Local search neighbourhoods for dealing with a novel nurse rostering model

A novel nurse rostering model is developed to represent real world problem instances more accurately. The proposed model is generic in the sense that it allows modelling of essentially different problem instances. Novel local search neighbourhoods are implemented to take advantage of the problem properties represented by the model. These neighbourhoods are used in a variable neighbourhood search and in an adaptive large neighbourhood search algorithm. The performance of the solution method is evaluated empirically on real world data. The proposed model is open to further extensions for covering personnel planning problems in different sectors and countries.     

Local search heuristics for multi-index assignment problems with decomposable costs

The multi-index assignment problem (MIAP) with decomposable costs is a natural generalization of the well-known assignment problem. Applications of the MIAP arise, for instance, in the field of multi-target multi-sensor tracking. We describe an (exponentially sized) neighbourhood for a solution of the MIAP with decomposable costs, and show that one can find a best solution in this neighbourhood in polynomial time. Based on this neighbourhood, we propose a local search algorithm. We empirically test the performance of published constructive heuristics and the local search algorithm on random instances; a straightforward iterated local search algorithm is also tested. Finally, we compute lower bounds to our problem, which enable us to assess the quality of the solutions found.     

Variable neighbourhood structures for cycle location problems

Variable neighbourhood search is a metaheuristic used mainly to tackle combinatorial optimization problems. Its performance depends on having a good variable neighbourhood structure: that is, a sequence of neighbourhoods that are ideally pairwise disjoint and contain feasible solutions further and further from a given feasible solution. This article defines a variable neighbourhood structure with these properties that is new for cycle location problems. It find bounds for the neighbourhood sizes and shows how to iterate over then when the cycle is a circuit. It tests the structure and iteration method using variable neighbourhood search on a range of median cycle problems and finds a neighbourhood size beyond which there is, on average, no benefit in applying local search. This neighbourhood size is found not to depend on problem size or bound on circuit length.     

An incremental tabu search heuristic for the generalized vehicle routing problem with time windows

This paper describes an incremental neighbourhood tabu search heuristic for the generalized vehicle routing problem with time windows. The purpose of this work is to offer a general tool that can be successfully applied to a wide variety of specific problems. The algorithm builds upon a previously developed tabu search heuristic by replacing its neighbourhood structure. The new neighbourhood is exponential in size, but the proposed evaluation procedure has polynomial complexity. Computational results are presented and demonstrate the effectiveness of the approach.     

A comparison of local search methods for flow shop scheduling

Local search techniques are widely used to obtain approximate solutions to a variety of combinatorial optimization problems. Two important categories of local search methods are neighbourhood search and genetic algorithms. Commonly used neighbourhood search methods include descent, threshold accepting, simulated annealing and tabu search. In this paper, we present a computational study that compares these four neighbourhood search methods, a genetic algorithm, and a hybrid method in which descent is incorporated into the genetic algorithm. The performance of these six local search methods is evaluated on the problem of scheduling jobs in a permutation flow shop to minimize the total weighted completion time. Based on the results of extensive computational tests, simulated annealing is found to generate better quality solutions than the other neighborhood search methods. However, the results also indicate that the hybrid genetic descent algorithm is superior to simulated annealing.     

Evaluating the performance of tabu search procedures for flow shop sequencing

We address the problem of minimising makespan in a flow shop by using tabu search procedures. By combining different neighbourhood structures, neighbourhood examinations, and stopping conditions we obtain alternative tabu search procedures. Starting from a common initial solution we evaluate the relative performance of such procedures considering both the solution quality and computational effort.     

VLSN search algorithms for partitioning problems using matching neighbourhoods

In this paper, we propose a general paradigm to design very large-scale neighbourhood search algorithms for generic partitioning-type problems. We identify neighbourhoods of exponential size, called matching neighbourhoods, comprised of the union of a class of exponential neighbourhoods. It is shown that these individual components of the matching neighbourhood can be searched in polynomial time, whereas searching the matching neighbourhood is NP-hard. Matching neighbourhood subsumes a well-known class of exponential neighbourhoods called cyclic-exchange neighbourhoods. Our VLSN algorithm is implemented for two special cases of the partitioning problem; the covering assignment problem and the single source transportation problem. Encouraging experimental results are also reported.     

A tabu-based large neighbourhood search methodology for the capacitated examination timetabling problem

Neighbourhood search algorithms are often the most effective known approaches for solving partitioning problems. In this paper, we consider the capacitated examination timetabling problem as a partitioning problem and present an examination timetabling methodology that is based upon the large neighbourhood search algorithm that was originally developed by Ahuja and Orlin. It is based on searching a very large neighbourhood of solutions using graph theoretical algorithms implemented on a so-called improvement graph. In this paper, we present a tabu-based large neighbourhood search, in which the improvement moves are kept in a tabu list for a certain number of iterations. We have drawn upon Ahuja–Orlin's methodology incorporated with tabu lists and have developed an effective examination timetabling solution scheme which we evaluated on capacitated problem benchmark data sets from the literature. The capacitated problem includes the consideration of room capacities and, as such, represents an issue that is of particular importance in real-world situations. We compare our approach against other methodologies that have appeared in the literature over recent years. Our computational experiments indicate that the approach we describe produces the best known results on a number of these benchmark problems.     

An adaptive TS approach to JIT sequencing with variable processing times and sequence-dependent setups

This paper addresses a single machine sequencing problem with variable processing times and sequence-dependent setups. The objective is to find the best trade-off between the JIT goal and the processing time compression and extension costs by simultaneously determining the job sequence and processing times for concerned jobs. Due to the combinatorial nature of the problem, it cannot be optimally solved in polynomial time. A tabu search approach is used to provide good and quick solutions. To improve the computational efficiency, an adaptive neighbourhood generation method is proposed and used in the tabu search algorithm. A total of 100 problems of different sizes have been solved to test the proposed approach. Our computational experience shows that the adaptive approach outperforms several other neighbourhood generation methods in terms of both convergence rate and solution quality. The effects of the search parameters are also discussed.     

Local search and constraint programming for the post enrolment-based course timetabling problem

We present a variety of approaches for solving the post enrolment-based course timetabling problem, which was proposed as Track 2 of the 2007 International Timetabling Competition. We approach the problem using local search and constraint programming techniques. We show how to take advantage of a list-colouring relaxation of the problem. Our local search approach won Track 2 of the 2007 competition. Our best constraint programming approach uses an original problem decomposition. Incorporating this into a large neighbourhood search scheme seems promising, and provides motivation for studying complete approaches in further detail.     

Local search for the Steiner tree problem in the Euclidean plane

Most heuristics for the Steiner tree problem in the Euclidean plane perform a series of iterative improvements using the minimum spanning tree as an initial solution. We may therefore characterize them as local search heuristics. In this paper, we first give a survey of existing heuristic approaches from a local search perspective, by setting up solution spaces and neighbourhood structures. Secondly, we present a new general local search approach which is based on a list of full Steiner trees constructed in a preprocessing phase. This list defines a solution space on which three neighbourhood structures are proposed and evaluated. Computational results show that this new approach is very competitive from a cost–benefit point of view. Furthermore, it has the advantage of being easy to apply to the Steiner tree problem in other metric spaces and to obstacle avoiding variants.     

A General Vehicle Routing Problem

In this paper, we study a rich vehicle routing problem incorporating various complexities found in real-life applications. The General Vehicle Routing Problem (GVRP) is a combined load acceptance and generalised vehicle routing problem. Among the real-life requirements are time window restrictions, a heterogeneous vehicle fleet with different travel times, travel costs and capacity, multi-dimensional capacity constraints, order/vehicle compatibility constraints, orders with multiple pickup, delivery and service locations, different start and end locations for vehicles, and route restrictions for vehicles. The GVRP is highly constrained and the search space is likely to contain many solutions such that it is impossible to go from one solution to another using a single neighbourhood structure. Therefore, we propose iterative improvement approaches based on the idea of changing the neighbourhood structure during the search.     

The maximum return-on-investment plant location problem with market share

This paper examines the plant location problem under the objective of maximizing return-on-investment. However, in place of the standard assumption that all demands must be satisfied, we impose a minimum acceptable level on market share. The model presented takes the form of a linear fractional mixed integer program. Based on properties of the model, a local search procedure is developed to solve the problem heuristically. Variable neighbourhood search and tabu search heuristics are also developed and tested. Thus, a useful extension of the simple plant location problem is examined, and heuristics are developed for the first time to solve realistic instances of this problem.     

A metaheuristic approach to solving a multiproduct EOQ-based inventory problem with storage space constraints

This paper considers the well-known static time-continuous multiproduct economic order quantity (EOQ) based inventory management problem with the storage space constraints. This problem is modelled as a combinatorial optimization problem in the corresponding dynamic discrete time system control process. In order to solve this problem approximately, we developed two heuristics: a special heuristic based on a local search technique and a metaheuristic procedure based on the variable neighbourhood search principle. The efficiency of two heuristics is preliminary examined and compared on several randomly generated instances with the same number of products.     

An integrated tabu search algorithm for the lot streaming problem in job shops

In this paper, we focus on solving the lot streaming problem in a job shop environment, where consistent sublots are considered. The presented three-phase algorithm incorporates the predetermination of sublot sizes, the determination of schedules based on tabu search and the variation of sublot sizes. With regard to tabu search implementation, a constructive multi-level neighbourhood is developed, which effectively connects three isolated neighbourhood functions. Moreover, enhancements of the basic version of tabu search are conducted. Combined with the procedure for varying sublot sizes, the algorithm further exploits the improvement potential. All tested instances show a rapid convergence to their lower bounds. The well-known difficult benchmark problems also achieve substantial makespan reduction. In addition, the performance of specific components is intensively examined in our study.     

New block properties for the permutation flow shop problem with application in tabu search

This paper deals with the classic flow-shop scheduling problem with the make-span criterion. Some new properties of the problem associated with the so-called blocks have been presented and discussed. The properties allow us to skip some non-perspective solutions during the search of the solution space. Applied to local search algorithms, they result in a significant reduction of neighbourhood size and quickly direct the search trajectory to promising regions of the solution space. The implementation of the proposed properties in a tabu search algorithm is also presented. Computational experiments (up to 500 jobs and 20 machines) are given and compared with the results yielded by the best known algorithms.     

Using metaheuristic compromise programming for the solution of multiple-objective scheduling problems

In this paper, we propose a generic approach to find compromise solutions for multiple-objective scheduling problems using metaheuristics. As an illustration, we present a new hybrid tabu search/variable neighbourhood search application of this approach for the solution of a bi-objective scheduling problem. Through numerical experiments we demonstrate its efficiency and effectiveness. We have confirmed that compromise programming with the tabu-VNS metaheuristic generates solutions that approach those of the known reference sets.     

The double travelling salesman problem with multiple stacks – Formulation and heuristic solution approaches

This paper introduces the double travelling salesman problem with multiple stacks and presents four different metaheuristic approaches to its solution. The double TSP with multiple stacks is concerned with determining the shortest route performing pickups and deliveries in two separated networks (one for pickups and one for deliveries) using only one container. Repacking is not allowed, instead each item can be positioned in one of several rows in the container, such that each row can be considered a LIFO (last in, first out) stack, but no mutual constraints exist between the rows. Two different neighbourhood structures are developed for the problem and used with each of three local search metaheuristics. Additionally some simpler removal and reinsertion operators are used in a Large neighbourhood search framework. Finally some computational results are given along with lower bounds on the objective value.     

An effective VNS for the capacitated p-median problem

In the capacitated p-median problem (CPMP), a set of n customers is to be partitioned into p disjoint clusters, such that the total dissimilarity within each cluster is minimized subject to constraints on maximum cluster capacity. Dissimilarity of a cluster is the sum of the dissimilarities between each customer who belongs to the cluster and the median associated with the cluster. An effective variable neighbourhood search heuristic for this problem is proposed. The heuristic is characterized by the use of easily computed lower bounds to assess whether undertaking computationally expensive calculation of the worth of moves, within the neighbourhood search, is necessary. The small proportion of moves that need to be assessed fully are then evaluated by an exact solution of a relatively small subproblem. Computational results on five standard sets of benchmark problem instances show that the heuristic finds all the best-known solutions. For one instance, the previously best-known solution is improved, if only marginally.     

Efficient solution of large scale,single-source,capacitated plant location problems

The single-source, capacitated plant location problem is considered. This problem differs from the capacitated plant location problem by the additional requirement that each customer must be supplied with all its demand from a single plant. An efficient heuristic solution, capable of solving large problem instances, is presented. The heuristic combines Lagrangian relaxation with restricted neighbourhood search. Computational experiments on two sets of problem instances are presented.