A Component Based Heuristic Search Method with Evolutionary Eliminations

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.Comment: 27 pages, 4 figure

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT Distributed Application in Erlang/OTP

The University Course-Faculty Timetabling Problem (UCFTP) occurs in the Mindanao State University-Iligan Institute of Technology (MSU-IIT) as the delegation of classrooms for available subjects including time schedule and appropriate faculty personnel, taking into consideration constraints such as classroom capacities, location, and faculty preferences, etc. It is a more difficult variant of the classical University Course Timetabling Problem, which is an assignment problem and known to be NP-hard. This paper presents parallel Ant Colony Optimization Max-Min Ant System (ACO-MMAS) algorithm as an approach in solving the UCFTP instance in the institute. ACO employs virtual ants moving across a search space and using an indirect form of constructive feedback by depositing pheromones on the paths they traverse in order to influence other ants in their searches. We have developed an application to automate the timetabling process using Erlang/OTP, a functional language specializing in concurrent and distributed systems. UCFTP was successfully represented into a mathematical problem instance and solved using the ACO-MMAS algorithm applied on a distributed network setup under Parallel Independent Run and Unidirectional Ring topologies. Extensive testing was performed to properly analyze the search behavior under different parameter settings

Fast point pattern matching by heuristic and stochastic optimization techniques

This work is concerned with one of the methodologies used in the final stages of machine vision: the matching of model point patterns to observed point patterns. Conventional search methods not only fail to arrive at the optimal match, but are also computationally expensive and time consuming. To arrive at the optimal pattern match, stochastic and heuristic optimization as the search technique, exploiting Simulated Annealing (SA), Evolutionary Programming (EP) and Mean Field Annealing (MFA), are explored in detail. A comparison of results obtained using SA versus hill-climbing and exhaustive search techniques, and results of EP are presented. The relative effectiveness of these optimizing search algorithms over other conventional algorithms will be demonstrated. Finally, the limitations of MFA are discussed

Disruption management

The main objective of this project is to model the ARP (Aircraft Recovery Problem) from a constraint programming (CP) point of view. The information required for this project is extracted from previous papers that cope with the problem using heuristics, metaheuristics or using network-models. Also, two scenarios will be tested to verify that the implementation is correct

Constrained Navigation with Mandatory Waypoints in Uncertain Environment

Also available online at http://www.ijisce.org/admin/upload/946980IJISCE-Constrained%20Navigation%20with%20Mandatory%20Waypoints%20in%20Uncertain%20Environment.pdfInternational audienceThis paper presents a hybrid solving method for vehicle path planning problems. As part of the vehicle system architecture (vetronic), planning is dynamic and has to be activated on-line, which requires response times to be compatible with mission execution. The proposed approach combines constraint solving techniques with an Ant Colony Optimization (ACO). The hybridization relies on a static probing technique which builds up a search strategy using a distance information between problem variables and a heuristic solution. Various forms of this approach are compared and evaluated on real world scenarios. Preliminary results exhibit response times close to vehicle control requirements, on realistic problem instances

A Component Based Heuristic Search Method with AdaptivePerturbations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with adaptive perturbations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then mimic a natural evolutionary process on these components to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs a dynamic evaluation function which evaluates how well each component contributes towards the final objective. Two perturbation steps are then applied: the first perturbation eliminates a number of components that are deemed not worthy to stay in the current schedule; the second perturbation may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems