A stochastic local search algorithm with adaptive acceptance for high-school timetabling

Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective 'heuristic to choose heuristics' to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism. © 2014 Springer Science+Business Media New York

A time predefined variable depth search for nurse rostering

This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue extending a chain and which candidates to examine as the next potential link in the chain. Because end users vary in how long they are willing to wait for solutions, a particular goal of this research was to create an algorithm that accepts a user specified computational time limit and uses it effectively. When compared against previously published approaches the results show that the algorithm is very competitive

Enhancement of Nurse Scheduling Steps Using Particle Swarm Optimization

Allocation of working schedule especially for shift approach is hard to ensure its fairness among them. In the case of nurse scheduling, to set up the time tablefor each available nurse is time consuming and complicated, which consider many factors including rules, regulation and human factor. Moreover, most nurses are women which have personnel constraints and maternity leave factors. The undesirable schedule can affect the nurse productivity, social life and the absenteeism can significantly as well affect patient's life. This paper aimed to enhance the scheduling process by utilizing the particle swarm optimization in order to generate an intelligent nurse schedule. The result shows that the multiple initial schedule can be generated and can be selected with the lowest cost of constraint violation

Mapping and scheduling hard real time applications on multicore systems - the ARGO approach

Using multi-core architectures for embedded time-critical systems creates a big challenge for developers due to the complexity of the underline mapping and scheduling problem. H2020 ARGO project [2] proposes a tool flow to minimize multi-core applications development time while guaranteeing real-time performance. In this paper, we provide an overview of ARGO tool flow and we focus on the heuristic approach of solving the worst case execution time aware (WCET) mapping and scheduling problem on hierarchical task graphs. Examples from two real applications from the aerospace and image processing domains are presented