A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem

Yagiura, Mutsunori; Iwasaki, Shinji; Ibaraki, Toshihide; Glover, Fred

doi:10.1016/j.disopt.2004.03.005

Cited by 57 publications

(31 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tabu search was also used by Diaz and Fernandez [15]. The differential evolution algorithm [16], bees algorithm [17], and neighborhood search algorithm [18] are other notable heuristics approaches applied to GAPs.…”

Section: The Generalized Assignment Problemmentioning

confidence: 99%

A modified genetic algorithm for a special case of the generalized assignment problem

Dörterler¹,

Bay²,

Akçayol³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Many central examinations are performed nationwide in Turkey. These examinations are held simultaneously throughout Turkey. Examinees attempt to arrive at the examination centers at the same time and they encounter problems such as traffic congestion, especially in metropolises. The state of mind that this situation puts them into negatively affects the achievement and future goals of the test takers. Our solution to minimize the negative effects of this issue is to assign the test takers to closest examination centers taking into account the capacities of examination halls nearby. This solution is a special case of the generalized assignment problem (GAP). Since the scale of the problem is quite large, we have focused on heuristic methods. In this study, a modified genetic algorithm (GA) is used for the solution of the problem since the classical GA often generates infeasible solutions when it is applied to GAPs. A new method, named nucleotide exchange, is designed in place of the crossover method. The designed method is run between the genes of a single parent chromosome. In addition to the randomness, the consciousness factor is taken into consideration in the mutation process. With this new GA method, results are obtained successfully and quickly in large-sized data sets.

show abstract

Section: The Generalized Assignment Problemmentioning

confidence: 99%

A modified genetic algorithm for a special case of the generalized assignment problem

Dörterler¹,

Bay²,

Akçayol³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Over the last decade there has been much interest in such very large neighborhoods, especially to address difficult combinatorial optimization problems, like the multi-resource generalized assignment problem [8], the traveling salesman problem as in [9] or [10], the time-tabling problem [11] or the vehicle routing problem [12], as well as real-life applications as in the through-fleet-assignment problem [13] or a car sequencing application in [14]. Defining an exponential size neighborhood is not sufficient to guarantee an efficient local search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Split–merge: Using exponential neighborhood search for scheduling a batching machine

Cabo

Possani

Potts

et al. 2015

Computers & Operations Research

View full text Add to dashboard Cite

We address the problem of scheduling a single batching machine to minimize the maximum lateness with a constraint restricting the batch size. A solution for this NP-hard problem is defined by a selection of jobs for each batch and an ordering of those batches. As an alternative, we choose to represent a solution as a sequence of jobs. This approach is justified by our development of a dynamic program to find a schedule that minimizes the maximum lateness while preserving the underlying job order. Given this solution representation, we are able to define and evaluate various job-insert and job-swap neighborhood searches. Furthermore we introduce a new neighborhood, named split-merge, that allows multiple job inserts in a single move. The split-merge neighborhood is of exponential size, but can be searched in polynomial time by dynamic programming. Computational results with an iterated descent algorithm that employs the split-merge neighborhood show that it compares favorably with corresponding iterated descent algorithms based on the job-insert and job-swap neighborhoods.

show abstract

“…These include the traveling salesman problem [28,35,39], the quadratic assignment problem [7], vehicle routing problems [2,29], the capacitated minimum spanning tree problem [10], the generalized assignment problem [50,51], and parallel machine scheduling problems [3]. In several of these problems, the VLSN search algorithms give the strongest known computational results, making the development of such algorithms desirable in practice.…”

Section: Very Large-scale Neighborhood Searchmentioning

confidence: 99%

Very Large-Scale Neighborhood Search Techniques in Timetabling Problems

Meyers

Orlin

Practice and Theory of Automated Timetabling VI

View full text Add to dashboard Cite

Abstract. We describe the use of very large-scale neighborhood search (VLSN) techniques in examination timetabling problems. We detail three applications of VLSN algorithms that illustrate the versatility and potential of such algorithms in timetabling. The first of these uses cyclic exchange neighborhoods, in which an ordered subset of exams in disjoint time slots are swapped cyclically such that each exam moves to the time slot of the exam following it in the order. The neighborhood of all such cyclic exchanges may be searched effectively for an improving set of moves, making this technique computationally reasonable in practice. We next describe the idea of optimized crossover in genetic algorithms, where the parent solutions used in the genetic algorithm perform an optimization routine to produce the 'most fit' of their children under the crossover operation. This technique can be viewed as a form of multivariate large-scale neighborhood search, and it has been applied successfully in several areas outside timetabling. The final topic we discuss is functional annealing, which gives a method of incorporating neighborhood search techniques into simulated annealing algorithms. Under this technique, the objective function is perturbed slightly to avoid stopping at local optima. We conclude by encouraging the timetabling community to further examine the promising potential of these techniques in practice.

show abstract

A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem

Cited by 57 publications

References 38 publications

A modified genetic algorithm for a special case of the generalized assignment problem

A modified genetic algorithm for a special case of the generalized assignment problem

Split–merge: Using exponential neighborhood search for scheduling a batching machine

Very Large-Scale Neighborhood Search Techniques in Timetabling Problems

Contact Info

Product

Resources

About