Showing 26 results for Heuristic
Ferda Can Çeti̇nkaya, Günce Boran Yozgat,
Volume 33, Issue 2 (6-2022)
Abstract
This paper considers a customer order scheduling (COS) problem in which each customer requests a variety of products processed in a two-machine flow shop. A sequence-independent attached setup for each machine is needed before processing each product lot. We assume that customer orders are satisfied by the job-based processing approach in which the same products from different customer orders form a product lot (job). Each customer order for a product is processed as a sublot (a batch of identical items) of the product lot by applying the lot streaming (LS) idea in scheduling. We assume that all sublots of the same product must be processed together by the same machine without intermingling the sublots of other products. The completion time of a customer order is the completion time of the product processed as the last product in that order. All products in a customer order are delivered in a single shipment to the customer when the processing of all the products in that customer order is completed. We aim to find an optimal schedule with a product lots sequence and the sequence of the sublots in each job to minimize the sum of completion times of the customer orders. We have developed a mixed-integer linear programming (MILP) model and a multi-phase heuristic algorithm for solving the problem. The results of our computational experiments show that our model can solve the small-sized problem instances optimally. However, our heuristic algorithm finds optimal or near-optimal solutions for the medium- and large-sized problem instances in a short time.
Islam Gomaa, Hegazy Zaher, Naglaa Ragaa Saeid, Heba Sayed ,
Volume 34, Issue 1 (3-2023)
Abstract
Researchers in many fields, such as operations research, computer science, AI engineering, and mathematical engineering, extra, are increasingly adopting nature-inspired metaheuristic algorithms because of their simplicity and flexibility. Natural metaheuristic algorithms are based on two essential terms: exploration (diversification) and exploitation (intensification). The success and limitations of these algorithms are reliant on the tuning and control of their parameters. When it comes to tackling real optimization problems, the Gorilla Troop Optimizer (GTO) is an extremely effective algorithm that is inspired by the social behavior of gorilla troops. Three operators of the original GTO algorithm are committed to exploration, and the other two operators are dedicated to exploitation. Even though the superiority of GTO algorithm to several metaheuristic algorithms, it needs to improve the balance between the exploration process and the exploitation process to ensure an accurate estimate of the global optimum. For this reason, a Novel Enhanced version of GTO (NEGTO), which focuses on the correct balance of exploration and exploitation, has been proposed. This paper suggests a novel modification on the original GTO to enhance the exploration process and exploitation process respectively, through introducing a dynamic controlling parameter and improving some equations in the original algorithm based on the new controlling parameter. A computational experiment is conducted on a set of well-known benchmark test functions used to show that NEGTO outperforms the standard GTO and other well-known algorithms in terms of efficiency, effectiveness, and stability. The proposed NEGTO for solving global optimization problems outperforms the original GTO in most unimodal benchmark test functions and most multimodal benchmark test functions, a wider search space and more intensification search of the global optimal solution are the main advantages of the proposed NEGTO.
Prasad Bari, Prasad Karande,
Volume 34, Issue 2 (6-2023)
Abstract
This paper presents a model for minimizing the makespan in the flow shop scheduling problem. Due to the impact of increased workloads, flow shops are becoming more popular and widely used in industries. To solve the challenge of minimizing makespan, a Hybrid-Heuristic-Metaheuristic-Genetic-Algorithm (HHMGA) is proposed. The proposed HHMGA algorithm is tested using the simulation software and demonstrated with steel industry data. The results are compared with those of the best available flow shop problem algorithms such as Palmer’s slope index, Campbell-Dudek-Smith (CDS), Nawaz-Enscore-Ham (NEH), genetic algorithm (GA) and particle swarm optimization (PSO). According to empirical results and relative differences from the lower bound, the proposed technique outperforms the three heuristics and two metaheuristics algorithms in three of six cases, while the remaining three produce the same results as the NEH heuristic. In comparison to the steel industry's regular job scheduling technique, the simulation model based on HHMGA can save 4642 hours. It was discovered that the suggested model enhanced the job sequence based on the makespan requirements.
Hamed Nozari, Maryam Rahmaty,
Volume 34, Issue 4 (12-2023)
Abstract
In this paper, the modeling of a make-to-order problem considering the order queue system under the robust fuzzy programming method is discussed. Considering the importance of timely delivery of ideal demand, a four-level model of suppliers, production centers, distribution centers, and customers has been designed to reduce total costs. Due to the uncertainty of transportation costs and ideal demand, the robust fuzzy programming method is used to control the model. The analysis of different sample problems with the League Championship Algorithm (LCA), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA) methods shows that with the increase in the uncertainty rate, the amount of ideal demand has increased, and this has led to an increase in total costs. On the other hand, with the increase of the stability coefficients of the model, contrary to the reduction of the shortage costs, the total costs of the model have increased due to transportation. Also, the analysis showed that with the increase in the number of servers in the production and distribution centers, the average waiting time for customers' order queues has decreased. By reducing the waiting time, the total delivery time of customer demand decreases, and the amount of actual demand increases. On the other hand, due to the lack of significant difference between the Objective Function Value (OBF) averages among the solution methods, they were prioritized, and SSA was recognized as an efficient algorithm. By implementing the model in a real case study in Iran for electronic components, it was observed that 4 areas of the Tehran metropolis (8-18-16-22) were selected as actual distribution centers. Also, the costs of the whole model were investigated in the case study and the results show the high efficiency of the solution methods in solving the make-to-order supply chain problem.
Malihe Masoumi, Javad Behnamian,
Volume 35, Issue 1 (3-2024)
Abstract
Due to the many applications of the travelling salesman problem, solving this problem has been considered by many researchers. One of the subsets of the travelling salesman problem is the metric travelling salesman problem in which a triangular inequality is observed. This is a crucial problem in combinatorial optimization as it is used as a standard problem as a basis for proving complexity or providing solutions to other problems in this class. The solution is used usually in logistics, manufacturing and other areas for cost minimization. Since this is an NP-hard problem, heuristic and meta-heuristic algorithms seek near-optimal solutions in polynomial time as numerical solutions. For this purpose, in this paper, a heuristic algorithm based on the minimum spanning tree is presented to solve this problem. Then, by generating 20 instances, the efficiency of the proposed algorithm was compared with one of the most famous algorithms for solving the travelling salesman problem, namely the nearest neighbour algorithm and the ant colony optimization algorithm. The results show that the proposed algorithm has good convergence to the optimal solution. In general, the proposed algorithm has a balance between runtime and the solution found compared to the other two algorithms. So the nearest neighbour algorithm has a very good runtime to reach the solution but did not have the necessary convergence to the optimal solution, and vice versa, the ant colony algorithm converges very well to the optimal solution, but, its runtime solution is very longer than the proposed algorithm.
Rabie Mosaad Rabie, Hegazy Zaher, Naglaa Ragaa Saied, Heba Sayed,
Volume 35, Issue 1 (3-2024)
Abstract
Harris Hawks Optimization (HHO) algorithm, which is a new metaheuristic algorithm that has shown promising results in comparison to other optimization methods. The surprise pounce is a cooperative behavior and chasing style exhibited by Harris' Hawks in nature. To address the limitations of HHO, specifically its susceptibility to local optima and lack of population diversity, a modified version called Modified Harris Hawks Optimization (MHHO) is proposed for solving global optimization problems. A mutation-selection approach is utilized in the proposed Modified Harris Hawks Optimization (MHHO) algorithm. Through systematic experiments conducted on 23 benchmark functions, the results have demonstrated that the MHHO algorithm offers a more reliable solution compared to other established algorithms. The MHHO algorithm exhibits superior performance to the basic HHO, as evidenced by its superior average values and standard deviations. Additionally, it achieves the smallest average values among other algorithms while also improving the convergence speed. The experiments demonstrate competitive results compared to other meta-heuristic algorithms, which provide evidence that MHHO outperforms others in terms of optimization performance.
