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

Abstract

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.

A New Genetic Algorithm with Agent-Based Crossover for Generalized Assignment Problem

“…Dorterler et al [8] proposed a new GA for a real problem seen as a special case of the GAP. The authors emphasised that classical crossover method violated the capacity constraint.…”

“…Table 3 shows the quality of solutions and a comparison of the results. T between best solution value (Si) and their known best-cost value (So) (8) of solutions:…”

A Simulated Annealing Algorithm for the Multi Resource Generalized Assignment Problem with Eligibility Constraint

“…Az sayıda çalışma çok amaçlı yapıdadır ( [20]- [23]). GAP literatürü çözüm yöntemleri açısından incelendiğinde, çözüm yöntemlerinin kesin çözüm yöntemleri ( [12], [13], [18]- [20], [24], [25]) ve sezgisel/metasezgisel yöntemler ([1], [3]- [6], [8]- [10], [15]- [17], [19], [21]- [23], [25]- [29]) olarak gruplanabileceği görülmektedir.…”

PSO-based group-oriented crow search algorithm (PGCSA)

“…This purpose is achieved by introducing new approaches to modify the algorithm or to hybridize two algorithms. Hybrid big bang big crunch with local search (Genc et al , 2013), hierarchical approximate Bayesian computation–migrating birds (Makas and Yumuşak, 2016), chaotic PSO (Javidi and Emami, 2016) and modified GA (Dörterler et al , 2017) are some of the approaches used by researchers to improve the proficiency of algorithms. To improve the balance, some researchers have introduced scads of modifications to algorithms implemented successfully in power systems (Nayak et al , 2018a, 2018b).…”