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Showing 5 results for Genetic Algorithm
Sheibani,
Volume 2, Issue 2 (6-2011)
Abstract
We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of standard benchmark problems indicate that the proposed hybrid meta-heuristic approach is very efficient.
Etebari, Aaghaie, Khoshalhan,
Volume 3, Issue 1 (4-2012)
Abstract
In recent years, enriching traditional revenue management models by considering the customer choice behavior has been a main challenge for researchers. The terminology for the airline application is used as representative of the problem. A popular and an efficient model considering these behaviors is choice-based deterministic linear programming (CDLP). This model assumes that each customer belongs to a segment, which is characterized by a consideration set, which is a subset of the products provided by the firm that a customer views as options. Initial models consider a market segmentation, in which each customer belongs to one specific segment. In this case, the segments are defined by disjoint consideration sets of products. Recent models consider the extension of the CDLP to the general case of overlapping segments. The main difficulty, from a computational standpoint, in this approach is solving the CDLP efficiently by column generation. Indeed, it turns out that the column generation subproblem is difficult on its own. It has been shown that for the case of nonoverlapping segments, this can be done in polynomial time. For the more general case of overlapping segments, the column generation sub-problem is NP-hard for which greedy heuristics are proposed for computing approximate solutions. Here, we present a new approach to solve this problem by using a genetic algorithm and compare it with the column generation method. We comparatively investigate the effect of using the new approach for firm’s revenue
Mrs. Mahnaz Naghshnilchi,
Volume 10, Issue 1 (7-2019)
Abstract
Capacitated vehicle routing problem (CVRP) is one of the most well-known and applicable issues in the field of transportation. It has been proved to be an NP-Complete problem. To this end, it is needed to develop a high-performance algorithm to solve the problem, particularly in large scales. This paper develops a novel mathematical model for the CVRP considering the satisfaction level of demand nodes. Then, the proposed model is validated using a numerical example and sensitivity analyses that are implemented by CPLEX solver/GAMS software. To solve the problem efficiently, a Genetic Algorithm (GA) is designed and implemented. The obtained results demonstrate that the proposed GA can yield high-quality solutions compared to exact solutions.
Dr Hamid Reza Yousefzadeh, Dr Davood Darvishi, Mrs Arezoo Sayadi Salar,
Volume 11, Issue 1 (9-2020)
Abstract
Ant colony optimization (ACOR) is a meta-heuristic algorithm for solving continuous optimization
problems (MOPs). In the last decades, some improved versions of ACOR have been proposed.
The UACOR is a unified version of ACOR that is designed for continuous domains. By adjusting
some specified components of the UACOR, some new versions of ACOR can be deduced. By doing
that, it becomes more practical for different types of MOPs. Based on the nature of meta-heuristic
algorithms, the performance of meta-heuristic algorithms are depends on the exploitation and
exploration, which are known as the two useful factors to generate solutions with different
qualities. Since all the meta-heuristic algorithms with random parameters use the probability
functions to generate the random numbers and as a result, there is no any control over the
amount of diversity; hence in this paper, by using the best parameters of UACOR and making
some other changes, we propose a new version of ACOR to increase the efficiency of UACOR.
These changes include using chaotic sequences to generate various random sequences and also
using a new local search to increase the quality of the solution. The proposed algorithm, the two
standard versions of UACOR and the genetic algorithm are tested on the CEC05 benchmark
functions, and then numerical results are reported. Furthermore, we apply these four algorithms
to solve the utilization of complex multi-reservoir systems, the three-reservoir system of Karkheh
dam, as a case study. The numerical results confirm the superiority of proposed algorithm over
the three other algorithms.
Mr Nosrat Al.. Mirzaei, Dr Reza Ehtesham Rasi, Dr Alireza Irajpour,
Volume 16, Issue 2 (8-2025)
Abstract
The current research provides a mixed integer nonlinear mathematical programming model for a company that operates with several stores and multiple products, in which demand for each customer is characterized using fuzzy logic by triangular numbers, while the replenishment policy of each store for any product is the popular economic order quantity (EOQ) model under backorder. The throughput, dispatch, and budget constraints are considered in the proposed EOQ model. The objective is to integrate a vendor selection problem and EOQ policy, in which a multi-sourcing strategy is considered. In the proposed strategy, the ordered value of each store for any product can be split between one or more vendors. As such, a set of selected vendors can replenish each store for each product. This research aims to answer the following question as follows: (i) which vendors are chosen; (ii) which store is allocated to the selected vendors for each product; (iii) what is the optimal value for the inventory decisions.
The aim is to reduce the total cost of the company, including costs related to the vendor selection decisions along with the inventory decisions. To solve the mathematical model, a novel and practical genetic algorithm (GA) is developed then the response surface methodology (RSM) is utilized to tune its parameters. At the end, some numerical instances under different categories are evaluated to explain the applicability of the proposed approach.
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