Combining the Min-Conflicts and Look-Forward Heuristics to Effectively Solve A Set of Hard University Timetabling Problems

University timetabling problems (UTPs) represent a class of challenging, high-dimensional and multi-objectives combinatorial optimization problems that are commonly solved by constructive search, local search methods or their hybrids. In this paper, we proposed to combine the min-conflicts and look-forward heuristics used in local search methods to effectively solve general university timetabling problems. Our combined heuristics when augmented with the k-reset operator, and appropriate heuristic variable ordering strategy achieved impressive results on a set of challenging UTPs obtained from an international timetabling competition. A preliminary analysis of the results was given. More importantly, our search proposal shed light on effectively solving other complex or large-scale scheduling problems.published_or_final_versio

An adjustable robust optimization approach for periodic timetabling

In this paper, we consider the Robust Periodic Timetabling Problem (RPTP), the problem of designing a periodic timetable that can easily be adjusted in case of small periodic disturbances. We develop a solution method for a parametrized class of uncertainty regions. This class relates closely to uncertainty regions known in the robust optimization literature, and naturally defines a metric for the robustness of the timetable. The proposed solution method combines a linear decision rule with well-known reformulation techniques and cutting-plane methods. We show that the RPTP can be solved for practical-sized instances by applying the solution method to practical cases of Netherlands Railways (NS). In particular, we show that the trade-off between the efficiency and robustness of a timetable can be analyzed using our solution method

Effective Heuristics to Solve Pickup and Delivery Problems with Time Windows

Pickup and delivery problem with time windows (PDP-TW) is a challenging scheduling problem for which each delivery is coupled with a pickup request. Metaheuristic search techniques like the tabu search have been used to solve PDP-TW. In this paper, we investigated a min-conflicts based micro-genetic algorithm combining some interesting construction heuristic, namely the Align-Fold or Boomerang, and repair heuristics including a new Swap operator and a modified billiard operator to effectively solve PDD-TW. Our results compared favorably against those of a tabu-embedded metaheuristic search on a set of modified Solomon's test cases. More importantly, our proposed heuristics can easily be integrated into many search schemes for solving other complex scheduling problems.published_or_final_versio

Effective Heuristics to Solve Pickup and Delivery Problems with Time Windows

Pickup and delivery problem with time windows (PDP-TW) is a challenging scheduling problem for which each delivery is coupled with a pickup request. Metaheuristic search techniques like the tabu search have been used to solve PDP-TW. In this paper, we investigated a min-conflicts based micro-genetic algorithm combining some interesting construction heuristic, namely the Align-Fold or Boomerang, and repair heuristics including a new Swap operator and a modified billiard operator to effectively solve PDD-TW. Our results compared favorably against those of a tabu-embedded metaheuristic search on a set of modified Solomon's test cases. More importantly, our proposed heuristics can easily be integrated into many search schemes for solving other complex scheduling problems.published_or_final_versio

Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems

Genetic algorithms in timetabling and scheduling

Thio thesis investigates the use of genetic algorithms (GAs) for solving a range of timetabling and scheduling problems. Such problems arc very hard in general, and GAs offer a useful and successful alternative to existing techniques.A framework is presented for GAs to solve modular timetabling problems in edu¬ cational institutions. The approach involves three components: declaring problemspecific constraints, constructing a problem specific evaluation function and using a problem-independent GA to attempt to solve the problem. Successful results are demonstrated and a general analysis of the reliability and robustness of the approach is conducted. The basic approach can readily handle a wide variety of general timetabling problem constraints, and is therefore likely to be of great practical usefulness (indeed, an earlier version is already in use). The approach rclicG for its success on the use of specially designed mutation operators which greatly improve upon the performance of a GA with standard operators.A framework for GAs in job shop and open shop scheduling is also presented. One of the key aspects of this approach is the use of specially designed representations for such scheduling problems. The representations implicitly encode a schedule by encoding instructions for a schedule builder. The general robustness of this approach is demonstrated with respect to experiments on a range of widely-used benchmark problems involving many different schedule quality criteria. When compared against a variety of common heuristic search approaches, the GA approach is clearly the most successful method overall. An extension to the representation, in which choices of heuristic for the schedule builder arc also incorporated in the chromosome, iG found to lead to new best results on the makespan for some well known benchmark open shop scheduling problems. The general approach is also shown to be readily extendable to rescheduling and dynamic scheduling