Scatter Search metaheuristic for Post-Enrolment Course Timetabling Problems: A Review

Hybrid scatter search and path relinking for the capacitated p-median problem

PurposeIn this paper, the author introduces a new variant of the pickup and delivery transportation problem, where one commodity is collected from many pickup locations to be delivered to many delivery locations within pre-specified time windows (one–to many–to many). The author denotes to this new variant as the 1-commodity pickup-and-delivery vehicle routing problem with soft time windows (1-PDVRPTW).Design/methodology/approachThe author proposes a hybrid genetic algorithm and a scatter search to solve the 1-PDVRPTW. It proposes a new constructive heuristic to generate the initial population solution and a scatter search (SS) after the crossover and mutation operators as a local search. The hybrid genetic scatter search replaces two steps in SS with crossover and mutation, respectively.FindingsSo, the author proposes a greedy local search algorithm as a metaheuristic to solve the 1-PDVRPTW. Then, the author proposes to hybridize the metaheuristic to solve this variant and to make a good comparison with solutions presented in the literature.Originality/valueThe author considers that this is the first application in one commodity. The solution methodology based on scatter search method combines a set of diverse and high-quality candidate solutions by considering the weights and constraints of each solution.

The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.

This paper studies a kind of hybrid flow shop problem with transportation delays (HFSTD) between consecutive production stages. A scatter search (SS) algorithm is proposed to solve the scheduling problem. A two-point order combination operator is designed for the SS. The authors randomly generate 12 instances to test the performance of the proposed algorithm. Experimental results show that the SS algorithm is efficient and effective for solving the HFSTD scheduling problem.

Ant System algorithms are nature-inspired population-based metaheuristics derived from the field of swarm intelligence. Seemingly, the ant system has a lack of search diversity control since it has only a global pheromone update that intensifies the search. Hence, one or more assistant mechanisms are required to strengthen the search of the ant system. Therefore, we propose, in this study, an elitist-ant system to strike a balance between search diversity and intensification while maintaining the quality of solutions. This process is achieved by employing two diversification and intensification mechanisms to assist both pheromone evaporation and elite pheromone updating, in order to gain a good control over the search exploration and exploitation. The diversification mechanism is employed to avoid early convergence, whilst the intensification mechanism is employed to exploore the neighbors of a solution more effectively. In this paper, we test our algorithm on post-enrolment course timetabling problem. Experimental results show that our algorithm produces good quality solutions and outperforms some results reported in the literature (with regards to Socha’s instances) including other ant system algorithms. Therefore, we can conclude that our elitist-ant system has performed an efficient problem’s specific knowledge exploitation, and an effective guided search exploration to obtain better quality solutions.KeywordsElitist-ant systemintensification and diversificationpost-enrolment course timetabling problem

In this work, we propose a Genetic Algorithm (GA) in addressing a post enrolment course timetabling (PE-CTT) problem for University Malaysia Sabah-Labuan International Campus (UMS-LIC). Tabu Search with Sampling and Perturbation (TSSP) is used to initiate a pool of feasible solutions in the GA. Experiments are conducted to set the best parameter values for the algorithm to operate optimally under a computation time limit. The proposed methodology is tested on a dataset based on semester 1, session 2018/2019 student registration. The automated timetables are compared with the one generated manually by the administrative staff of UMS-LIC. The former outperformed the latter in terms of hard and soft constraint violations (approximately 54% improvement). Experimental results are discussed.

The evolutionary metaheuristic called scatter search has been applied successfully to optimization problems for several years. In this paper, we apply the scatter search technique to the well-known 0–1 multidimensional knapsack problem. We propose a new relaxation-based diversification generator, which produces an initial population with elite solutions. The computational results obtained for a set of classic and correlated instances clearly show that (1) this generator can also be used as a heuristic for solving the multidimensional knapsack problem; (2) using the population produced by our generator as a starting point for the scatter search algorithm leads to better performance. We also enhance the scatter search algorithm by integrating memory and by using adapted intensification phases. Overall, the results are interesting and competitive compared to other population-based algorithms, such as genetic algorithms.

Logistics Equipment Scheduling (LES) problem in iron and steel industry is to select appropriate raw material piles and equipments for logistics equipment schedule with the objective of minimizing the operation cost of raw material yard, maximizing the utilization and the equilibrium ratio of reclaimers in the production process. Different from previous literatures, according to the characteristics of raw material yard, the LES problem is divided into two phases: the first one is how to select raw material piles, the other one is a reclaimer scheduling problem. The LES problem is formulated as a multi-targets mixed integer programming model by considering the practical requirements. The complexity of the model motivates us to develop a scatter search-based strategy algorithm to solve the problem approximately. Problem-oriented coding scheme and solution combination method are proposed in scatter search. The computational results on real data show that the cost is decreased by 25.85% in average compared with manual schedule.

Solving scheduling problems enables more efficient use of production capacity. It involves defining the sequence of operations, determining the capacity of resources, and balancing workloads. Different methods, especially metaheuristics, have been used to solve these problems. This study presents the application of Scatter Search (SS), Genetic Algorithm (GA), and Greedy Randomized Adaptive Search Procedures (GRASP) for minimizing makespan in a permutation flow shop environment. In this study, the performances of these methods are compared through various test problems in the literature and a real-life problem of a company operating in the automotive sector. Study comprises 48 jobs that must be planned within a day for eight consecutive operations. In cellular manufacturing, the sequence in which each job is processed in eight operations is the same. In solving permutation flow shop scheduling problems (PFSP), one of the NP-hard problems, meta-heuristic methods are widely applied due to their successful results. From this point of view, SS, GA, and GRASP are employed in this study, and their performances are compared.

Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. We present a scatter-search implementation designed to find high-quality solutions for the 3D image-registration problem, which has many practical applications. This problem arises in computer vision applications when finding a correspondence or transformation between two computer images obtained under different conditions. Our implementation goes beyond a simple exercise on applying scatter search, by incorporating innovative mechanisms to combine and improve solutions and to create a balance between intensification and diversification in the reference set. Furthermore, heuristic information taken from a preprocessing of the images is incorporated into the algorithm to improve its performance. Our computational experimentation tackling two different medical registration applications establishes the effectiveness of scatter search in relation to different approaches usually applied to solving the problem. We have considered both simulated magnetic resonance images and real-world computerized tomography images as data sets. To measure the robustness of our proposal, the image data sets are intentionally selected for addressing registration environments with the presence of noise, anatomical lesions, and occlusions between images.

A todos tengo que agradecer las sugerencias y/o preguntas sobre SSm que me han ayudado a encontrar errores, a mejorarlo y a aprender un poco ms. Quiero resaltar a algunos por su relacin ms

Evolutionary feature selection on high dimensional data using a search space reduction approach

The effect of elite pool in hybrid population-based meta-heuristics for solving combinatorial optimization problems

The Course Timetabling problem is one of the most difficult combinatorial problems that arises with a University. The main objective of this problem is to obtain a timetable that minimises student conflicts between assigned activities. This is a discrete combinatorial problem that can be extremely difficult to solve for a human expert so computational heuristics are usually implemented in order to find good solutions within a reasonable time. With the advent of multi-core and hyper-threading technologies, parallel heuristics can speed up the solution process and with a proper parallel design these heuristics can improve the quality of solutions with the same number of Fitness evaluations than sequential algorithms. This paper explores the implementation of a parallel set of heuristic algorithms based on genetic algorithms, Scatter Search and discrete PSO for CTTP problem. Our experiments used as benchmark set instances from ITC2007 Track 2. Also the algorithms described in this paper make use of a layer of independence called methodology of design in order to be easily adaptable to new instances. Every parallel algorithm is compared against its sequential counterpart through speed-up metrics like Weak speed-up proposed by Alba et al.

This paper addresses the problem of parts scheduling in a cellular manufacturing system (CMS) by considering exceptional parts processed on machines located in multiple cells. To optimize the scheduling of parts as well as to minimize material handling between cells, the practice has to develop processing sequences for the parts in cells. A commonly chosen objective is to find part sequences within cells which results in a minimum tardiness. This paper proposes a nonlinear mathematical programming model of the problem by minimizing the total weighted tardiness in a CMS. To solve the mathematical model, a scatter search approach is developed, in which the common components of scatter search are redefined and redesigned so as to better fit the problem. This scatter search approach considers two different methods to generate diverse initial solutions and two improvement methods, and adopts the roulette wheel selection in the combination method to further expand the conceptual framework and implementation of the scatter search. The proposed approach is compared with the commercial solver CPLEX on a set of test problems, some of which are large dimensions. Computational results have demonstrated the effectiveness of this scatter search approach.