indicates the cost value of a travel request that "fuzzily aggregates" multiple criteria in simple rules incorporating human dispatching expertise. The approach is examined via extensive simulations anchored in a representative scenario of taxi deployment, and compared to the conventional case of using only distances (each from the taxi position to the source point and finally destination point of a travel request) as cost entries. Discussion in the context of related work examines the performance and practicality of the proposed approach

Resource allocation for massively multiplayer online games using fuzzy linear assignment technique

This paper investigates the possible use of fuzzy system and Linear Assignment Problem (LAP) for resource allocation for Massively Multiplayer Online Games (MMOGs). Due to the limitation of design capacity of such complex MMOGs, resources available in the game cannot be unlimited. Resources in this context refer to items used to support the game play and activities in the MMOGs, also known as in-game resources. As for network resources, it is also one of the important research areas for MMOGs due to the increasing number of players. One of the main objectives is to ensure the Quality of Service (QoS) in the MMOGs environment for each player. Regardless, which context the resource is defined, the proposed method can still be used. Simulated results based on the network resources to ensure QoS shows that the proposed method could be an alternative

Resilience Assignment Framework using System Dynamics and Fuzzy Logic.

This paper is concerned with the development of a conceptual framework that measures the resilience of the transport network under climate change related events. However, the conceptual framework could be adapted and quantified to suit each disruption’s unique impacts. The proposed resilience framework evaluates the changes in transport network performance in multi-stage processes; pre, during and after the disruption. The framework will be of use to decision makers in understanding the dynamic nature of resilience under various events. Furthermore, it could be used as an evaluation tool to gauge transport network performance and highlight weaknesses in the network. In this paper, the system dynamics approach and fuzzy logic theory are integrated and employed to study three characteristics of network resilience. The proposed methodology has been selected to overcome two dominant problems in transport modelling, namely complexity and uncertainty. The system dynamics approach is intended to overcome the double counting effect of extreme events on various resilience characteristics because of its ability to model the feedback process and time delay. On the other hand, fuzzy logic is used to model the relationships among different variables that are difficult to express in numerical form such as redundancy and mobility