Samer Hanoun

Solving a multiobjective job shop scheduling problem using Pareto Archived Cuckoo Search

This paper investigates a new approach for solving the multiobjective job shop scheduling problem, namely the Cuckoo Search (CS) approach. The requirement is to schedule jobs on a single machine so that the total material waste is minimised as well as the total tardiness time. The material waste is quantified in terms of saving factors to show the reduction in material that can be achieved when producing two jobs with the same materials in sequence. The estimated saving factor is used to calculate a cost savings for each job based on its material type. A formulation of multiobjective optimisation problems is adopted to generate the set of schedules that maximise the overall cost savings and minimise the total tardiness time, where all trade-offs are considered for the two conflicting objectives. A Pareto Archived Multiobjective Cuckoo Search (PAMOCS) is developed to find the set of nondom-inated Pareto optimal solutions. The solution accuracy of PAMOCS is shown by comparing the closeness of the obtained solutions to the true Pareto front generated by the complete enumeration method. Results show that CS is a very effective and promising technique to solve job shop scheduling problems.

Decentralized mobility models for data collection in wireless sensor networks

Controlled mobility in wireless sensor networks provides many benefits towards enhancing the network performance and prolonging its lifetime. Mobile elements, acting as mechanical data carriers, traverse the network collecting data using single-hop communication, instead of the more energy demanding multi-hop routing to the sink. Scaling up from single to multiple mobiles is based more on the mobility models and the coordination methodology rather than increasing the number of mobile elements in the network. This work addresses the problem of designing and coordinating decentralized mobile elements for scheduling data collection in wireless sensor networks, while preserving some performance measures, such as latency and amount of data collected. We propose two mobility models governing the behaviour of the mobile element, where the incoming data collection requests are scheduled to service according to bidding strategies to determine the winner element. Simulations are run to measure the performance of the proposed mobility models subject to the network size and the number of mobile elements.

Target coverage in camera networks for manufacturing workplaces

In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k-covered with a predefined minimum spatial resolution. Since the problem is NP-complete, and even NP-hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.

Dynamic Route Construction for Mobile Collectors in Wireless Sensor Networks

Wireless sensor networks with mobile data collectors have been recently proposed for extending the sensor network lifetime. Powerful mobile collectors are deployed to patrol the network and approach the static sensors for collecting their data buffers using single hop communication. The route followed by the mobile collector is very crucial for the data collection operation performed in the network and highly impacts the data collection time. This paper presents a practically efficient algorithm for constructing the mobile collector route. The route is constructed dynamically during the network operational time regardless of the sensors data generation rates. The algorithm acts on minimizing the sleeping time and the number of sensors waiting for the arrival of the mobile collector. Simulation results demonstrate that the presented algorithm can effectively reduce the overall data collection time.

A Multiobjective State Transition Algorithm for Single Machine Scheduling

In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental results have demonstrated the effectiveness of the multiobjective state transition algorithm.

An effective heuristic for stockyard planning and machinery scheduling at a coal handling facility

Coal handling is a complex process involving different correlated and highly dependent operations such as selecting appropriate product types, planning stockpiles, scheduling stacking and reclaiming activities and managing train loads. Planning these operations manually is time consuming and can result in non-optimized schedules as future impact of decisions may not be appropriately considered. This paper addresses the operational scheduling of the continuous coal handling problem with multiple conflicting objectives. As the problem is NP-hard in nature, an effective heuristic is presented for planning stockpiles and scheduling resources to minimize delays in production and the coal age in the stockyard. A model of stockyard operations within a coal mine is described and the problem is formulated as a Bi-Objective Optimization Problem (BOOP). The algorithm efficacy is demonstrated on different real-life data scenarios. Computational results show that the solution algorithm is effective and the coal throughput is substantially impacted by the conflicting objectives. Together, the model and the proposed heuristic, can act as a decision support system for the stockyard planner to explore the effects of alternative decisions, such as balancing age and volume of stockpiles, and minimizing conflicts due to stacker and reclaimer movements.

Optimal Feature Subset Selection for Neuron Spike Sorting Using the Genetic Algorithm

It is crucial for a neuron spike sorting algorithm to cluster data from different neurons efficiently. In this study, the search capability of the Genetic Algorithm GA is exploited for identifying the optimal feature subset for neuron spike sorting with a clustering algorithm. Two important objectives of the optimization process are considered: to reduce the number of features and increase the clustering performance. Specifically, we employ a binary GA with the silhouette evaluation criterion as the fitness function for neuron spike sorting using the Super-Paramagnetic Clustering SPC algorithm. The clustering results of SPC with and without the GA-based feature selector are evaluated using benchmark synthetic neuron spike data sets. The outcome indicates the usefulness of the GA in identifying a smaller feature set with improved clustering performance.

A multi-objective evolutionary algorithm-based decision support system: A case study on job-shop scheduling in manufacturing

In this paper, an evolutionary algorithm is used for developing a decision support tool to undertake multi-objective job-shop scheduling problems. A modified micro genetic algorithm (MmGA) is adopted to provide optimal solutions according to the Pareto optimality principle in solving multi-objective optimisation problems. MmGA operates with a very small population size to explore a wide search space of function evaluations and to improve the convergence score towards the true Pareto optimal front. To evaluate the effectiveness of the MmGA-based decision support tool, a multi-objective job-shop scheduling problem with actual information from a manufacturing company is deployed. The statistical bootstrap method is used to evaluate the experimental results, and compared with those from the enumeration method. The outcome indicates that the decision support tool is able to achieve those optimal solutions as generated by the enumeration method. In addition, the proposed decision support tool has advantage of achieving the results within a fraction of the time.

Activity and Flight Trajectory Monitoring of Mosquito Colonies for Automated Behaviour Analysis

Monitoring and tracking of mosquitoes using image processing is important to facilitate the mosquitos’ behaviour analysis automatically over longer period of times. In this paper, we propose a simple methodology to monitor mosquitos’ activity using multiple cameras optimally placed. In order to ensure optimal camera coverage for the area of observation and desired image quality; we propose to simulate the experimental setup in a 3D virtual environment to obtain one-off optimum camera placement parameters. Our proposed methodology is demonstrated to have improved the activity monitoring process using two cameras for accurate count of occluded mosquitoes and 3D trajectory path reconstruction. This framework will enable working out more challenging tasks of constructing 3D trajectories using information received from multiple low quality cameras, which provide inconsistent and discontinuous trajectories.

A practical framework for data collection in wireless sensor networks

Optimizing energy consumption for extending the lifetime in wireless sensor networks is of dominant importance. Groups of autonomous robots and unmanned aerial vehicles (UAVs) acting as mobile data collectors are utilized to minimize the energy expenditure of the sensor nodes by approaching the sensors and collecting their buffers via single hop communication, rather than using multihop routing to forward the buffers to the base station. This paper models the sensor network and the mobile collectors as a system-of-systems, and defines all levels and types of interactions. A practical framework that facilitates deploying heterogeneous mobiles without prior knowledge about the sensor network is presented. Realizing the framework is done through simulation experiments and tested against several performance metrics.