An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

Non-linear great deluge with learning mechanism for solving the course timetabling problem

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Designing a multi-agent approach system for distributed course timetabling

This paper proposes tackling the difficult course timetabling problem using a multi-agent approach. The proposed design seeks to deal with the problem using a distributed solution environment in which a mediator agent coordinates various timetabling agents that cooperate to improve a common global solution. Initial timetables provided to the multi-agent system are generated using several hybrid heuristics that combine graph colouring heuristics and local search in different ways. The hybrid heuristics are capable of generating feasible timetables for all instances of the two sets of benchmark problems used here. We discuss how these initialisation hybrid heuristics can be incorporated into the proposed multi-agent approach in order to conduct distributed timetabling. This preliminary work serves as a solid basis towards the design of an effective multi-agent distributed timetabling system

A guided search non-dominated sorting genetic algorithm for the multi-objective university course timetabling problem

Copyright @ Springer-Verlag Berlin Heidelberg 2011.The university course timetabling problem is a typical combinatorial optimization problem. This paper tackles the multi-objective university course timetabling problem (MOUCTP) and proposes a guided search non-dominated sorting genetic algorithm to solve the MOUCTP. The proposed algorithm integrates a guided search technique, which uses a memory to store useful information extracted from previous good solutions to guide the generation of new solutions, and two local search schemes to enhance its performance for the MOUCTP. The experimental results based on a set of test problems show that the proposed algorithm is efficient for solving the MOUCTP

Hybrid heuristic for multi-carrier transportation plans

This paper describes a hybrid heuristic approach to construct transportation plans for a singlecustomer multi-carrier scenario that arises at 3T Logistics Ltd, a UK company that provides outsourced transportation planning and management services. The problem consists on planning the delivery, using a set of carrier companies, of a set of shipments from a warehouse to different consignees across the UK. The problem tackled resembles a vehicle routing problem with time windows but there are several differences in our scenario. The hybrid heuristic algorithm described here combines a clustering algorithm, constructive and local search heuristics, and exact assignment based on integer programming. This approach is being currently evaluated at the company and results so far indicate the suitability of the algorithm to produce practical transportation plans at reduced cost compared to current practice

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1