Scatter Search for Truck Scheduling Problem with Product Loading/Unloading Constraints in a Crossdocking System

Cross-docking is a practice in logistics in which shipments are directly moved from an inbound truck into an outbound truck. A recognized problem in this domain is the assignment of trucks to doors in a way that the distance to be traveled between the doors is minimized. This problem is known as the Cross-Dock Door Assignment Problem (CDAP). A lot of research has been conducted regarding this topic still, up to our knowledge, none used Scatter Search (SS). In this paper, we implemented this evolutionary metaheuristic algorithm and tested it, then compared the results with those of another evolutionary algorithm, Genetic Algorithm (GA). The results indicate that the SS outperformed the GA.

A scatter search algorithm for stacking sequence optimisation of laminate composites

Multistage hybrid flow shop scheduling problems are considered in this paper. Each stage consists of several identical machines. The jobs are to be processed on any one of the machines at each stage. In most of the scheduling research works it is assumed that all jobs are to be processed at all stages. But in real-life industries some of the jobs may not be processed at some stages. Hence hybrid flow shop scheduling problems with missing operations are considered in this paper with the objective of determining a schedule that minimises makespan. The hybrid flow shop scheduling problems are non-deterministic polynomial time hard (NP-hard) problems. We propose a hybrid meta-heuristic algorithm, namely improved hybrid genetic scatter search (IHGSS) algorithm, based on genetic algorithm and scatter search algorithms. A case study problem of a leading steel furniture manufacturing company in India is presented to illustrate the proposed algorithm. Computational experiments show that the proposed IHGSS algorithm outperforms other heuristic and meta-heuristic algorithms.

The Scatter Search is one of the population-based metaheuristics, which can be used to solve a wide range of problems including Traveling Salesman Problem (TSP), Routing Protocol, and End-to-End QoS, while Bees Algorithm can be applied to optimize the solution of these problems. In this paper, an improved Scatter Search algorithm based on Bees Algorithm has been presented. The improvement provides Scatter Search with random exploration for search space of problem and more of intensification for promising solutions. The original and improved Scatter Search has been tested on Traveling Salesman Problem. A computational experiment with benchmark instances is reported. The results demonstrate that the improved Scatter Search algorithm produces better performance than original Scatter Search algorithm. The developed algorithm has been compared with other algorithms for the same problem, and the result was competitive with some algorithm and insufficient with another.

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.

This paper presents a new approach based on the Scatter Search (SS) algorithm applied to the Location Management problem using the Location Area (LA) scheme. The LA scheme is used to achieve the best configuration of the network partitioning, into groups of cells (location areas), that minimizes the costs involved. In this work we execute five distinct experiments with the aim of setting the best values for the Scatter Search parameters, using test networks generated with realistic data [1]. We also want to compare the results obtained by this new approach with those achieved through classical strategies, other algorithms from our previous work and also by other authors. The simulation results show that this SS based approach is very encouraging.

Optimization problems are very complex to be solved, as they can present multiple optimal solutions and, in certain cases, only a globally optimal solution. The mathematical formulation of these problems is composed of continuous and discrete variables with linear and nonlinear functions. The solution of these problems by mathematical modeling through solvers may not converge to a feasible solution or may demand much computational time, when many variables are considered. Thus, using metaheuristics to solve these problems is desirable according to the literature. The scatter search (SS) metaheuristic algorithm has been highlighted in the literature as an excellent combinatorial optimization algorithm for dealing with quality and diversity solutions. This work aims to present two methods using the SS algorithm to solve problems with continuous and discrete variables. Therefore, the SS algorithm is implemented to solve the traveling salesperson problem (TSP), which is a problem that contains only discrete variables, and also, another version of SS is implemented to solve the optimal power flow (OPF) problem, which has continuous and discrete variables. Two instances of the TSP and three instances of the OPF problem were used. The results show that the SS solves both problems efficiently and presents better results in some cases than the solutions of the same problems using classical mathematical models through solvers. It is concluded that implementing the SS algorithm does not require much effort and that good results can be achieved for solving optimization problems with variables of any nature.

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.

Material Recovery Facilities (MRFs) location and waste flow assignment decision problem were investigated. In a three stages network consisted of production factory, landfills and transfer stations, material recovery facilities, the decision problem is to choose a best set of material recovery facilities with capacity constraints and decide the optimal flow on the network to minimize the total operational cost. The problem is firstly formulated a mathematic model. Then, a scatter search algorithm with update strategy of reference set according to the level of the diversity and quality of solutions is developed to solve effectively the model. The computational experiments show that the scatter search algorithm can obtain the valid solution of the location problem and is better than the general scatter search without the special update strategy on solution quality.

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

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.

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.

This contribution introduces a new approach for the quality enhancement of the solutions reached by a stochastic feature subset selection procedure using a scatter search algorithm via a genetic-based algorithm feature subset selector. The scatter search approach makes use of correlation measures in the context of classification algorithms and in a natural way the genetic approach does also the exploration and exploitation of the search space through a correlation-based metric. It is said that data preparation helps to improve the effectivity of classification models as well as to reduce the feature space. Additionally, the feature subset selection application once is very common though doing the procedure twice is less frequent and is the scenario of this paper. The results are better than in the proposal with a single meta-heuristic and the reduction in the feature space is very appealing.

In this paper we study two different meta-heuristics to solve a real-word frequency assignment problem (FAP) in GSM networks. We have used a precise mathematical formulation in which the frequency plans are evaluated using accurate interference information coming from a real GSM network. We have developed an improved version of the scatter search (SS) algorithm in order to solve this problem. After accurately tuning this algorithm, it has been compared with a version fixed for the FAP problem of the population-based incremental learning (PBIL) algorithm. The results show that SS obtains better frequency plannings than PBIL for all the experiments performed.

In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high--quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Moreover, in the own algorithm the overlapping among biclusters is controlled adding a penalization term in the fitness function. Experimental results from yeast cell cycle are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.