scholarly journals Local search intensified: Very large-scale variable neighborhood search for the multi-resource generalized assignment problem

2009 ◽  
Vol 6 (4) ◽  
pp. 370-377 ◽  
Author(s):  
Snežana Mitrović-Minić ◽  
Abraham P. Punnen
Keyword(s):  
Local Search ◽  
Assignment Problem ◽  
Large Scale ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Generalized Assignment Problem ◽  
Generalized Assignment ◽  
Scale Variable

scholarly journals A multi-start iterated tabu search algorithm for the multi-resource agent bottleneck generalized assignment problem

2019 ◽  
Vol 10 (1) ◽  
pp. 37-46
Author(s):  
Gülçin Bektur
Keyword(s):  
Tabu Search ◽  
Assignment Problem ◽  
Large Scale ◽  
Search Algorithm ◽  
Maximum Load ◽  
Mixed Integer ◽  
Generalized Assignment Problem ◽  
Generalized Assignment ◽  
Iterated Tabu Search ◽  
Resource Agent

In this study, a multi-resource agent bottleneck generalized assignment problem (MRBGAP) is addressed. In the bottleneck generalized assignment problem (BGAP), more than one job can be assigned to an agent, and the objective function is to minimize the maximum load over all agents. In this problem, multiple resources are considered and the capacity of the agents is dependent on these resources and it has minimum two indices. In addition, agent qualifications are taken into account. In other words, not every job can be assignable to every agent. The problem is defined by considering the problem of assigning jobs to employees in a firm. BGAP has been shown to be NP- hard. Consequently, a multi-start iterated tabu search (MITS) algorithm has been proposed for the solution of large-scale problems. The results of the proposed algorithm are compared by the results of the tabu search (TS) algorithm and mixed integer linear programming (MILP) model.

scholarly journals Solving balanced multi-weighted attribute set partitioning problem with variable neighborhood search

Filomat ◽  
2019 ◽  
Vol 33 (9) ◽  
pp. 2875-2891
Author(s):  
Dusan Dzamic ◽  
Bojana Cendic ◽  
Miroslav Maric ◽  
Aleksandar Djenic
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Variable Neighborhood Search ◽  
Heuristic Method ◽  
Set Partitioning ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Computationally Efficient ◽  
Local Search Procedure ◽  
Problem Instances

This paper considers the Balanced Multi-Weighted Attribute Set Partitioning (BMWASP) problem which requires finding a partition of a given set of objects with multiple weighted attributes into a certain number of groups so that each attribute is evenly distributed amongst the groups. Our approach is to define an appropriate criterion allowing to compare the degree of deviation from the ?perfect balance? for different partitions and then produce the partition that minimizes this criterion. We have proposed a mathematical model for the BMWASP and its mixed-integer linear reformulation. We evaluated its efficiency through a set of computational experiments. To solve instances of larger problem dimensions, we have developed a heuristic method based on a Variable Neighborhood Search (VNS). A local search procedure with efficient fast swap-based local search is implemented in the proposed VNS-based approach. Presented computational results show that the proposed VNS is computationally efficient and quickly reaches all optimal solutions for smaller dimension instances obtained by exact solver and provide high-quality solutions on large-scale problem instances in short CPU times.

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