Showing 29 results for Genetic Algorithm
M. Kargari, Z. Rezaee, H. Khademi Zare ,
Volume 18, Issue 3 (11-2007)
Abstract
Abstract : In this paper a meta-heuristic approach has been presented to solve lot-size determination problems in a complex multi-stage production planning problems with production capacity constraint. This type of problems has multiple products with sequential production processes which are manufactured in different periods to meet customer’s demand. By determining the decision variables, machinery production capacity and customer’s demand, an integer linear program with the objective function of minimization of total costs of set-up, inventory and production is achieved. In the first step, the original problem is decomposed to several sub-problems using a heuristic approach based on the limited resource Lagrange multiplier. Thus, each sub-problem can be solved using one of the easier methods. In the second step, through combining the genetic algorithm with one of the neighborhood search techniques, a new approach has been developed for the sub-problems. In the third step, to obtain a better result, resource leveling is performed for the smaller problems using a heuristic algorithm. Using this method, each product’s lot-size is determined through several steps. This paper’s propositions have been studied and verified through considerable empirical experiments.
K. Shahanaghi, V.r. Ghezavati,
Volume 19, Issue 4 (12-2008)
Abstract
In this paper, we present the stochastic version of Maximal Covering Location Problem which optimizes both location and allocation decisions, concurrently. It’s assumed that traveling time between customers and distribution centers (DCs) is uncertain and described by normal distribution function and if this time is less than coverage time, the customer can be allocated to DC. In classical models, traveling time between customers and facilities is assumed to be in a deterministic way and a customer is assumed to be covered completely if located within the critical coverage of the facility and not covered at all outside of the critical coverage. Indeed, solutions obtained are so sensitive to the determined traveling time. Therefore, we consider covering or not covering for customers in a probabilistic way and not certain which yields more flexibility and practicability for results and model. Considering this assumption, we maximize the total expected demand which is covered. To solve such a stochastic nonlinear model efficiently, simulation and genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed algorithm.
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Volume 20, Issue 1 (5-2009)
Abstract
The problem of lot sizing, sequencing and scheduling multiple products in flow line production systems has been studied by several authors. Almost all of the researches in this area assumed that setup times and costs are sequence –independent even though sequence dependent setups are common in practice. In this paper we present a new mixed integer non linear program (MINLP) and a heuristic method to solve the problem in sequence dependent case. Furthermore, a genetic algorithm has been developed which applies this constructive heuristic to generate initial population. These two proposed solution methods are compared on randomly generated problems. Computational results show a clear superiority of our proposed GA for majority of the test problems.
M. Yaghini , J. Lessan , H. Gholami Mazinan ,
Volume 21, Issue 1 (6-2010)
Abstract
Arash Motaghedi-Larijani, Kamyar Sabri-Laghaie , Mahdi Heydari,
Volume 21, Issue 4 (12-2010)
Abstract
In this paper flexible job-shop scheduling problem (FJSP) is studied in the case of optimizing different contradictory objectives consisting of: (1) minimizing makespan, (2) minimizing total workload, and (3) minimizing workload of the most loaded machine. As the problem belongs to the class of NP-Hard problems, a new hybrid genetic algorithm is proposed to obtain a large set of Pareto-optimal solutions in a reasonable run time. The algorithm utilizes from a local search heuristic for improving the chance of obtaining more number of global Pareto-optimal solutions. The solution method uses from a perturbed global criterion function for guiding the search direction of the hybrid algorithm. Computational experiences show that the hybrid algorithm has superior performance in contrast to previous studies .
Mohammad Bagher Fakhrzad, Mitra Moobed ,
Volume 21, Issue 4 (12-2010)
Abstract
Managing products’ end-of-life and recovery of used products is gaining significant importance during last years. Therefore, managing the reverse flow of products can be an important potential for winning consumers in future competitive markets. In this context, establishing reverse logistics networks is becoming a main problem in reverse supply chains. Genetic Algorithm (GA) is utilized to solve the proposed NP-hard problem and find the best possible design for different facilities. In order to test the applicability of proposed GA, we suppose a tire reverse logistic case and solve the problem. The results show that the least cost will be achieved by using the free space of distribution centers and integrating collection and inspection centers within them. In addition, we suggest using hybrid algorithm in future allocation problems to obtain best solutions .
Hossein Sadeghi, Mahdi Zolfaghari , Mohamad Heydarizade,
Volume 22, Issue 1 (3-2011)
Abstract
This paper aimed at estimation of the per capita consumption of electricity in residential sector based on economic indicators in Iran. The Genetic Algorithm Electricity Demand Model (GAEDM) was developed based on the past data using the genetic algorithm approach (GAA). The economic indicators used during the model development include: gross domestic product (GDP) in terms of per capita and real price of electricity and natural gas in residential sector. Three forms of GAEDM were developed to estimate the electricity demand. The developed models were validated with actual data, and the best estimated model was selected on base of evaluation criteria. The results showed that the exponential form had more precision to estimate the electricity demand than two other models. Finally, the future estimation of electricity demand was projected between 2009 and 2025 by three forms of the equations linear, quadratic and exponential under different scenarios .
M. Mohammadi, R. Tavakkoli-Moghaddam, A. Ghodratnama , H. Rostami ,
Volume 22, Issue 3 (9-2011)
Abstract
Hub covering location problem, Network design, Single machine scheduling, Genetic algorithm, Shuffled frog leaping algorithm |
Hub location problems (HLP) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc.) through special facilities or transshipment points called hubs. In this paper, we consider a central mine and a number of hubs (e.g., factories) connected to a number of nodes (e.g., shops or customers) in a network. First, the hub network is designed, then, a raw materials transportation from a central mine to the hubs (i.e., factories) is scheduled. In this case, we consider only one transportation system regarded as single machine scheduling. Furthermore, we use this hub network to solve the scheduling model. In this paper, we consider the capacitated single allocation hub covering location problem (CSAHCLP) and then present the mixed-integer programming (MIP) model. Due to the computational complexity of the resulted models, we also propose two improved meta-heuristic algorithms, namely a genetic algorithm and a shuffled frog leaping algorithm in order to find a near-optimal solution of the given problem. The performance of the solutions found by the foregoing proposed algorithms is compared with exact solutions of the mathematical programming model .
Mahdi Karbasian, Saeed Abedi,
Volume 23, Issue 1 (3-2012)
Abstract
One of the main principles of the passive defense is the principle of site selection. In this paper, we propose a multiple objective nonlinear programming model that considers the principle of the site selection in terms of two qualitative and quantitative aspects. The purpose of the proposed model is selection of the place of key production facilities of a system in which not only it observes the dispersion principle but also reduces the system transportation costs. Moreover, the proposed model tries to select the sites that can fulfill other elements of site selection as well as dispersion in a way that it increases the trustworthiness of the selected network. For solving the proposed model we used the Genetic Algorithm integrated with TOPSIS method.
Hossein Akbaripour, Ellips Masehian,
Volume 24, Issue 2 (6-2013)
Abstract
The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA.
Jafar Bagherinejad, Maryam Omidbakhsh,
Volume 24, Issue 3 (9-2013)
Abstract
Location-allocation of facilities in service systems is an essential factor of their performance. One of the considerable situations which less addressed in the relevant literature is to balance service among customers in addition to minimize location-allocation costs. This is an important issue, especially in the public sector. Reviewing the recent researches in this field shows that most of them allocated demand customer to the closest facility. While, using probability rules to predict customer behavior when they select the desired facility is more appropriate. In this research, equitable facility location problem based on the gravity rule was investigated. The objective function has been defined as a combination of balancing and cost minimization, keeping in mind some system constraints. To estimate demand volume among facilities, utility function(attraction function) added to model as one constraint. The research problem is modeled as one mixed integer linear programming. Due to the model complexity, two heuristic and genetic algorithms have been developed and compared by exact solutions of small dimension problems. The results of numerical examples show the heuristic approach effectiveness with good-quality solutions in reasonable run time.
Mahdi Bashiri, Masoud Bagheri,
Volume 24, Issue 3 (9-2013)
Abstract
The quality of manufactured products is characterized by many controllable quality factors. These factors should be optimized to reach high quality products. In this paper we try to find the controllable factors levels with minimum deviation from the target and with a least variation. To solve the problem a simple aggregation function is used to aggregate the multiple responses functions then an imperialist competitive algorithm is used to find the best level of each controllable variable. Moreover the problem has been better analyzed by Pareto optimal solution to release the aggregation function. Then the proposed multiple response imperialist competitive algorithm (MRICA) has been compared with Multiple objective Genetic Algorithm. The experimental results show efficiency of the proposed approach in both aggregation and non aggregation methods in optimization of the nonlinear multi-response programming.
Rashed Sahraeian,
Volume 25, Issue 1 (2-2014)
Abstract
In this paper the problem of serial batch scheduling in a two-stage hybrid flow shop environment with minimizing Makesapn is studied. In serial batching it is assumed that jobs in a batch are processed serially, and their completion time is defined to be equal to the finishing time of the last job in the batch. The analysis and implementation of the prohibited transference of jobs among the machines of stage one in serial batch is the main contribution of this study. Machine set-up and ready time for all jobs are assumed to be zero and no Preemption is allowed. Machines may not breakdown but at times they may be idle. As the problem is NP-hard, a genetic algorithm is developed to give near optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed GA. Many test problems have been solved using GA and results compared with lower bound. Results showed GA can obtain a near optimal solution for small, median and large size problems in reasonable time.
Parviz Fattahi, Seyed Mohammad Hassan Hosseini, Fariborz Jolai, Azam Dokht Safi Samghabadi,
Volume 25, Issue 1 (2-2014)
Abstract
A three stage production system is considered in this paper. There are two stages to fabricate and ready the parts and an assembly stage to assembly the parts and complete the products in this system. Suppose that a number of products of different kinds are ordered. Each product is assembled with a set of several parts. At first the parts are produced in the first stage with parallel machines and then they are controlled and ready in the second stage and finally the parts are assembled in an assembly stage to produce the products. Two objective functions are considered that are: (1) to minimizing the completion time of all products (makespan), and (2) minimizing the sum of earliness and tardiness of all products (∑_i▒(E_i∕T_i ) . Since this type of problem is NP-hard, a new multi-objective algorithm is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with two prominent multi-objective genetic algorithms, i.e. NSGA-II and SPEA-II. The computational results show that performance of the proposed algorithms is good in both efficiency and effectiveness criterions.
Seyed Mojtaba Jafari Henjani, Valeriy Severin,
Volume 25, Issue 3 (7-2014)
Abstract
The paper is devoted to solution of some problems in nuclear power station generating unit intellectual control systems using genetic algorithms on the basis of control system model development, optimizations methods of their direct quality indices and improved integral quadratic estimates. Some mathematical vector models were obtained for control system multicriterion quality indices with due consideration of stability and quality indices criteria, this increasing the reliability of optimal control system synthesis. Optimal control systems with fuzzy controllers were synthesized for nuclear reactor, steam generator and steam turbine, thus allowing comparison between fuzzy controllers and traditional PID controllers. Mathematical models built for nuclear power station generating unit control systems, including nuclear reactor, steam generator, steam turbine and their control systems interacting under normal operational modes, which permitted to perform parametrical synthesis of system and to study various power unit control laws. On the basis of power unit control system models controllers were synthesized for normal operational modes.
Mahdi Bashiri, Mahdyeh Shiri, Mohammad Hasan Bakhtiarifar,
Volume 26, Issue 2 (7-2015)
Abstract
There are many real problems in which multiple responses should be optimized simultaneously by setting of process variables. One of the common approaches for optimization of multi-response problems is desirability function. In most real cases, there is a correlation structure between responses so ignoring the correlation may lead to mistake results. Hence, in this paper a robust approach based on desirability function is extended to optimize multiple correlated responses. Main contribution of the current study is the synthesis of ideas considering correlation structure in robust optimization through defining joint confidence interval and desirability function method. A genetic algorithm was employed to solve the introduced problem. Effectiveness of the proposed method is illustrated through some computational examples and some comparisons with previous methods were performed to show applicability of the proposed approach. Also, a sensitivity analysis was provided to show relationship of correlation and robustness in these approaches.
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Dr. Yahia Zare Mehrjerdi, Amir Ebrahimi Zade, Dr. Hassan Hosseininasab,
Volume 26, Issue 3 (9-2015)
Abstract
Abstract One of the basic assumptions in hub covering problems is considering the covering radius as an exogenous parameter which cannot be controlled by the decision maker. Practically and in many real world cases with a negligible increase in costs, to increase the covering radii, it is possible to save the costs of establishing additional hub nodes. Change in problem parameters during the planning horizon is one of the key factors causing the results of theoretical models to be impractical in real world situations. To dissolve this problem in this paper a mathematical model for dynamic single allocation hub covering problem is proposed in which the covering radius of hub nodes is one of the decision variables. Also Due to NP-Hardness of the problem and huge computational time required to solve the problem optimally an effective genetic algorithm with dynamic operators is proposed afterwards. Computational results show the satisfying performance of the proposed genetic algorithm in achieving satisfactory results in a reasonable time. Keywords: hub location problem, dynamic hub covering problem, flexible covering radius, dynamic genetic algorithm.
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Esmaeil Mehdizadeh, Amir Fatehi-Kivi,
Volume 28, Issue 1 (3-2017)
Abstract
In this paper, we propose a vibration damping optimization algorithm to solve a fuzzy mathematical model for the single-item capacitated lot-sizing problem. At first, a fuzzy mathematical model for the single-item capacitated lot-sizing problem is presented. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP) which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity and NP-hardness of the problem, a vibration damping optimization (VDO) is used to solve the model for large-scale problems. To verify the performance of the proposed algorithm, we computationally compared the results obtained by the VDO algorithm with the results of the branch-and-bound method and two other well-known meta-heuristic algorithms namely simulated annealing (SA) and genetic algorithm (GA). Additionally, Taguchi method is used to calibrate the parameters of the meta-heuristic algorithms. Computational results on a set of randomly generated instances show that the VDO algorithm compared with the other algorithms can obtain appropriate solutions.
Reza Babazadeh, Reza Tavakkoli-Moghaddam,
Volume 28, Issue 2 (6-2017)
Abstract
A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND problem as a strategic level decision-making problem in supply chain management is an NP-hard class of computational complexity. To escape infeasible solutions emerged in the problem of interest due to realistic constraints, combination of a random key and priority-base encoding scheme is also used. To assess the quality of the proposed hybrid GA-TLBO algorithm, some numerical examples are conducted. Then, the results are compared with the GA, TLBO, differential evolution (DE) and branch-and -bound algorithms. Finally, the conclusion is provided.
Ali Mohtashami, Alireza Alinezhad,
Volume 28, Issue 3 (9-2017)
Abstract
In this article, a multi objective model is presented to select and allocate the order to suppliers in uncertainty condition and in a multi source, multi customer and multiproduct case in a multi period state at two levels of supply chain. Objective functions considered in this study as the measures to evaluate suppliers are cost including purchase, transportation and ordering costs, timely delivering, shipment quality or wastages which are amongst major quality aspects, partial and general coverage of suppliers in respect of distance and finally suppliers weights making the products orders amount more realistic. The major limitations are price discount for products by suppliers which are calculated using signal function. In addition, suppliers weights in the fifth objective function is calculated using fuzzy Topsis technique. Lateness and wastes parameters in this model are considered as uncertain and random triangular fuzzy number. Finally the multi objective model is solved using two multi objective algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Particle Swarm Optimization (PSO) and the results are analyzed using quantitative criteria Taguchi technique was used to regulate the parameters of two algorithms.
