Scatter Search Approach Research Articles

The index selection problem with configurations and memory limitation: A scatter search approach

Within the physical designing process of relational databases, the Index Selection Problem aims at finding the subset of indexes to build for accessing the stored information. More precisely, given a database workload, each query must be served by at most one predefined set of indexes (named configuration) to maximize the net gain in terms of time. This gain is made up of the time gain obtained to serve the queries through the configurations minus the fixed time needed to create and maintain those configurations. The clustering of indexes into configurations and the limited amount of memory available to store the indexes characterize our variant of the problem. At the same time, established approaches in the literature have only considered those two aspects separately. We model this setting as a generalization of the Uncapacitated Facility Location Problem with budget constraint and propose an Integer Linear Programming formulation for it. Then, to find near-optimal solutions in a reasonable computational time, we develop a Scatter Search meta-heuristic exploiting the specific facility location features of the problem. We test our algorithm over a broad set of benchmark instances and compare it with an exact solver and an efficient state-of-the-art heuristic method.

Scatter search-based identification of local patterns with positive and negative correlations in gene expression data

This paper presents a scatter search approach based on linear correlations among genes to find biclusters, which include both shifting and scaling patterns and negatively correlated patterns contrarily to most of correlation-based algorithms published in the literature. The methodology established here for comparison is based on a priori biological information stored in the well-known repository Gene Ontology (GO). In particular, the three existing categories in GO, Biological Process, Cellular Components and Molecular Function, have been used. The performance of the proposed algorithm has been compared to other benchmark biclustering algorithms, specifically a group of classical biclustering algorithms and two algorithms that use correlation-based merit functions. The proposed algorithm outperforms the benchmark algorithms and finds patterns based on negative correlations. Although these patterns contain important relationship among genes, they are not found by most of biclustering algorithms. The experimental study also shows the importance of the size in a bicluster in addition to the value of its correlation. In particular, the size of a bicluster has an influence over its enrichment in a GO term.

Using Lagrangian Relaxation Decomposition With Heuristic to Integrate the Decisions of Cell Formation and Parts Scheduling Considering Intercell Moves

Cell formation and parts scheduling are two important correlated processes in a cellular manufacturing system; however, the decisions involved in these processes are typically made individually. Determining how to integrate these decisions effectively to pursue a productive and lower cost system has become an important issue. This paper focuses on providing an effective solution to integrate the decisions of cell formation and parts scheduling, while considering intercell moves by using a Lagrangian relaxation decomposition method. A mixed integer nonlinear programming mathematical model (CFPSP) is proposed to determine which part families and machine groups are assigned to cells and in which sequence the parts are processed in the machines to minimize the total tardiness penalty cost. To effectively solve the model, a Lagrangian relaxation decomposition method with a heuristic (LRDH) is developed. Using the LRDH, the CFPSP model is solved by decomposing the model into two subproblems, i.e., the cell formation subproblem (CFPSP-FD) and the parts scheduling subproblem (CFPSP-SD). After linearizing the CFPSP-FD model, the subproblem CFPSP-FD is solved by the MIP optimizer CPLEX. A scatter search approach is developed to solve the subproblem CFPSP-SD. Combined with the Lagrange multipliers, the CFPSP-SD model takes into consideration the assignment of part families and the associated machine groups to each cell, when it sequences the processing of the parts on each machine in cells. An illustration of the application of the CFPSP model in an electronic appliance cellular manufacturing enterprise in China is presented.

A multi-objective scatter search for the ladle scheduling problem

We consider the ladle scheduling problem, which can be regarded as a vehicle routing problem with semi-soft time windows and adjustment times. The problem concerns allocating ladles to serve molten steel based on a given steelmaking scheduling plan, and determining the modification operations for the empty ladles after the service process. In addition, combining the controllable processing time of molten steel, the other aspect of the problem is to determine the service start times taking into consideration the technological constraints imposed in practice. We present a non-linear mathematical programming model with the conflicting objectives of minimising the occupation ratio of the ladles and maximising the degree of satisfaction with meeting the soft windows. To solve the multi-objective model, we develop a new scatter search (SS) approach by re-designing the common components of SS and incorporating a diversification generator, a combination method and a diversification criterion to conduct a wide exploration of the search space. We analyse and compare the performance of the proposed approach with a multi-objective genetic algorithm and with manual scheduling adopted in practical production using three real-life instances from a well-known iron–steel production plant in China. The computational results demonstrate the effectiveness of the proposed SS approach for solving the ladle scheduling problem.

Advanced scatter search approach and its application in a sequencing problem of mixed-model assembly lines in a case company

Mixed-model assembly line sequencing is significant in reducing the production time and overall cost of production. To improve production efficiency, a mathematical model aiming simultaneously to minimize overtime, idle time and total set-up costs is developed. To obtain high-quality and stable solutions, an advanced scatter search approach is proposed. In the proposed algorithm, a new diversification generation method based on a genetic algorithm is presented to generate a set of potentially diverse and high-quality initial solutions. Many methods, including reference set update, subset generation, solution combination and improvement methods, are designed to maintain the diversification of populations and to obtain high-quality ideal solutions. The proposed model and algorithm are applied and validated in a case company. The results indicate that the proposed advanced scatter search approach is significant for mixed-model assembly line sequencing in this company.

Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch problem

The environmental/economic dispatch (EED) is an important daily optimization task in the operation of many power systems. It involves the simultaneous optimization of fuel cost and emission objectives which are conflicting ones. The EED problem can be formulated as a large-scale highly constrained nonlinear multiobjective optimization problem. In recent years, many metaheuristic optimization approaches have been reported in the literature to solve the multiobjective EED. In terms of metaheuristics, recently, scatter search approaches are receiving increasing attention, because of their potential to effectively explore a wide range of complex optimization problems. This paper proposes an improved scatter search (ISS) to deal with multiobjective EED problems based on concepts of Pareto dominance and crowding distance and a new scheme for the combination method. In this paper, we have considered the standard IEEE (Institute of Electrical and Electronics Engineers) 30-bus system with 6-generators and the results obtained by proposed ISS algorithm are compared with the other recently reported results in the literature. Simulation results demonstrate that the proposed ISS algorithm is a capable candidate in solving the multiobjective EED problems.

Economic Design for Expanding Local Area Networks Using a Scatter Search Approach

Economic Design for Expanding Local Area Networks Using a Scatter Search Approach

SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis

In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75%.

Cell formation and layout design with alternative routing: a multi-objective scatter search approach

Cellular manufacturing is a technology to improve the productivity and flexibility of manufacturing systems. Cell formation and layout design are two main problems in designing cellular manufacturing systems (CMSs). Due to the interaction between the two problems, these problems must be solved simultaneously to obtain the optimal solution. The problem is formulated as three different mathematical models which differ with respect to layout constraints and backward movements. A multi-objective scatter search is proposed to solve the integrated cell formation and layout design problem in CMSs. We introduce a new solution representation which is used in the algorithm. The performance of the algorithm is compared with the epsilon-constraint method and a well-known multi-objective evolutionary algorithm, non-dominated sorting genetic algorithm II (NSGA-II). The proposed algorithm not only obtained better solutions in comparison to the solutions proposed by past authors, but also it performs better than NSGA-II.

Heuristic methods for the fleet size and mix vehicle routing problem with time windows and split deliveries

This paper proposes a scatter-search (SS) approach to solve the Fleet Size and Mixed Vehicle Routing Problem with Time Windows and Split Deliveries (FSMVRPTWSD). In the Vehicle Routing Problem with Split Deliveries (VRPSD), each customer can be served by more than one vehicle, as opposed to the classical VRP in which each customer is served only once. In the FSMVRPTW, the customers must be serviced within their time windows with minimal costs using a heterogeneous fleet. Experimental testing and benchmark examples are used to assess the merit of our proposed procedure. The results show that the proposed heuristics are competitive with the best results found in the literature.

Design and analysis of hybrid metaheuristics for the Reliability p-Median Problem

In the p-Median Problem, it is assumed that, once the facilities are opened, they may not fail. In practice some of the facilities may become unavailable due to several factors. In the Reliability p-Median Problem some of the facilities may not be operative during certain periods. The objective now is to find facility locations that are both inexpensive and also reliable. We present different configurations of two hybrid metaheuristics to solve the problem, a genetic algorithm and a scatter search approach. We have carried out an extensive computational experiment to study the performance of the algorithms and compare its efficiency solving well-known benchmark instances.

Scatter search for the stochastic travel-time vehicle routing problem with simultaneous pick-ups and deliveries

In parallel with the growth of both domestic and international economies, there have been substantial efforts in making manufacturing and service industries more environmental friendly (i.e., promotion of environmental protection). Today manufacturers have become much more concerned with coordinating the operations of manufacturing (for new products) and recycling (for reuse of resources) together with scheduling the forward/reverse flows of goods over a supply chain network. The stochastic travel-time vehicle routing problem with simultaneous pick-ups and deliveries (STT-VRPSPD) is one of the major operations problems in bi-directional supply chain research. The STT-VRPSPD is a very challenging and difficult combinatorial optimization problem due to many reasons such as a non-monotonic increase or decrease of vehicle capacity and the stochasticity of travel times. In this paper, we develop a new scatter search (SS) approach for the STT-VRPSPD by incorporating a new chance-constrained programming method. A generic genetic algorithm (GA) approach for STT-VRPSPD is also developed and used as a reference for performance comparison. The Dethloff data will be used to evaluate the performance characteristics of both SS and GA approaches. The computational results suggest that the SS solutions are superior to the GA solutions.

Parameter determination and feature selection for C4.5 algorithm using scatter search approach

The C4.5 decision tree (DT) can be applied in various fields and discovers knowledge for human understanding. However, different problems typically require different parameter settings. Rule of thumb or trial-and-error methods are generally utilized to determine parameter settings. However, these methods may result in poor parameter settings and unsatisfactory results. On the other hand, although a dataset can contain numerous features, not all features are beneficial for classification in C4.5 algorithm. Therefore, a novel scatter search-based approach (SS + DT) is proposed to acquire optimal parameter settings and to select the beneficial subset of features that result in better classification results. To evaluate the efficiency of the proposed SS + DT approach, datasets in the UCI (University of California, Irvine) Machine Learning Repository are utilized to assess the performance of the proposed approach. Experimental results demonstrate that the parameter settings for the C4.5 algorithm obtained by the SS + DT approach are better than those obtained by other approaches. When feature selection is considered, classification accuracy rates on most datasets are increased. Therefore, the proposed approach can be utilized to identify effectively the best parameter settings for C4.5 algorithm and useful features.

Single machine scheduling for minimising earliness and tardiness penalties by scatter search approach

This paper proposes a new evolutionary technique named scatter search for scheduling a number of jobs on a single machine against a restrictive common due date. Individual earliness and tardiness penalties for the jobs are under consideration and the objective is to find an optimal schedule, which jointly minimises the sum of the earliness and tardiness costs. In this paper, the results are obtained for numerous benchmark problems generated by Biskup and Feldmann (2001) for common due date (CDD) problems. The best results of each of the benchmark problems of the three meta-heuristic techniques used by Feldmann and Biskup (2003) are selected and they are compared with the results of SS. This methodology provides substantially better results than the benchmark problems. At the same time, this method holds almost the same result as the best results (best-FB) of three meta-heuristic approaches of Feldmann and Biskup (2003).

A scatter search methodology for the nurse rostering problem

The benefits of automating the nurse scheduling process in hospitals include reducing the planning workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and to attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare. This paper presents a scatter search approach for the problem of automatically creating nurse rosters. Scatter search is an evolutionary algorithm, which has been successfully applied across a number of problem domains. To adapt and apply scatter search to nurse rostering, it was necessary to develop novel implementations of some of scatter search's subroutines. The algorithm was then tested on publicly available real-world benchmark instances and compared against previously published approaches. The results show the proposed algorithm is a robust and effective method on a wide variety of real-world instances.

A hybrid scatter search approach for resource-constrained project scheduling problem in PERT-type networks

This paper presents a metaheuristic algorithm for resource-constrained project scheduling problem (RCPSP) in PERT networks. A PERT-type project, where activities require resources of various types with random duration, is considered. The problem is to minimize the regular criterion namely project’s makespan. The resource project scheduling model is an NP-hard, therefore to obtain a precise solution, a metaheuristic algorithm is suggested namely hybrid scatter search (HSS). The path relinking algorithm and two operators like crossover and prominent permutation-based are applied to solve the problem. The problem has to be solved at each decision point, when at least more than one activity is ready to be operated but the available amount of resources is limited. The metaheuristic model is illustrated by a numerical example. In order to validate the performance of new hybrid metaheuristic algorithm, solutions are compared with “optimal solution” for small networks. Also the efficiency of the proposed algorithm, for real world problems , in terms of solution quality, is compared with well-reported benchmark test problems available on the PSPLIB. The computational results reveal that the proposed algorithm has appropriate results for small networks and real world problems.

Enhancing the classification accuracy by scatter-search-based ensemble approach

Data-mining algorithms have been used in many classification problems. Among them, the decision tree (DT), back-propagation network (BPN), and support vector machine (SVM) are popular and can be applied to various areas. Nevertheless, different problems may require different parameter values when applying DT, BPN or SVM. If parameter values are not set well, results may turn out to be unsatisfactory. Further, a dataset may contain many features; however, not all features are beneficial for classifications. Therefore, a scatter search (SS) approach is proposed to obtain the better parameters and select the beneficial subset of features to attain better classification results. The above classification algorithms have their respective advantages and disadvantages, and suitability is influenced by the characteristics of the problem. If the algorithms can function together in a so-called ensemble, it is expected that better results can be obtained. Therefore, this study adapts ensemble to further enhance the classification accuracy rate. In order to evaluate the performance of the proposed approach, datasets in UCI (University of California, Irvine) were applied as the test problem set. The corresponding results were compared to several well-known, published approaches. The comparative study shows that the proposed approach improved the classification accuracy rate in most datasets. Thus, the proposed approach can be useful to both practitioners and researchers.

Scatter search for a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries in Brazil

In this paper, we consider a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries that occurs in a major Brazilian retail group. A single depot attends 519 stores of the group distributed in 11 Brazilian states. To find good solutions to this problem, we propose heuristics as initial solutions and a scatter search (SS) approach. Next, the produced solutions are compared with the routes actually covered by the company. Our results show that the total distribution cost can be reduced significantly when such methods are used. Experimental testing with benchmark instances is used to assess the merit of our proposed procedure.

Scatter search technique for exam timetabling

At universities where students enjoy flexibility in selecting courses, the Registrar's office aims to generate an appropriate exam timetable for numerous courses and large number of students. An appropriate, real-world exam timetable should show fairness towards all students, respecting the following constraints: (a) eliminating or minimizing the number of simultaneous exams; (b) minimizing the number of consecutive exams; (c) minimizing the number of students with two or three exams per day (d) eliminating the possibility of more than three exams per day (e) exams should fit in rooms with predefined capacity; and (f) the number of exam periods is limited. These constraints are conflicting, which makes exam timetabling intractable. Hence, solving this problem in realistic time requires the use of heuristic approaches. In this work, we develop an evolutionary heuristic technique based on the scatter search approach for finding good suboptimal solutions for exam timetabling. This approach is based on maintaining and evolving a population of solutions. We evaluate our suggested technique on real-world university data and compare our results with the registrar's manual timetable in addition to the timetables of other heuristic optimization algorithms. The experimental results show that our adapted scatter search technique generates better timetables than those produced by the registrar, manually, and by other meta-heuristics.

A scatter search approach to sequence-dependent setup times job shop scheduling

Scheduling jobs on multiple machines is a difficult problem when real-world constraints such as the sequence setup time, setup times for jobs and multiple criteria are used for solution goodness. It is usually sufficient to obtain a near-optimal solution quickly when an optimal solution would require days or weeks of computation. Common scheduling heuristics such as Shortest Processing Time can be used to obtain a feasible schedule quickly, but are not designed for multiple simultaneous objectives. We use a new meta-heuristic known as a scatter search (SS) to solve these types of job shop scheduling problems. The results are compared with solutions obtained by common heuristics, a tabu search, simulated annealing, and a genetic algorithm. We show that by combining the mechanism of diversification and intensification, SS produces excellent results in a very reasonable computation time. The study presents an efficient alternative for companies with a complicated scheduling and production situation.