جستجوی مقالات مرتبط با کلیدواژه
تکرار جستجوی کلیدواژه imperialist competitive algorithm در نشریات گروه فنی و مهندسی
imperialist competitive algorithm
در نشریات گروه صنایع-
Nowadays, designing a reliable network for blood supply chains by which most blood demands can be supplied is an important problem in the health care systems. In this paper, a multi-objective model is provided to create a sustainable blood supply chain, which contains multiple donors, collection centers, distribution centers, and hospitals at different echelons. Regarding the potential of a blood shortage occurring, the suggested model considers the supply chain's capacity to meet hospitals' blood demands as dependable and a means of achieving the societal purpose. In addition, limiting the overall cost and environmental effect of designing a supply network and blood transportation are considered economical and environmental objectives. To solve the proposed multi-objective model, an improved ε-constraint approach is first employed to construct a single-objective model. Additionally, an imperialist competitive algorithm is developed to solve the single-objective model. Several test cases are analysed to determine the technique's effectiveness. CPLEX is then used to compare the results.Keywords: Supply chain, Sustainability, Reliability, blood supply chain, Environment, Imperialist Competitive Algorithm
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Journal of Quality Engineering and Production Optimization, Volume:8 Issue: 1, Winter-Spring 2023, PP 33 -56Flexible job-shop scheduling problem (F-JSP) is an expansion of the job shop scheduling problem (JSP) which allows an operation to be fulfilled by any machine among a set of accessible machines at each stage. This paper investigates a no-wait F-JSP (NW-F-JSP) with machines accessibility restrictions for maintenance activities and machines processing capability to minimize total weighted tardiness. The study is organized in two phases. Firstly, a novel nonlinear mathematical model is developed for the supposed problem, and then it is converted into a linear mathematical model using techniques found in the literature. Since the structure of the problem is NP-hard, an imperialist competitive algorithm is proposed in the second phase to solve large instances of the problem. In the proposed algorithm, an effective solution representation with an efficient and greedy decoding methodology is adopted to reduce the search space. Numerical experiments are used to appraise the performance of the developed algorithm. It is inferred that in small instances, solving the mathematical model by GAMS leads to the optimal solution. Still, with an increased instance size, this method loses its efficiency and the ICA approach performs better under these conditions.Keywords: Flexible Job Shop, No-Wait, Maintenance Activities, Imperialist Competitive Algorithm
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نشریه نوآوری های صنعتی، پیاپی 1 (بهار 1402)، صص 83 -101در دنیای صنعتی امروز، واحدهای تولیدی سعی دارند با مکان یابی مناسب انبارهای مورد نیاز خود و همچنین مسیریابی وسایل نقلیه به منظور حمل کالاهای تولیدی به این انبارها، هزینه های خود را کاهش دهند. لازم به ذکر است که، مکان انبارها درتعیین مسیر وسایل نقلیه موثر است. بنابراین در این مقاله، یک مدل برنامه ریزی ریاضی جهت بهینه سازی هم زمان تعیین مکان انبارها و مسیریابی وسایل نقلیه ارایه شده-است. تابع هدف در این مدل شامل مینیمم کردن مجموع هزینه های مرتبط با وسایل حمل و نقل و هزینه ی اجاره انبارها می باشد. محدودیت های مدل ارایه شده شامل ظرفیت وسایل نقلیه، حداکثر میزان مسافت طی شده توسط وسایل نقلیه و... می باشد. از آنجایی که هریک از مسایل مکان یابی و مسیریابی خود به تنهایی یک مساله NP-hard محسوب می شوند، آنگاه مساله مکان یابی- مسیریابی نیز یک مساله NP-hard ترکیبی محسوب می شود و برای حل آن نیاز به بهره گیری از الگوریتم های فراابتکاری می باشد.کلید واژگان: بهینه سازی، مساله مکانیابی - مسیریابی وسایل نقلیه، عدم قطعیت، الگوریتم رقابت استعماریIn the industrial world today, manufacturing units are trying to locate your requirements and the depot vehicle routing in order to transport the goods for reduce your cost. Needless to mention that the location of the warehouse is effective for vehicle routing. Therefore, in this paper, a mathematical programming model to optimize the storage location and vehicle routing are presented. The objective function of the model is minimizing the total cost associated with the transportation and storage of rental fee. Limitations of the model include vehicle capacity, the maximum distance traveled by vehicles and etc. In addition, labor costs, such as salaries, rent, warehouses, rental vehicles and etc. Approach to model the real world has been considered.Also, since each location and routing issues alone are a NP-hard problem, then location routing problem can be combined problem and It requires the use of meta- heuristic algorithms to solve.Keywords: Optimization, Location Routing Problem, Uncertainty, Imperialist Competitive Algorithm
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Portfolio selection is of great importance among financiers, who seek to invest in a financial market by selecting a portfolio to minimize the risk of investment and maximize their profit. Since there is a covariant among portfolios, there are situations in which all portfolios go high or down simultaneously, known as systemic risks. In this study, we proposed three improved meta-heuristic algorithms namely, genetic, dragonfly, and imperialist competitive algorithms to study the portfolio selection problem in the presence of systemic risks. Results reveal that our Imperialist Competitive Algorithm are superior to Genetic algorithm method. After that, we implement our method on the Iran Stock Exchange market and show that considering systemic risks leads to more robust portfolio selection. . Results reveal that our Imperialist Competitive Algorithm are superior to Genetic algorithm method. After that, we implement our method on the Iran Stock Exchange market and show that considering systemic risks leads to more robust portfolio selection.
Keywords: Portfolio Selection, Systemic Risks, Genetic Algorithm, Imperialist competitive algorithm -
This paper studies a location-inventory problem with uncertain demands and lead times in a three-level supply chain including a producer, multiple distribution centres (DCs) and multiple retailers. A number of perishable products such as food and medicine goods are considered with a specific shelf life; unlike the previous studies in the literature, the restrictions of storing different perishable products in identical DC is considered. The objective is to determine the number and location of DCs, the allocation of retailers to DCs, the reorder point and demand rate at each DC. Due to the uncertainty on demands and lead times, a queuing approach is utilized to model the problem. The problem is formulated as an integer nonlinear programming and solved using the Genetic and the Imperialist Competitive algorithms.Keywords: Location-inventory, perishable products, uncertain demands, lead times, Genetic Algorithm, Imperialist Competitive Algorithm
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A Cellular Manufacturing System (CMS) is the practical use of Group Technology (GP) in a production environment, which has received attention from researchers in recent years. In this paper, a mathematical model for the design of a cell production system is presented with consideration of Production Planning (PP). Consideration of environmental factors such as energy consumption and waste generated by machines in the proposed model is considered. Also, the problem of scheduling component processing in the presented model has been considered. Due to the complexity of the model presented in this paper, a hierarchical approach is proposed for solving the model. At first, the proposed model is analyzed without considering the scheduling topic using the GAMS software and the results are analyzed. Then an Imperialist Competitive Algorithm (ICA) was used to solve the scheduling problem. To evaluate the performance of the proposed model, numerical examples are used in small, medium, and large dimensions. In addition, the ICA presented in this paper is compared with the methods available in the literature as well as the genetic algorithm and its quality is confirmed.
Keywords: Cellular Manufacturing System, environmental effects, Imperialist Competitive Algorithm, machine-part processing scheduling -
در این مقاله، مساله زمان بندی یکپارچه تولید و توزیع با در نظر گرفتن مسیریابی وسایل نقلیه بررسی می شود. یک کارخانه که چند خط تولید موازی در اختیار دارد، سفارش های مشتریان را دریافت می کند؛ پس از تولید محصولات سفارش داده شده، آن ها به صورت دسته ای بوسیله ناوگانی از وسایل نقلیه برای مشتریان ارسال می شوند. بر خلاف شیوه ارسال مستقیم سفارشات از کارخانه برای هر یک از مشتریان، ارسال دسته ای به علت استفاده حداکثری از ظرفیت وسایل حمل و نقل باعث کاهش هزینه های حمل می شود، اما ممکن است منجر به افزایش هزینه های نگه داری و دیرکرد شود. هدف، یافتن یک برنامه زمانی یکپارچه ی تولید و توزیع است به گونه ای که هزینه های آماده سازی، نگه داری، توزیع و دیرکرد حداقل شود. ابتدا، مساله به صورت یک مدل برنامه-ریزی خطی عددصحیح مختلط مدله می شود. به دلیل NP-hard بودن آن، یک الگوریتم ترکیبی از الگوریتم رقابت استعماری و قواعد غلبه برای حل مسائل با ابعاد بزرگ ارائه می شود. به منظور ارزیابی عملکرد الگوریتم پیشنهادی، تعدادی مساله نمونه تولید و حل می شوند. نتایج محاسباتی حاکی از آن است که الگوریتم عملکرد خوبی برای مسائل با ابعاد بزرگ دارد.کلید واژگان: زمان بندی یکپارچه، مسیریابی وسایل نقلیه، ارسال دسته ای، هزینه نگه داری، الگوریتم رقابت استعماری، قواعد غلبهJournal of Industrial Engineering Research in Production Systems, Volume:6 Issue: 12, 2018, PP 63 -81In this paper, integrated scheduling of production and distribution with vehicle routing problem is considered. A manufacturer with parallel production lines receives customer orders; after producing them, they are then delivered to the customers in batches by a fleet of vehicles. Unlike a direct delivery of products from the manufacturer to each customer, batch delivery reduces the transportation costs because of the maximum utilization of the vehicle capacities, but it may increase the holding and tardiness costs. The objective is to find an integrated schedule of production and distribution so as to minimize the setup, holding, distribution and tardiness costs. The problem is first formulated as a mixed integer linear programming model. In view of its NP-hardness, a procedure by incorporating dominance properties with imperialist competitive algorithm is then proposed to solve large-sized problem instances. To evaluate the performance of the proposed algorithm, several instances are generated and solved. Computational results demonstrate that the algorithm has a good performance for large problems.Keywords: Integrated scheduling, Vehicle routing, Batch delivery, Holding cost, Imperialist competitive algorithm, Dominance properties
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مسئله چندین فروشنده دوره گرد (MTSP) تعمیم یافته مسئله معروف فروشنده دوره گرد 4(TSP) است که هدف این مسئله تعیین حداقل هزینه سفر به n شهر می باشد، به گونه ای که فروشندگان سفر خود را از یک نقطه به عنوان مبدا آغاز کرده و با عبور از تمام شهرها دوباره به نقطه مبدا بازگردند. همچنین در مسیر خود باید هر شهر را دقیقا یک مرتبه ملاقات کنند. در این مقاله که در شرکت پخش محصولات لبنی پگاه (بازار گستر) منطقه یک و برای حل مسئله واقعی ایشان انجام شده است، مدل فازی برای حل مسئله چندین فروشنده دوره گرد در شرایط وجود تقاضای غیرقطعی مشتریان، ارائه خواهد شد. تقسیم بندی شهر به مناطق کوچک تر و تخصیص هر یک از آنها به عاملین توزیع نیازمند صرف زمان زیادی است که نتیجه ای غیرقطعی نیز به دنبال خواهد داشت. در این پژوهش با استفاده از الگوریتم های فرا ابتکاری رقابت استعماری و جریان آب مسیرهای بهینه تعیین شد. درنتیجه محاسبات، جواب های به دست آمده از الگوریتم رقابت استعماری از کیفیت بهتر و جواب های به دست آمده از الگوریتم جریان آب از مدت زمان محاسباتی کمتر برخوردار بودند. بر این مبنا نحوه تخصیص و ترتیب خدمت دهی به مشتریان اصلاح و متعادل سازی گردید.کلید واژگان: چندین فروشنده دوره گرد، عدم قطعیت، برنامه ریزی خطی فازی، الگوریتم رقابت استعماری، الگوریتم جریان آبThe Multiple Traveling Salesman Problem (MTSP) is a generalization of the famous Traveling Salesman Problem (TSP), whose goal is to determine the minimum cost of travels to n cities; so that salespersons begin their travels from one point as an origin and return to it after visiting all the cities. They must also visit each city exactly once. In this paper, which is implemented in Pegah Bazar-Gostar region 1 company, customer demands is considered as a fuzzy model. Dividing the city into smaller areas and allocating them to salesmen (vehicles) is so time-consuming and uncertain. In this research, by using meta-heuristic algorithms (imperialist competitive and water flow-like), the optimal routes are determined. Results specify that imperialist competitive algorithm is better in solution quality and water flow-like algorithm is better in computation time; so, based on this, the method of allocation and customer service prioritization has been corrected and balanced.Keywords: Multiple Traveling Salesman, Uncertainty, Fuzzy Linear Programming, Imperialist Competitive Algorithm, Water flow-like Algorithm
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در این مقاله، الگوریتم جدیدی براساس چارچوب الگوریتم رقابت استعماری برای حل مسئله زمان بندی پروژه با محدودیت منابع ارائه می شود. در این مسئله، فعالیت های پروژه با توجه به محدودیت های منابع و روابط پیش نیازی، به گونه ای زمان بندی می شوند که زمان پروژه حداقل شود. در الگوریتم پیشنهادی، به منظور مدل سازی عملگر جذب، از عملگر تقاطع یکنواخت استفاده شده و برای جلوگیری از همگرایی ناقص الگوریتم، دو عملگر انقلاب یک نقطه ای و چندنقطه ای پیشنهاد شده است. همچنین به منظور جست وجوی بهتر فضای جواب، دو الگوریتم بهبود پیشرو- پس رو و الگوریتم جست وجوی محلی مبتنی بر جایگشت به کار رفته است. پارامترهای الگوریتم، به وسیله طراحی آزمایش تاگوچی تنظیم و کارایی الگوریتم با حل مجموعه مسائل PSPLIB ارزیابی شده است. نتایج محاسبات و مقایسه آن ها با الگوریتم های موجود نشان می دهد که الگوریتم پیشنهادی، قابلیت یافتن جواب های نزدیک به بهینه در مسائل کوچک و تولید جواب های رقابتی در مسائل بزرگ را دارد.کلید واژگان: الگوریتم بهینه سازی، الگوریتم رقابت استعماری، مسئله زمان بندی پروژه با محدودیت منابعIn this paper, a new algorithm based on the framework of the imperialist competitive algorithm for solving resource constrained project scheduling problem (RCPSP) will be proposed. In this problem, the activities are scheduled based on the resource and precedence relationships constraints in a way that the makes pan will be minimized. In order to model the assimilation process, a uniform crossover has been used, and to avoid premature convergence of the proposed algorithm, two revolution operators including one point revolution and multi-point revolution will be introduced. Also, in order to enhance the exploitation ability, a combined local search including permutation based local search (PBLS) and forward-backward improvement (FBI) is performed. The algorithm parameters are determined by designing Taguchi experiment, and the efficiency of proposed ICA is demonstrated by solving PSPLIB problems. Computational results and comparisons with some existing algorithms show that the proposed algorithm can produce near-optimal solution for small problems and competitive solution for large ones.Keywords: Imperialist competitive algorithm, Optimization algorithm, Resource constrained project scheduling problem
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در این مقاله، مساله مسیریابی وسائط نقلیه با هدف کاهش انرژی مصرفی و زمان های طی مسیر در شرایطی که سرعت های سفر وابسته به زمان هستند مورد بررسی قرار می گیرد. این مساله به تعیین مسیرهای بهینه برای ناوگانی از وسائط نقلیه می پردازد به طوری که زمان طی مسیر میان نقاط (مشتریان) به زمانی از روز که سفر در آن نقطه آغاز می شود وابسته است. زمان دقیق سفر با دانستن زمان عزیمت و یک تخمین دقیق از سرعت متوسط وسیله نقلیه در آن مسیر محاسبه می شود. از این رو در ادامه به ارائه ی یک مدل ریاضی جدید برای کاهش زمان طی مسیر می پردازیم و از آنجایی که مساله مسیریابی وسائط نقلیه مورد بررسی از نوع مسایل NP-Hard است ازروش فراابتکاری رقابت استعماری (ICA) استفاده می شود. به همین جهت تعدادی از مسایل با در نظر گرفتن سرعت های سفر متغیر در بازه های زمانی مختلف موردبررسی قرار گرفته است و سپس برای نشان دادن کارایی الگوریتم طراحی شده جواب های به دست آمده با روش بهینه سازی انبوه ذرات (PSO) مقایسه می شوند.کلید واژگان: مسیریابی وسائط نقلیه، کاهش انرژی مصرفی، الگوریتم رقابت استعماری، بهینه سازی انبوه ذراتJournal of Industrial Engineering Research in Production Systems, Volume:4 Issue: 9, 2017, PP 213 -219In this paper, a new mathematical model for vehicle routing problem is presented. The objectives are to minimize the energy consumption and the travel times in which speeds varied in different hours of the day. Since the vehicle routing problem belongs to the category of NP-hard problems, to solve the problem, a method based on the imperialist competitive algorithm (ICA) is proposed. Finally, the associated results are compared with the results obtained by particle swarm optimization (PSO) on the well-known benchmark problems.Keywords: Vehicle routing problem, energy consumption, Imperialist competitive algorithm, Particle Swarm Optimization
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هدف این پژوهش، مطالعه سیستم های تولیدی چندمحصولی و چنددوره ای در محیط جریان کارگاهی انعطاف پذیراست، به طوری که اندازه انباشته و زمان بندی در آن به صورت هم زمان و یکپارچه تعیین می شود. در این زمینه، یک مدل برنامه ریزی عدد صحیح مختلط جدید برای فرموله کردن مسئله پیشنهاد می شود. تابع هدف شامل هزینه های تولید، موجودی و تامین خارجی است. درصورت برآورده نشدن تقاضای مشتریان باید این تقاضاها از طریق تامین کنندگان خارجی با قیمتی بالاتر تامین شود. با توجه به پیچیدگی محاسباتی زیاد مسئله مورد مطالعه، الگوریتم های بهینه سازی گروه ذرات و رقابت استعماری برای حل پیشنهاد می شود. ابتدا الگوریتم ها برای حل مثال های تولیدشده تصادفی با اندازه های مختلف استفاده می شود و سپس با معرفی مطالعه موردی صنعت کاشی، الگوریتم های مورد نظر برای حل آن نیز استفاده می شود. نتایج محاسباتی نشان می دهد الگوریتم های حل قابلیت رسیدن به جواب با کیفیت خوب در زمان معقول را دارند و برای مسئله ما الگوریتم رقابت استعماری، عملکرد محاسباتی بهتری دارد.کلید واژگان: الگوریتم بهینه سازی گروه ذرات (PSO)، الگوریتم رقابت استعماری (ICA)، تعیین اندازه انباشته و زمان بندی هم زمان، سیستم تولید جریان کارگاهی انعطاف پذیر_ محدودیت ظرفیت ماشین، مدل سازی ریاضیThe aim of this Paper is to study a multi-product, multi-period production systems in a hybrid flow shop so that lot-sizing and scheduling will be detemined simultaneously. A new mixed-integer programming model is proposed to formulate the studied problem. The objective function in this investigation includes the total cost of production, inventory and external supply. In the case of not satisfying the demand of customers, this demand should be met by foreign suppliers with higher price. The simultaneous lot-sizing and scheduling problem are classified in strongly NP-hard class. Due to the high computational complexity of the studied problem, particle swarm optimization (PSO) and imperialist competitive algorithms (ICA) are implemented for solving the considered problem. The algorithms explore the solution space for both lot-sizing and scheduling and find a combination of production plan and sequence that is feasible and close to optimum. First, the implemented algorithms are used for solving randomly generated instances with different sizes. Then, these methods are used to solve the case of tile industry and the obtained results by two methods are compared with each other. Computational experiences show that the algorithms are able to achieve good-quality solutions for the problem in a reasonable time. Also, the results of ICA are better than PSO results for the mentioned case study.Keywords: Hybrid flow shop production system, Imperialist competitive algorithm, Machine capacity constraint, Mathematical modeling, Particle swarm optimization, Simultaneous lot-sizing, scheduling
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International Journal of Industrial Engineering and Productional Research, Volume:27 Issue: 4, Dec 2016, PP 353 -371Flexible and dynamic supply chain network design problem has been studied by many researchers during past years. Since integration of short-term and long-term decisions in strategic planning leads to more reliable plans, in this paper a multi-objective model for a sustainable closed-loop supply chain network design problem is proposed. The planning horizon of this model contains multiple strategic periods so that the structure of supply chain can be changed dynamically during the planning horizon. Furthermore, in order to have an integrated design, several short-term decisions are considered besides strategic network design decision. One of these short-term decisions is determining selling price and buying price in the forward and reverse logistics of supply chain, respectively. Finally, an augmented e-constraint method is used to transform the problem to a single-objective model and an imperialist competitive algorithm is presented to solve large scale problems. The results analysis indicates the efficiency of the proposed model for the integrated and dynamic supply chain network design problem.Keywords: Supply Chain Network Design, Sustainability, Long-term, Short-term Decisions, e-constraint Method, Imperialist Competitive Algorithm
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Journal of Industrial Engineering and Management Studies, Volume:3 Issue: 1, Winter-Spring 2016, PP 77 -88Resource limitation in zero time may cause to some profitable projects not to be selected in project selection problem, thus simultaneous project portfolio selection and scheduling problem has received significant attention. In this study, budget, investment costs and earnings are considered to be stochastic. The objectives are maximizing net present values of selected projects and minimizing variance of them. Benefiting an efficient multi-objective approach to satisfy every conflicting objective, an integer non-linear goal programming model is developed. Another contribution of this paper is to consider cost dependency between the projects, in project portfolio selection and scheduling problem. Due to the complexity of this problem, especially in large sizes, imperialist competitive algorithm and genetic algorithm are presented. The effectiveness of the model and proposed algorithms are demonstrated via a case study in a knowledge based company at Ferdowsi University of Mashhad. The result shows high performance of the both proposed algorithms.Keywords: Project selection, scheduling, Cost dependency, Stochastic programming, Genetic Algorithm, Imperialist competitive algorithm
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Journal of Quality Engineering and Production Optimization, Volume:1 Issue: 2, Summer - Autumn 2015, PP 33 -44An optimal desirability function method is proposed to optimize multiple responses in multiple production scenarios, simultaneously. In dynamic environments, changes in production requirements in each condition create different production scenarios. Therefore, in multiple production scenarios like producing in several production lines with different technologies in a factory, various fitted response models are obtained for each response according to their related conditions. In order to consider uncertainty in these models, confidence interval of fitted responses has been defined in the proposed method. This method uses all values in the confidence region of model outputs to define the robustness measure. This method has been applied on the traditional desirability function of each scenario in order to get the best setting of controllable variables for all scenarios simultaneously. To achieve this, the Imperialist Competitive Algorithm has been used to find the robust optimal controllable factors setting. The reported results and analysis of the proposed method confirm efficiency of the proposed approach in a dynamic environment.Keywords: Multiple production scenarios, Robustness, Desirability function, Uncertainty, Imperialist Competitive Algorithm
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Journal of Industrial Engineering and Management Studies, Volume:2 Issue: 2, Summer-Autumn 2015, PP 26 -42This ýpaper presents a new multi-objective fuzzy stochastic data envelopment analysis model (MOFS-DEA) under mean chance constraints and common weights to estimate the efficiency of decision making units for future financial periods of them. In the initial MOFS-DEA þmodel, the outputs and inputs are ýcharacterized by random triangular fuzzy variables with normal distribution, in which data are ýchanging sequentially. Since the initial MOFS-DEA model is a complex model, we ýconvert it to its equivalent one-objective stochastic programming by ýusing infinite-norm approach. To solve it, we design a new hybrid meta-heuristic algorithm by integrating Imperialist Competitive Algorithm and Monte Carlo simulation. Finally, this paper presents a real application of the proposed model and the designed hybrid algorithm for predicting the efficiency of five gas stations for the next two periods of them, with using real information which gathered from credible sources. The results will be compared with the Qins hybrid algorithm in terms of solution quality and runtime.Keywords: Data envelopment analysis, Random fuzzy variable, Dynamic stochastic programming, Monte Carlo simulation, Imperialist competitive algorithm
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یکی از اهداف سیستم های یکپارچه لجستیکی، که به مثابه یک فلسفه مدیریتی جدید طی چند دهه گذشته پدید آمده، افزایش کارایی توزیع محصولات است. این نوع مسائل معمولا در دو بخش بررسی می شوند؛ مکان یابی تسهیلات برای سیاست های بلندمدت و مسیریابی وسائط نقلیه برای پاسخگویی بیشتر به تقاضای مشتریان در تصمیم های عملیاتی. این دو جزء به صورت جداگانه قابل حل است؛ اما این حل ممکن است به جواب بهینه مسئله اصلی منجر نشود و برای هر زیرمسئله جواب بهینه پیدا کند. این تحقیق، به تعیین همزمان مسائل مکان تسهیلات و مسیریابی وسائط نقلیه برای بازدید از تسهیلات مورد نظر، که باید سرویس دهی شوند، می پردازد. از آنجا که مسئله مورد بررسی از نوع مسائل NP-Hard است، به منظور حل آن در ابعاد بزرگ از الگوریتم رقابت استعماری تلفیقی استفاده می شود. برای نشان دادن کارایی الگوریتم پیشنهادی تعدادی از مسائل در ابعاد کوچک و بزرگ با این الگوریتم و روش حل دقیق به کمک نرم افزار CPLEX حل می شود. مقایسه این دو روش نشان دهنده کارایی الگوریتم پیشنهادی است. در پایان نتیجه گیری ارائه می شود.
کلید واژگان: الگوریتم رقابت استعماری تلفیقی، مسیریابی وسائط نقلیه، مکان یابی تسهیلاتIncreasing of the distribution efficiency is one of the most objectives of an integrated logistic system developed as a new management philosophy in the past few decades. The problem is examind in two parts: facilities location problem (FLP) for long policies and vehicle routing problem (VRP) to meet the customer demand. These two components can be solved separately; however, this solution may not be the optimum solution of the original problem. Hence, in this paper, facilities location and vehicle routing problems are considered simultaniously to visit the facilities that should be serviced. Due to the complexity of the integrated problem in large sizes, a hybrid imperialist competitive algorithm (ICA) is proposed. Furthermore, to show the efficiency of the proposed hybrid ICA, a number of test problems in small and large sizes are solved. Finally, the obtained results are evaluated with the results obtained by CPLEX. Finally, the conclusion is provided.
Keywords: Facilities location, Vehicle routing problem, Imperialist competitive algorithm -
Journal of Optimization in Industrial Engineering, Volume:7 Issue: 16, Summer and Autumn 2014, PP 55 -63The multiple traveling salesman problem (MTSP) is a generalization of the famous traveling salesman problem (TSP), where more than one salesman is used in the solution. Although the MTSP is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. This paper presents an efficient and evolutionary optimization algorithm which has been developed through combining Modified Imperialist Competitive Algorithm and Lin-Kernigan Algorithm (MICA) in order to solve the MTSP. In the proposed algorithm, an absorption function and several local search algorithms as a revolution operator are used. The performance of our algorithm was tested on several MTSP benchmark problems and the results confirmed that the MICA performs well and is quite competitive with other meta-heuristic algorithms.Keywords: Imperialist Competitive Algorithm, Multiple Traveling Salesman Problem, Lin, Kernigan Algorithm, NP, hard Problems
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مسئله ی چندین فروشنده ی دوره گرد (M T S P) گسترشی است مشهور از مسئله ی فروشنده ی دوره گرد (T S P)، که ثابت شده یک مسئله ی N P-h a r d است. اگرچه M T S P یک مسئله ی پیچیده ی بهینه سازی ترکیباتی است، می توان آن را به مسائل گوناگونی در مسیریابی و زمان بندی توسعه داد. به علاوه، تحقیقات روی این مسئله، برخلاف T S P که گستردگی آن توجه بسیار زیادی را به خود معطوف کرده، بسیار محدود بوده است. از این رو، در این نوشتار یک روش جدید بهینه سازی به نام «الگوریتم رقابت بهره جویانه (I C A)» برای حل این مسئله ارائه می شود. این روش ملهم از رقابت کشورهای مستقل و وابسته برای حل مسائل بهینه سازی ترکیباتی است. الگوریتم پیشنهادی روی دو دسته مسائل از ادبیات موضوع مورد آزمایش قرار گرفته است. نتایج محاسباتی نشان می دهد که الگوریتم رقابت خوبی با دیگر الگوریتم های فراابتکاری برای حل مسائل M T S P داشته است. همچنین چندین جواب بهینه به وسیله ی الگوریتم پیشنهادی به دست آمد.
کلید واژگان: مسئله ی چندین فروشنده ی دوره گرد، الگوریتم رقابت بهره جویانه، مسائل بهینه سازی ترکیباتی، مسئله ی مسیریابی وسیله ی نقلیهThe Multiple Traveling Salesmen Problem (MTSP) is a well-known generalization of the Traveling Salesman Problem (TSP). In general, The MTSP can be defined as follows:Given a set of n nodes (cities), let there be m salesmen located at a single depot node. The remaining nodes that are to be visited are called intermediate nodes. Then, the MTSP consists of finding tours for m salesmen. All of them start and end at the depot, such that each intermediate node is visited exactly once. Our aim is to minimize the total cost of visited nodes by the salesmen. There is a broad variety of versions of MTSP and a wide range of literature on this class of problems. The variants include MTSP with pickup and delivery, time windows, multi depots, and others. There have been important advances in the development of exact and approximate algorithms for solving the MTSP, as an important NP-hard combinatorial optimization problem. Although heuristic methods solve NP-hard problems, they become trapped in local optima and cannot gain a good suboptimal solution. As a result, in the last 30 years, a new kind of approximate algorithm called meta-heuristics has emerged. Basically, this type of algorithm tries to combine basic heuristic methods into higher level frameworks aimed at efficiently and effectively exploring a search space.In contrast, the research on the MTSP has received less attention than the TSP. Therefore, in this paper for the MTSP, a new meta-heuristic algorithm called the Imperialist Competitive Algorithm (ICA), is proposed. This algorithm has been motivated by the notion of imperialism and imperialistic competition in order to solve optimization problems. The proposed algorithm is tested using two benchmark data sets available from the literature. The computational results show that the ICA is competitive with other published results for solving MTSP. In addition, for some instances, the proposed algorithm gives better solutions.Keywords: Multiple traveling salesmen problem, Imperialist competitive algorithm, Combinatorial optimization problems, Vehicle routing problem -
The main goal in this paper is to propose an optimization model for determining the structure of a series-parallel system. Regarding the previous studies in series-parallel systems, the main contribution of this study is to expand the redundancy allocation parallel to systems that have repairable components. The considered optimization model has twoObjectivesmaximizing the system mean time to first failure and minimizing the total cost of the system. The main constraints of the model are: maximum number of the components in the system, maximum and minimum number of components in each subsystem and total weight of the system. After establishing the optimization model, a multi objective approach of Imperialist Competitive Algorithm is proposed to solve the model.Keywords: redundancy allocation problem, series, parallel system, repairable components, multi objective optimization, imperialist competitive algorithm
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Journal of Optimization in Industrial Engineering, Volume:6 Issue: 13, Summer and Autumn 2013, PP 65 -72This paper investigates the problem of selecting and scheduling a set of projects among available projects. Each project consists of several tasks and to perform each one some resource is required. The objective is to maximize total benefit. The paper constructs a mathematical formulation in form of mixed integer linear programming model. Three effective metaheuristics in form of the imperialist competitive algorithm, simulated annealing and genetic algorithm are developed to solve such a hard problem. The proposed algorithms employ advanced operators. The performance of the proposed algorithms is numerically evaluated. The results show the high performance of the imperialist competitive algorithm outperforms the other algorithms.Keywords: Project portfolio selection, scheduling, imperialist competitive algorithm, Simulated annealing, genetic algorithm, mixed integer programming
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