Multiple Neighborhood Search Research Articles

Minimizing fuzzy makespan in a distributed assembly flow shop by using an efficient artificial bee colony algorithm

Assembly flow shop scheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades; however, the distributed AFSP with DPm → 1 layout considering uncertainty is seldom investigated. In this study, a distributed assembly flow shop scheduling problem with fuzzy makespan minimization (FDAFSP) is considered, and an efficient artificial bee colony algorithm (EABC) is proposed. In EABC, an adaptive population division method based on evolutionary quality of subpopulation is presented; a competitive employed bee phase and a novel onlooker bee phase are constructed, in which diversified combinations of global search and multiple neighborhood search are executed; the historical optimization data set and a new scout bee phase are adopted. The proposed EABC is verified on 50 instances from the literature and compared with some state-of-the-art algorithms. Computational results demonstrate that EABC performs better than the comparative algorithms on over 74% instances.

An improved multi-objective evolutionary algorithm for multiple-target asynchronous parallel selective disassembly sequence planning

The product’s service life has been shortened rapidly along with the development of modern technologies and human esthetic evolutions, resulting in a great number of end-of-life (EOL) products. Disassembling renewable parts from EOL products for remanufacturing is of great significance to save resources and protect the environment. The existing studies on selective disassembly sequence planning (SDSP) largely focus on the sequential disassembly planning, which is inefficient especially for complex products because it is a linear process where only one component can be removed at a time. Thus, this work focuses on parallel SDSP and further proposes a multiple-target asynchronous parallel selective DSP (APSDSP) with the objectives of minimizing disassembly time and maximizing disassembly profit simultaneously. In APSDSP, operators can remove multiple components simultaneously as long as disassembly constraints are not violated, and without synchronization requirement between operators. The feasible disassembly sequence and disassembly direction sequence are generated by a space interference matrix method (SIMM) to meet the actual disassembly environment. Based on SIMM, an improved multi-objective evolutionary algorithm based on multiple neighborhood search strategy is developed to create the pareto frontier of the problem. Finally, two cases study are presented to validate the effectiveness of the proposed methodology. It gives enterprises new insights to reduce disassembly time and improve disassembly profit.

An effective hybrid meta-heuristic for flexible flow shop scheduling with limited buffers and step-deteriorating jobs

This paper addresses a flexible flow shop scheduling problem considering limited buffers and step-deteriorating jobs, where there are multiple non-identical parallel machines. A mixed integer programming model is proposed, with the criterion of minimizing the makespan and total tardiness simultaneously. To handle this problem, an effective hybrid meta-heuristic algorithm, named GVNSA, is developed based on genetic algorithm (GA), variable neighborhood search (VNS) and simulated annealing (SA). In the algorithm, with a two-dimensional matrix encoding scheme, the NEH (Nawaz–Enscore–Ham) heuristic and bottleneck elimination method are implemented to determine the initial population. A three-level rolling translation approach is designed for decoding. To balance the exploration and exploitation abilities, three effective steps are executed: 1) partial matching crossover and mutation strategy based on multiple neighborhood search structures are imposed on the GA operators; 2) a VNS with SA is introduced to re-optimize some individuals from GA, where four neighborhood structures are constructed; 3) a modified CDS (Campbell–Dudek–Smith) heuristic is embedded to disturb population in the mid-iteration. Numerical experiments are carried out on test problems with different scales. Computational results demonstrate that the proposed GVNSA can obtain higher quality solutions in comparison with other heuristics and meta-heuristics existing in literature.

Multiobjective Multiple Neighborhood Search Algorithms for Multiobjective Fleet Size and Mix Location-Routing Problem With Time Windows

This paper introduces a multiobjective fleet size and mix location-routing problem with time windows and designs a set of real-world benchmark instances. Then, two versions of multiobjective multiple neighborhood search algorithms based on decomposition and vector angle are developed for solving the problem. In the proposed algorithms, three different kinds of neighborhood search operators, including general local search, objective-specific local search, and large neighborhood search, are carefully designed and combined in a synergistic manner. The experimental results show the effectiveness of the proposed algorithms. Relationships between different objectives in this multiobjective problem are also discussed.

Large multiple neighborhood search for the soft-clustered vehicle-routing problem

The soft-clustered vehicle-routing problem (SoftCluVRP) is a variant of the classical capacitated vehicle-routing problem. Customers are partitioned into clusters and all customers of the same cluster must be served by the same vehicle. In this paper, we present a large multiple neighborhood search for the SoftCluVRP. We design and analyze multiple cluster destroy and repair operators as well as two post-optimization components, which are both based on variable neighborhood descent. The first allows inter-route exchanges of complete clusters, while the second searches for intra-route improvements by combining classical neighborhoods (2-opt, Or-opt, double-bridge) and the Balas-Simonetti neighborhood. Computational experiments show that our algorithm clearly outperforms the only existing heuristic approach from the literature. By solving benchmark instances, we provide 130 new best solutions for 220 medium-sized instances with up to 483 customers and prove 12 of them to be optimal. Furthermore, we describe a straightforward adaption of our algorithm to the clustered vehicle-routing problem, a variant of the SoftCluVRP.

Dynamic shuffled frog-leaping algorithm for distributed hybrid flow shop scheduling with multiprocessor tasks

Distributed scheduling problems have attracted much attention in recent years; however, distributed hybrid flow shop scheduling problem (DHFSP) is seldom investigated. In this paper, DHFSP with multiprocessor tasks is studied and a dynamic shuffled frog-leaping algorithm (DSFLA) is proposed to minimize makespan. Dynamic search process is executed in each memeplex with at least two improved solutions. Global search and dynamic multiple neighborhood search are applied, in which neighborhood structure is chosen based on its optimization effect. A new destruction-construction process is hybridized with DSFLA and population shuffling is done when shuffling condition is met. Lower bound is obtained and proved. A number of experiments are conducted on a set of instances. The computational results validate the effectiveness of the new strategies of DSFLA and the competitive performances on solving the considered DHFSP.

Multi-objective artificial bee colony algorithm for multi-stage resource leveling problem in sharing logistics network

Multi-stage resource leveling problem in sharing logistics network is a multi-objective optimization problem, which is strongly non-deterministic polynomial hard in open loop environment. In this paper, we attempt to develop a logistics task-resource allocation model for this problem, which not only considers the total cost and duration for sharing logistics network, but also refers to the resource efficiency intra-stage and stability inter-stage for resource providers. As the defects of slow convergence, weak local search and easy-to-precocious in traditional algorithms, an improved multi-objective artificial bee colony algorithm is developed with adaptive neighborhood rules. The process of algorithm improvement involves: (I) an adaptive moving step size in population update strategy instead of random step size and (II) an adaptive weigh updating method with multiple neighborhood search rules in local optimum. The results show that the improved algorithm can effectively solve multi-stage resource leveling problem proposed in this paper, compared with traditional artificial bee colony algorithm, non-dominated sorting genetic algorithm-II and multi-objective particle swarm optimization, and can obtain a better non-dominated solution set with multiple metrics for algorithm.

An Imperialist Competitive Algorithm With the Diversified Operators for Many-Objective Scheduling in Flexible Job Shop

A number of related works have been done on multi-objective flexible job shop scheduling problem, however, many-objective flexible job shop scheduling problems (MaOFJSP) with at least four objectives are seldom considered. In this paper, an imperialist competitive algorithm with the diversified operators (DOICA) is proposed for MaOFJSP with the minimization of makespan, total tardiness, total workload, and total energy consumption. In DOICA, the diversified assimilation and revolution are used according to the features of empires, a novel imperialist competition is implemented by excluding the strongest empire and doing multiple neighborhood search of a solution in the strongest empire. The extensive experiments are conducted by using a number of benchmark instances to test the impact of strategies of DOICA on its performance and compare DOICA with other algorithms from literature finally. The computational results validate that the new strategies of DOICA are effective and DOICA can provide promising results for the considered MaOFJSP.

Large multiple neighborhood search for the clustered vehicle-routing problem

The clustered vehicle-routing problem is a variant of the classical capacitated vehicle-routing problem in which customers are partitioned into clusters, and it is assumed that each cluster must have been served completely before the next cluster is served. This decomposes the problem into three subproblems, i.e., the assignment of clusters to routes, the routing inside each cluster, and the sequencing of the clusters in the routes. The second task requires the solution of several Hamiltonian path problems, one for each possibility to route through the cluster. We pre-compute the Hamiltonian paths for every pair of customers of each cluster. We present a large multiple neighborhood search which makes use of multiple cluster destroy and repair operators and a variable-neighborhood descent (VND) for post-optimization. The VND is based on classical neighborhoods such as relocate, 2-opt, and swap all working on the cluster level and a generalization of the Balas–Simonetti neighborhood modifying simultaneously the intra-cluster routings and the sequence of clusters in a route. Computational results with our new approach compare favorably to existing approaches from the literature.

Multiple neighborhood search, tabu search and ejection chains for the multi-depot open vehicle routing problem

In this paper, we address the Multi-Depot Open Vehicle Routing Problem (MDOVRP), which is a generalization of the Capacitated Vehicle Routing Problem (CVRP) where vehicles start from different depots, visit customers, deliver goods and are not required to return to the depot at the end of their routes. The goal of this paper is twofold. First, we have developed a general Multiple Neighborhood Search hybridized with a Tabu Search (MNS-TS) strategy which is proved to be efficient and second, we have settled an unified view of ejection chains to be able to handle several neighborhoods in a simple manner. The neighborhoods in the proposed MNS-TS are generated from path moves and ejection chains. The numerical and statistical tests carried out over OVRP and MDOVRP problem instances from the literature show that MNS-TS outperforms the state-of-the-art methods.

Integrating a heterogeneous fixed fleet and a flexible assignment of destination depots in the waste collection VRP with intermediate facilities

We consider a complex recyclable waste collection problem that extends the class of vehicle routing problems with intermediate facilities by integrating a heterogeneous fixed fleet and a flexible assignment of destination depots. Several additional side constraints, such as a mandated break period contingent on tour start time, multiple vehicle capacities, and site dependencies are also included. This specific problem was inspired by a real-world application and does not appear in the literature. It is modeled as an MILP which is enhanced with several valid inequalities. Due to the rich nature of the problem, state-of-the-art solvers are only able to tackle instances of small to medium size. To solve realistic instances, we propose a multiple neighborhood search heuristic capable of systematically treating all problem features and general enough to respond to the varying characteristics of the case study regions for which it is intended. The results show that the heuristic achieves optimality on small instances, exhibits competitive performance in comparison to state-of-the-art solution methods for special cases of our problem, and leads to important savings in the state of practice. Moreover, it highlights and quantifies the savings from allowing a flexible depot assignment. The data from the state of practice comes from a company in the waste collection industry in Geneva, Switzerland.

A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents

Two-agent scheduling problem has attracted much attention; however, two-agent hybrid flow shop scheduling problem (TAHFSP) is seldom studied. In this study, TAHFSP and its minimality model are considered and a novel shuffled frog-leaping algorithm (SFLA) is proposed is to minimize the sum of the objectives of two agents. The following new principles are applied in SFLA: a tournament selection based method is used to divide population, not all solutions of population are allocated into memeplexes, the best solution of each memeplex acts as the object of the search process and the search process within memeplex consists of the global searches on two sub-problems sequentially and multiple neighborhood search. We test the performance of SFLA using a number of instances and the experimental results show the notable advantage of our SFLA when compared to other algorithms of TAHFSP.

A multiple neighborhood search for dynamic memory allocation in embedded systems

Memory allocation has a significant impact on power consumption in embedded systems. We address the dynamic memory allocation problem, in which memory requirements may change at each time interval. This problem has previously been addressed using integer linear programming and iterative approaches which build a solution interval by interval taking into account the requirements of partial time intervals. A GRASP that builds a solution for all time intervals has been proposed as a global approach. Due to the complexity of this problem, the GRASP algorithm solution quality decreases for larger instances. In order to overcome this drawback, we propose a multiple neighborhood search hybridized with a Tabu Search and enhanced by complex ejection chains. The proposed approach outperforms all previously developed methods devised for the dynamic memory allocation problem.

Track maintenance production team scheduling in railroad networks

US railroad companies spend billions of dollars every year on track maintenance in order to ensure safety and operational efficiency. Optimizing the production team (i.e., large maintenance team) schedule is a very complex problem with major cost implications. In current practice, the decision making process for production team scheduling is largely manual and primarily relies on the knowledge and judgment of experts. This paper addressed the production team scheduling problem by formulating it as a time–space network model with many types of challenging side constraints. Some of these constraints are identified from industry practice and formulated for the first time. Multiple neighborhood search and other enhancement algorithms were proposed to solve the model. The proposed modeling approach has been tested through numerical experiments and also applied to large-scale real-world problem instances, and superior computational performances were found. The proposed approach has been adopted by a Class I railroad to help make annual network maintenance scheduling decisions.

A hybrid particle swarm optimization algorithm for the vehicle routing problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for successfully solving one of the most popular supply chain management problems, the vehicle routing problem. The vehicle routing problem is considered one of the most well studied problems in operations research. The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search–greedy randomized adaptive search procedure (MPNS–GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is suitable for solving very large-scale vehicle routing problems as well as other, more difficult combinatorial optimization problems, within short computational time. It is tested on a set of benchmark instances and produced very satisfactory results. The algorithm is ranked in the fifth place among the 39 most known and effective algorithms in the literature and in the first place among all nature inspired methods that have ever been used for this set of instances.

Honey Bees Mating Optimization algorithm for large scale vehicle routing problems

Honey Bees Mating Optimization algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm with the Multiple Phase Neighborhood Search---Greedy Randomized Adaptive Search Procedure (MPNS---GRASP) and the Expanding Neighborhood Search (ENS) algorithm. Besides these two procedures, the proposed algorithm has, also, two additional main innovative features compared to other Honey Bees Mating Optimization algorithms concerning the crossover operator and the workers. Two sets of benchmark instances are used in order to test the proposed algorithm. The results obtained for both sets are very satisfactory. More specifically, in the fourteen classic instances proposed by Christofides, the average quality is 0.029% and in the second set with the twenty large scale vehicle routing problems the average quality is 0.40%.

A Particle Swarm Optimization Algorithm with Path Relinking for the Location Routing Problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for solving successfully one of the most popular logistics management problems, the location routing problem (LRP). The proposed algorithm for the solution of the location routing problem, the hybrid particle swarm optimization (HybPSO-LRP), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search – greedy randomized adaptive search procedure (MPNS-GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is tested on a set of benchmark instances. The results of the algorithm are very satisfactory for these instances and for six of them a new best solution has been found.

Multiple phase neighborhood Search—GRASP based on Lagrangean relaxation, random backtracking Lin–Kernighan and path relinking for the TSP

In this paper, a new modified version of Greedy Randomized Adaptive Search Procedure (GRASP), called Multiple Phase Neighborhood Search—GRASP (MPNS-GRASP), is proposed for the solution of the Traveling Salesman Problem. In this method, some procedures have been included to the classical GRASP algorithm in order to improve its performance and to cope with the major disadvantage of GRASP which is that it does not have a stopping criterion that will prevent the algorithm from spending time in iterations that give minor, if any, improvement in the solution. Thus, in MPNS-GRASP a stopping criterion based on Lagrangean Relaxation and Subgradient Optimization is proposed. Also, a different way for expanding the neighborhood search is used based on a new strategy, the Circle Restricted Local Search Moves strategy. A new variant of the Lin-Kernighan algorithm, called Random Backtracking Lin-Kernighan that helps the algorithm to diversify the search in non-promising regions of the search space is used in the Expanding Neighborhood Search phase of the algorithm. Finally, a Path Relinking Strategy is used in order to explore trajectories between elite solutions. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results.