Genetic neuro-scheduler for job shop scheduling

This paper describes a hybrid approach between two new techniques, Genetic Algorithms and Artificial Neural Networks, for generating Job Shop Schedules (JSS) in a discrete manufacturing environment based on non-linear multi-criteria objective function. Genetic Algorithm (GA) is used as a search technique for an optimal schedule via a uniform randomly generated population of gene strings which represent alternative feasible schedules. GA propagates this specific gene population through a number of cycles or generations by implementing natural genetic mechanism (i.e. reproduction operator and crossover operator). It is important to design an appropriate format of genes for JSS problems. Specifically, gene strings should have a structure that imposes the most common restrictive constraint; a precedence constraint. The other is an Artificial Neural Network, which uses its highly connected-neuron network to perform as a multi-criteria evaluator. The basic idea is a neural network evaluator which maps a complex set of scheduling criteria (i.e. flowtime, lateness) to evaluate values provided by experienced experts. Once, the network is fully trained, it will be used as an evaluator to access the fitness or performance of those stimulated gene strings.

The proposed approach was prototyped and implemented on JSS problems based on different model sizes; namely small, medium, and large. The results are compared to the Shortest Proceesing Time heuristic used extensively in industry.     

Preserving User Anonymity in Context‐Aware Location‐Based Services: A Proposed Framework

Protecting privacy is an important goal in designing location‐based services. Service providers want to verify legitimate users and allow permitted users to enjoy their services. Users, however, want to preserve their privacy and prevent tracking. In this paper, a new framework providing users with more privacy and anonymity in both the authentication process and the querying process is proposed. Unlike the designs proposed in previous works, our framework benefits from a combination of three important techniques: k‐anonymity, timed fuzzy logic, and a one‐way hash function. Modifying and adapting these existing schemes provides us with a simpler, less complex, yet more mature solution. During authentication, the one‐way hash function provides users with more privacy by using fingerprints of users' identities. To provide anonymous authentication, the concept of confidence level is adopted with timed fuzzy logic. Regarding location privacy, spatial k‐anonymity prevents the users' locations from being tracked. The experiment results and analysis show that our framework can strengthen the protection of anonymity and privacy of users by incurring a minimal implementation cost and can improve functionality.