جستجوی مقالات مرتبط با کلیدواژه « imperialist competitive algorithm » در نشریات گروه « مدیریت »

Today, due to some challenges and competition, such as external pressures, factories are forced to reduce production time, traditional centralized production scheduling is not flexible enough to respond to rapid market changes. In such an environment, factories decide to merge and form a multi-factory production network to work more closely together.  In this research, the multi-factory scheduling problem is considered, which factories belong to a company. The problem is assigning the jobs to appropriate factory and scheduling jobs on machines in each factory. In this paper, it is assumed machines in each factory are unrelated parallel machines. For scheduling jobs on machines sequence-dependent setup times are considered. After proposing a novel mixed integer linear programming model for the problem which is a combination of two types of modeling based on sequence and assignment, we developed an evolutionary metaheuristic namely imperialist competitive algorithm (ICA) to minimize the maximum completion time or makespan among the factories. We compare the obtained solutions using the proposed ICA with those using an adopted genetic algorithm to show the efficiency of the proposed algorithm. Finally, the results are reported. Numerical results show that the proposed algorithm has good performance.

In this research, an integrated scheduling problem of job shop systems with an assembly stage and transportation to minimize the total tardiness time is studied. In this problem, the parts are processed in a job shop system and then assembled in the assembly stage. Ultimately, the products are shipped in packages to customers. Setup time is assumed to depend on sequence. At first, a mixed-integer linear model is developed. Since the problem is NP-hard, a hybrid imperialist competitive and simulated annealing (ICA-SA) algorithm is proposed to solve the problems with the medium and large sizes. To validate the performance of the proposed algorithm, results are compared to an imperialist competitive algorithm and a hybrid imperialist competitive and tabu search (ICA-TS) algorithm. Analysis of variance random block design is used to compare the results of the algorithms. P-values of algorithms and blocks in this test are smaller than the significance level of 0.05. The computational results show that the proposed hybrid algorithm achieves better performance than the imperialist competitive algorithm and hybrid imperialist competitive and tabu search.

Health care solid waste includes all types of waste that are produced as a result of medical and therapeutic activities in hospitals and health centers. About 15% to 20% of these waste materials are infectious waste, which falls within the category of hazardous materials. Infectious waste is the one that must be treated before disposal or recycling. Hence, this paper seeks to develop a bi-objective mixed integer programming model for the management of infectious waste. In the proposed model, in addition to minimizing the chain costs, the reduction of risks for the population exposed to the spread of contamination resulting from infectious waste is also desired. For this purpose, a multi-echelon chain is proposed by taking into account the green location-routing problem, which involves the location of recycling, disposal, and treatment centers through various treatment technologies and routing of vehicles between treatment levels and the hospital. The routing problem has been considered to be multi-depot wherein the criterion of reducing the cost of fuel consumption of heterogeneous cars is used for green routing. Finally, a hybrid meta-heuristic algorithm based on ICA and GA is developed and, following its validation, its function in solving large-scale problems has been investigated. The results indicate that the proposed algorithm is effective and efficient.

Neglecting the supply chain management of perishable goods could create a lot of costs for organizations and companies. Blood is a perishable product in the healthcare supply chain, the  shortage of which could prove quite problematic and disastrous. Any improvements in the blood supply chain management operations may increase service efficiency and decrease the cost of the healthcare system, saving the lives of lots of people. In this paper, a mixed-integer nonlinear programming model is proposed for comprehensive blood supply chain management, which includes gathering, processing and distributing blood and blood products by taking into account the demand lifetime and age. This model aims at decreasing supply chain costs and blood product deficiency. Robust optimization is utilized to take into account the inherent uncertainty and volatility of the demand and supply. The proposed model is first tested on a small-scale numerical example in GAMS software. Then a large-scale problem is solved using Whale and Imperialist Competition algorithms and the results are compared. In addition, a case study is presented to show the applicability of the proposed model.

vStock market has been one of the most influential economic phenomena in the world for many years. The main players in the stock market are investors that are always looking to make the most profit. Since prices of stock market transactions is Impressionable from political, economic, social problems and the high volatility of prices, the prediction of stock market is very difficult.  The main solution for more profits in the market is making the right decisions about buying and selling appropriate stocks in appropriate time. Therefore, prediction is the most important requirements for traders. I this research, a new hybrid algorithm is proposed that uses imperialist competitive algorithm as a feature selection method and fuzzy adaptive neural inference system as a prediction function. This approach uses 63 features that affect the stock market, including economic features, Iran and other countries stock market indexes, technical analysis indexes and Japanese Candlestick on a daily basis in the period from 2010-2016. The Exchange Stock Index for the next day is considered as the target variable. The results show that the hybrid model includes Adaptive Neural Fuzzy Inference System (ANFIS) and Imperialist Competitive Algorithm, is much appropriate. This model is compared with a single ANFIS model has better approximation speed and the ability to predict the sto

In this study, a location-allocation problem is proposed regarding capacity factor in critical situation of an earthquake. Output is the selection of the best places for temporary shelters and optimized arrangement of the casualties in those places somehow minimizing casualties and damages. In the following, efficient allocation of the casualties to the medical centers will be discussed. Reaching these goals, a mathematics model proportionate to the problem conditions and constraints is presented. In literatures, location-allocation problem has been classified as a NP-Hard Problem. For these problems, metaheuristic algorithm were proposed. In this research, Imperialist Compeitive Algorithm (ICA) and Genetic Algorithm (GA) are used and the results comprised with each other. Based on the results of research, in such cases, ICA can be an opponent for Genetic Algorithm, because of the average of the solution obtained by this algorithm is rather better than Genetic Algorithm. However the GA convergence is faster than ICA. Case study is performed on region 3 of Tehran. Using available information of this region, the most fitted places for sheltering are extracted from GIS science and ARC GIS software. ICA is implemented to solve the problem. At the end, the number of optimized shelters and arrangement of inhabitants in these places and also arrangement of casualties to available medical centers in the region are presented.

E-Banking is a new type of banking service where banking services are provided using -environments. This kind of banking activity has spread throughout the world since 1991. Customers are migrating from traditional banking to modern banking with the increase of e-banking services and the provision of various tools for banking transactions. Accordingly, it is important to examine the progress of banks in these tools and the level of customer satisfaction. This study first analyzes the extent of customer satisfaction with these tools by examining customer transactions in Branch, ATM, Internet Bank and Mobile Bank Which has conducted this survey for a hundred thousand customers. After an initial analysis of the use of tools, Customers were clustered using k-Mean Method and Imperialist Competitive Algorithm with MATLAB software and grouped into seven clusters. Then the characteristics of each cluster are examined and strategies for each cluster are presented. In the final stage, we analyzed the strategies proposed by the Shapely value-based game theory approach and the most important strategies proposed.

Packing is one of the well-known problems in operation research, especially in production planning. The main objective of studying the packing problem is to reduce the wastes of cutting through optimization of packing of pieces. Packing is a kind of NP-hard problem that the precise methods are not able to solve it. In this paper, in order to achieve an optimal packing of Non-guillotine cutting problems, the meta-heuristic emerging Imperialist Competitive Algorithm was used and the results were compared with the output of the genetic algorithm, which is the typical algorithm in solving packing problems. To achieve better solutions, the parameters of all meta-heuristics were calibrated with Taguchi experiment method. The efficacy of the proposed approach was tested on a set of instances, taken from the literature, and the results of the proposed algorithm were tested statistically by ANOVA. The results of this study showed that the meta-heuristic emerging Imperialist Competitive algorithm is more efficient and faster in solving packing problems.

This paper addresses a new mathematical model of economic lot sizing and scheduling problem in distributed permutation flow shop problem with a number of non-identical factories and machines that have not been considered in previous articles. For this purpose, different products must be distributed between the factories then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs, finished products inventory costs per unit of time and total production cost that consists of cost of unbalanced assignment of products between factories. Since the proposed model is NP-hard, an efficient hybrid Vibration Damping Optimization with Imperialist Competitive Algorithm and Simulated Annealing are considered to solve the model. In addition, Genetic Algorithm and VDO are used for comparison. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed. The results show that the HVDO has a better performance in achieving the minimum goal in this problem.

In modern societies, production centers should be able to supply the variety of demand and type of productions correctly, exactly, clearly and straightly. So, this study aims to present the dynamic facility layout problem (DFLP) in order to minimize the total cost of the facilities and costumes. Design an optimal plan leads to reduce the production process and cost of inventory. Transportation cost plays a key-role to specify the performance designing of part systems. This content displays the Dynamic Facility Location Problem (DFLP) in this paper. In order to handle the proposed problem, five metaheuristic methods are introduced: Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) are tackled to solve the problem. Taguchi approach is considered to select the proper values for presented algorithms. The results explain that proposed methods are provided the optimal design for the dynamic facility location problem. As a result, ICA and PSO reach the most performance output in the comparison of other algorithms with assumed conditions in the problem.

The two-stage assembly flow shop with setup times is one of the new production scheduling problems. In this problem, the parts are produced in the first stage and then they are assembled in the second stage. The objective is to sequence parts in the first stage to minimize the makespan. First, a mathematical model in form of a mixed integer linear programming model is developed. Then, to solve the problem, two metaheuristics, imperialist competitive and genetic algorithms, are proposed. The performance of the model and the two algorithms are evaluated by numerical experiments. The result show that the imperialist algorithm works more effectively.

In this research, optimization of examination's timetable for university courses, based on a real problem in one of the universities in Iran is studied. The objective function defined for this problem is more practical and realistic than the other objective functions that have been utilized by previous researchers in literature and effectively reflects the real objective of the problem. In order to define the objective function, we have made use of Coulomb's law in electricity that says the magnitude of the electrostatic force of interaction between two point charges is directly proportional to the scalar multiplication of the magnitudes of the charges and inversely proportional to the square of the distance between them. We have defined a repulsive force between any pair of Examinations. The optimum solution is achieved when the sum of all forces is minimized. Hence, the obtained mathematical model is a non-linear programming with binary variables, similar to the quadratic assignment problem (QAP) which is an NP-Hard problem. This sort of problems can be solved exactly only if they are in small sizes. For solving this problem in medium and large scale, some methods are used based on Simulated Annealing (SA) algorithm and Imperialist Competitive algorithm (ICA). These algorithms can reach good sub-optimal solutions in a short period of time. Practical results of this mathematical model are already used in one of the national universities in Iran. The practical results demonstrate the high efficiency and effectiveness of this model.

In recent years, rough set theory has been considered as a strong solution to solve artificial intelligence problem such as data mining. But, the classic rough set theory is not effective in the case of attribute reduction in incomplete information systems. Since there are null values for some of attributes in a data set, an incomplete information system is created. In this paper, a novel method proposed to solve attribute reduction in incomplete information system based on rough set theory by combining and modifying imperialist competitive algorithm with fuzzy logic. Utilizing the fuzzy logic to control the parameters of the algorithm was useful and generated better solutions compared to its classic draft. In this research, no changes imposed on incomplete data, and it was just considered as a complete systems. The fuzzy imperialist competitive algorithm acted intelligently to reduce the number of attribute in incomplete information system, providing appropriate results that is worthy of attention.

In this paper, hybrid flow shop scheduling problem with parallel machines in each work station is examined. This problem considered two works: no-wait restriction jobs and typical jobs. In no-wait restriction jobs, the operations must be processed with no stop. This paper mathematically formulates the problem with two different mixed integer linear models under proposed considerations. The small instances of the problem are solved and performance results are analyzsed using commercial software of mathematical programming. To solve larger instances of the problem, two solution algorithms have been developed. These two algorithms are based on imperialist competitive algorithm and simulated annealing. A comprehensive numerical experiment is conducted to evaluate algorithms. The results show that the imperialist competitive algorithm outperforms simulated annealing.

In today's world of global markets industry, Companies cannot survive without considering competitors' moves and progress because they are part of a supply chain and the success or failure of any member of the chain affect the other members. In this paper, the two echelon supply chain with multiple products and a producer as well as a distributor and several customer cases has been investigated.  In the first part of the chain, just one type of vehicle was applied and the second parts of the chain two types of vehicle were used. The proposed model for this study is an integrated mathematical model of mixed integer programming. This is considered to minimize overall costs which incluses shipping cost, maintenance cost, inventory cost and the penality cost for lack of inventory. This case study concentrates on the sent rolls (produced by Mobarakeh Steel Company) to Structure Gostar Saipa Co. (S.G.S) and then after to automotive parts manufacturer. The "Imperialist Competitive Algorithm solved in 20 different sizes was applied, and its results (in small size) were compared with the software GAMS results.

In this paper we develop the concept of boring caused by doing the same jobs to two types of boring, negative or undesirable and positive or desirable, which are felt by operators because of doing similar jobs and not only due to doing the same ones. Based on this new concept, the flexible model has been proposed by which jobs will be scheduled to minimize the total cost of assignment including the cost of doing the jobs by operators and the boring cost so that job scheduled with respect to their similarities in the smallest time period as well as dissimilarities in the biggest given time period. For the reason that the proposed job rotation scheduling model has a multi-­period assignment structure and formulated as an integer non-linear model, it is recognized as a combinatorial optimization problem. So applying the metaheuristic algorithms to overcome the complexity of such a problem is required. We use the genetic and imperialist competitive algorithms to do that and verify their efficiency in comparison to that of Lingo software which solves the small integer nonlinear problems. It is also shown that the quality of imperialist competitive algorithm solutions is better than those of genetic algorithm for the proposed model.