metaheuristic algorithm
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International Journal of Research in Industrial Engineering, Volume:14 Issue: 2, Spring 2025, PP 234 -255Cloud Manufacturing (CMfg) enables flexible and customized manufacturing services through dynamic service composition. However, achieving optimal service composition remains challenging due to the need to meet complex Quality of Service (QoS) requirements, including cost, time, quality, and resource workload balance. Notably, previous studies on service composition models have rarely considered workload balancing as part of their QoS criteria, which is critical for maintaining efficient and sustainable resource use. This study addresses this gap by presenting an advanced service composition model that integrates workload balance as an essential QoS metric alongside traditional factors like composite service quality, time, and cost. To further support optimization, the Simulated Annealing (SA) and Tabu Search (TS) algorithms are enhanced with a novel shaking mechanism designed to expand the search space and mitigate premature convergence risks common in metaheuristics. Experimental evaluations conducted on an OR-Library dataset confirm that the enhanced SA algorithm achieves up to a 25% improvement in the fitness function and a 7% reduction in computational time, while the improved TS algorithm achieves a 2% reduction in the fitness function and a 21% decrease in computational time. These findings highlight the model's potential to enhance CMfg service composition efficiency, offering substantial performance benefits over traditional methods. The core contributions of this study include the development of a workload-integrated service composition model and enhancements to SA and TS algorithms for effective problem-solving within this framework.Keywords: Cloud Manufacturing, Optimal Service Composition, Metaheuristic Algorithm, Improved SA, Improved TS
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مسائل مکان یابی هاب، یکی از مهم ترین شاخه های حمل ونقل هستند، که استفاده ی زیادی از آن ها در حوزه های راهبردی می شود. در شبکه ی توزیع، استفاده از هاب ها باعث کاهش هزینه های انتقال جریان در شبکه می شود. در پژوهش حاضر، یک مدل دوهدفه برای مسئله ی مکان یابی هاب سلسله مراتبی ارائه شده است. در پژوهش حاضر، با توجه به اهمیت مشکلات زیست محیطی در دنیای واقعی و نگرانی از افزایش آلودگی های مخرب زیست محیطی، علاوه بر بهبود و کاهش هزینه ها به مسائل زیست محیطی نیز پرداخته شده است. مدل ارائه شده به بررسی حمل ونقل چندحالته و ایجاد چند نوع سیستم حمل ونقل در یک هاب می پردازد. در ادامه، با استفاده از نرم افزار GAMS مسائل با ابعاد کوچک و نیز در ابعاد بزرگ با استفاده از الگوریتم های فراابتکاری ژنتیک، پارتویی قوی، و گرگ خاکستری حل و نتایج با یکدیگر مقایسه شده اند. با توجه به نتایج به دست آمده مشاهده شده است که الگوریتم های ارائه شده، کارایی مناسبی دارند.
کلید واژگان: مسئله ی مکان یابی، هاب سلسله مراتبی، پایداری، زنجیره ی تامین، الگوریتم فراابتکاریIdentifying the optimal location for facilities is a key strategic objective for companies striving to enhance their competitiveness. Managers carefully select facility locations to ensure they effectively meet demand and align with organizational goals. Given the impracticality of establishing direct communication between all points in a network, utilizing hub points within networks can result in significant cost savings. Hub location problems are one of the new and remarkable topics in industrial engineering and one of the most important branches of transportation which is widely used in strategic areas such as transportation systems, postal systems, and communication networks. The use of hubs in the distribution network reduces the costs of current transmission in the network and thus increases system efficiency. In summary, hubs are used in different places of the supply chain such as transferring from point to point, sorting, and switching. The problem of location-allocation of hub is one important problem that is common in many transportation systems. One of the important branches of hub area is hierarchical hub that has been considered by many researchers. In this research, a two-objective model for the hierarchical hub location problem is presented. Given the importance of real-world environmental problems and concerns about increasing destructive environmental pollution, in this study, in addition to reviewing and trying to improve and reduce costs, environmental problems and their improvement have been studied. The proposed model also examines multi-mode transport and creates several types of transport systems in one hub. In the following, smaller problems are solved by GAMS software and large-scale problems are solved by genetic, strong Pareto and gray wolf metaheuristic algorithms and the results are compared. The results of solving problems with different dimensions show the good performance of the proposed algorithm, so that by using this method in an acceptable time, a suitable quality answer can be obtained.
Keywords: Location, Hierarchical Hub, Sustainability, Supply Chain, Metaheuristic Algorithm -
هدف
این مقاله به مدل سازی یک شبکه زنجیره تامین پایدار صنعت برق تحت عدم قطعیت می پردازد. هدف از ارایه این شبکه زنجیره تامین برآورده سازی تقاضای مشتریان در رابطه با پنل های خورشیدی به منظور تولید انرژی پاک است.
روش شناسی پژوهش:
یک مدل برنامه ریزی خطی عدد صحیح مختلط شامل مکان یابی مراکز تولید، انتخاب تامین کننده، تخصیص بهینه جریان و تعیین قیمت بهینه پنل های خورشیدی در شبکه درنظر گرفته شده است. اهداف پایداری مدل شامل بیشینه سازی سود شبکه زنجیره تامین، کمینه سازی میزان انتشار گازهای گلخانه ای و بیشینه سازی قابلیت اطمینان است. به منظور کنترل پارامترهای غیرقطعی مدل نیز روش بهینه سازی استوار امکانی درنظر گرفته شده و از روش های دقیق و فرا ابتکاری برای حل مدل بهره گرفته شده است.
یافته هانتایج مدل نشان داد با افزایش قابلیت اطمینان در شبکه، میزان ارزش خالص فعلی در شبکه کاهش و میزان انتشار گازهای گلخانه ای در شبکه افزایش یافته است. هم چنین، تحلیل نتایج نشان داد که با افزایش نرخ عدم قطعیت در شبکه، ارزش خالص فعلی و قابلیت اطمینان در شبکه کاهش و میزان انتشار گازهای گلخانه ای افزایش یافته است. درنهایت، نتایج آزمون آماری نیز نشان داد که هیچ گونه اختلاف معناداری بین میانگین های تعداد جواب کارا، بیشترین گسترش و فاصله متریک بین دو الگوریتم وجود نداشته و تنها بین زمان حل دو الگوریتم اختلاف معنادار وجود دارد. نتایج روش های حل ارایه شده نشان از کارایی بالای آن ها در حل مدل شبکه زنجیره تامین پایدار صنعت برق دارند.
اصالت/ارزش افزوده علمی:
در مدل پیشنهادی تصمیمات مهمی از جمله انتخاب تامین کننده، احداث مراکز تولید، تخصیص بهینه جریان محصول و قیمت گذاری پنل خورشیدی اتخاذ گردید. از سوی دیگر، تحلیل های بیشتر بر روی 15 مثال عددی نشان از کارایی بالای دو الگوریتم MOALO و MOWOA نسبت به روش اپسیلون محدودیت بود.
کلید واژگان: زنجیره تامین صنعت برق، بهینه سازی استوار امکانی، قابلیت اطمینان، پایداری، الگوریتم فرا ابتکاریPurposeThis paper focuses on modeling a sustainable electricity industry supply chain network under uncertainty. The aim of presenting this supply chain network is to meet customer demands for solar panels to generate clean energy.
MethodologyA mixed-integer linear programming model, including facility location, supplier selection, optimal flow allocation, and determination of the optimal price of solar panels in the network, is considered. The sustainability objectives of the model include maximizing the profit of the supply chain network, minimizing greenhouse gas emissions, and maximizing reliability. A robust optimization method is also considered to control uncertain parameters, and precise and innovative techniques are used to solve the model.
FindingsThe results of the model show that with an increase in network reliability, the current net value in the network decreases, and greenhouse gas emissions in the network increase. Additionally, the analysis of the results shows that with an increase in the network's uncertainty rate, the network's current net value and reliability decrease, and greenhouse gas emissions increase. Finally, the statistical test results also show that there was no significant difference between the averages of the number of practical solutions, the maximum spread, and the metric distance between the two algorithms, and only a significant difference exists between the solution times of the two algorithms. The results of the presented solution methods demonstrate their high efficiency in solving the sustainable electricity industry supply chain model.
Originality/Value:
In the proposed model, essential decisions such as supplier selection, establishment of production centers, optimal product flow allocation, and pricing of solar panels are made. On the other hand, further analyses of 15 numerical examples show the high efficiency of the MOALO and MOWOA algorithms compared to the epsilon-constraint method.
Keywords: Electricity industry supply chain, robust optimization, Reliability, Sustainability, Metaheuristic Algorithm -
International Journal of Research in Industrial Engineering, Volume:12 Issue: 2, Spring 2023, PP 197 -204Feature selection is the process of picking the most effective feature among a considerable number of features in the dataset. However, choosing the best subset that gives a higher performance in classification is challenging. This study constructed and validated multiple metaheuristic algorithms to optimize Machine Learning (ML) models in diagnosing Alzheimer’s. This study aims to classify Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s by selecting the best features. The features include Freesurfer features extracted from Magnetic Resonance Imaging (MRI) images and clinical data. We have used well-known ML algorithms for classifying, and after that, we used multiple metaheuristic methods for feature selection and optimizing the objective function of the classification. We considered the objective function a macro-average F1 score because of the imbalanced data. Our procedure not only reduces the irreverent features but also increases the classification performance. Results showed that metaheuristic algorithms could improve the performance of ML methods in diagnosing Alzheimer’s by 20%. We found that classification performance can be significantly enhanced by using appropriate metaheuristic algorithms. Metaheuristic algorithms can help find the best features for medical classification problems, especially Alzheimer’s.Keywords: Metaheuristic Algorithm, Alzheimer’s disease, MRI, Machine Learning, Feature selection, Data mining
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Nowadays, in the competitive global market, increasing market share is the main objective of the most manufacturers, however, customization, service speed, customer satisfaction, and environmental problems are vital factors that manufacturers ought to consider to expand their market share.so, the supply chain management can be applied as a proper approach to optimize these factors in whole supply chain to benefit the supply chain members. In this way, the current paper addresses an integrated production and distribution model with combination of Stackelberg competition and Make-to-order production system in different periods. In addition, this model wants to investigate how discounts impact the chain's profits with presence of competition and Make-to-Order production system. This study uses a modified Non-Dominated Sorting Genetic Algorithm II (NSGA-II) approach to solve the medium and large cases model because of the NP-hardness feature. Additionally, the model is applied to Furniture Company to demonstrate its efficacy and validity and results are provided. According to the obtained results, the modified algorithm has better performance in solving model in medium and large-scale cases. The proposed model would be beneficial to increase network efficiency by integrating production-distribution planning.Keywords: Production-distribution, Competition, Metaheuristic Algorithm, Stackelberg competition, Environmental Problems
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In Home Health Care (HHC) operations, one of the major aims of HHC centers is to timely meet patients' demands. According to the feedback from HHC centers, their decision-makers deal with some complexity in scheduling and routing of their health workers. Inspired by this point, the present research introduces a new HHC routing and scheduling problem considering different skill levels of health workers and different levels of patients’ needs. So, in such a condition, a highly qualified health worker can visit those patients who need lower-skilled demands while a low-qualified health worker cannot visit those who request higher skills. In this way, the total cost of the system will be lower compared to the situation in which the patients' needs exactly match the health workers' skills. Moreover, we consider that the maximum number of homes each health worker is tasked to visit during the day is specified and if more patients than this specified limit are assigned to each health worker, an additional cost will be imposed on the center in proportion to the excess number of patients. Since patient satisfaction, which is obtained with timely visits, is important for each HHC center, a hard time window is considered for each patient. The presented model is solved using the GAMS software with the CPLEX solver. Along with the MIP approach, a metaheuristic algorithm based on a Simulated Annealing (SA) algorithm is adopted to solve the problem. The results give the managers insight into this method of cost management in comparison with manual and traditional traditional planning. This study may help the decision-makers of HHC centers make more accurate decisions which, in turn, result in timelier service provision, increase the patients' satisfaction level, and improve the overall efficiency of HHC centers.Keywords: Home Health Care, routing, scheduling, Health worker, Metaheuristic Algorithm
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International Journal of Supply and Operations Management, Volume:5 Issue: 3, Spring 2018, PP 234 -255
Nowadays, produced wastes in urban areas are growing exponentially all over the world. On the other hand, the environment and natural resources are on the way to destruction. One way to deal with increasing waste generation and protecting the environment is proper management of municipal solid wastes. One aspect of municipal solid waste management is locating the various facilities and the routing between them. In this study, a new mathematical model is developed for location-routing problem in MSWM system. Considering the integrity of MSWM facilities is the strength of this study. The proposed model meets two objectives including minimization of system costs and environmental impacts. In this model, the location of waste collection centers and reverse logistics centers are determined. In order to improve the efficiency and practicality of the proposed model, a solution method based on the NSGA-II is proposed. Also, a new method based on best worst approach developed to parameter tuning of NSGA-II. As a result, it observed that the total costs of the system increases exponentially as a result of increase in the volume of waste in sources. Numeral experiments indicate the efficiency of proposed algorithm in achieving approximate optimum solution in an acceptable time.
Keywords: Location-routing, metaheuristic algorithm, multi-objective problem, Municipal solid waste management -
Emissions resulted from transportation activities may lead to dangerous effects on the whole environment and human health. According to sustainability principles, in recent years researchers attempt to consider the environmental burden of logistics activities in traditional logistics problems such as vehicle routing problems (VRPs). The pollution-routing problem (PRP) is an extension of the VRP which consists of routing a number of vehicles to serve a set of customers and determining their speed on each route segment so as to minimize a function of comprising fuel, emissions and driver costs. This paper proposes an adaptive large neighborhood search for the robust PRP (RPRP) under demand uncertainty. The achieved results indicate a premium performance of the solutions obtained by the proposed robust models.Keywords: Green vehicle routing, pollution-routing problem, robust optimization, Metaheuristic Algorithm
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زمانبندی کار ها در صنایعی که روند حرکت کار ها بر روی ماشین ها به صورت دوره ای می باشد، همچون صنایعی که محصولات آنها فاسد شدنی نظیر صنایع غذایی و یا دارای طول عمر همانند مواد شیمیایی، رادیواکتیو و غیره هستند، از اهمیت زیادی برخوردار است، زیرا که این صنایع به دلیل محدودیت های زمانی و یا رقابت با سایر شرکت ها سعی در کمینه نمودن بازه زمانی انجام کار ها دارند. از آنجا که غالبا محیط تولیدی این صنایع به صورت تولید جریان کارگاهی مختلط دوره ای می باشد و اثر یادگیری اپراتور در سرعت تولید مشهود است، این پژوهش در نظر دارد که زمان چرخه بر روی هر ماشین را با وجود اثر یادگیری به کمک چینش فعالیت ها کمینه نماید. برای این منظور در روند این پژوهش، ابتدا تحقیقات پیشین در این حوزه مورد مطالعه قرار گرفت. سپس مدل ریاضی این مساله نوشته و به دلیل آنکه ماهیت کمینه نمودن زمان انجام کار ها در محیط تولید جریان کارگاهی مختلط دوره ای، جزء مسائل سخت (NP-Hard) می باشد، برای حل این مساله از سه روش فراابتکاری الگوریتم ژنتیک، الگوریتم شبیه سازی تبرید و الگوریتم شبیه سازی تبرید مبتنی بر جمعیت استفاده شد. نتایج نشان می دهند که الگوریتم شبیه سازی تبرید مبتنی بر جمعیت به دلیل ساختار جمعیتی آن، به طور میانگین نسبت به دو الگوریتم دیگر کارایی بهتری دارد.کلید واژگان: زمانبندی، جریان کارگاهی مختلط، اثر یادگیری، الگوریتم فراابتکاریJobs scheduling in industries with cyclic procedure on machines, such as perishable products (food industries) or products with a limited lifetime (chemicals, radio actives, etc), is very important. Due to time limitation or competition with other companies, these industries try to minimize thecycle time of jobs processing. Since most productive environments of the industries are cyclic hybrid flow shop and operators learning effect is obvious in speed of productions, the aim of this study is to minimize cycle time of each machine with learning effect by consequence of jobs. After proposing a mathematical model and since the cyclic hybrid flow shop environment is NP-hard, three metaheuristics, i.e., genetic algorithm, simulated annealing algorithm and population based simulated annealing algorithm, have been proposed for solving this problem. Results show that on average, population based simulated annealing algorithm due to its population-based structure has a better performance in comparison to other algorithms.Keywords: Scheduling, Hybrid flow shop, Learning effect, Metaheuristic algorithm
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در این نوشتار زنجیره ی تامین حلقه بسته با لحاظ انواع هزینه ها ونیز محدودیت های ظرفیت و زمان، برای محصول های تکنولوژیک که هزینه ی تولید و قیمت فروش آنها نزولی است، به منظور تعیین مقدار و زمان سفارش دهی، تولید و تحویل برنامه ریزی می شود. بدین منظور از چهار روش متاهیوریستیک الگوریتم ژنتیک، بهینه سازی انبوه ذرات، تکامل تفاضلی و کولونی زنبورهای مصنوعی به همراه برخی تغییرات در حل مسئله استفاده شده و جواب آنها برای مسائل با ابعاد کوچک با جواب بهینه ی حاصل ازحل مدل برنامه ریزی مختلط عدد صحیح مسئله مقایسه شده است. نتایج حاصل از تحلیل عددی برای مسئله ی با ابعاد کوچک و نیز با ابعاد بزرگ نشان می دهد که خطای روش تکامل تفاضلی قابل قبول است و نیز کم ترین خطا در بین چهار روش ذکر شده است.کلید واژگان: زنجیره ی تامین حلقه بسته، قیمت پیوسته ی نزولی، الگوریتم متاهیوریستیکIn a global economy, providing products, at the right time in the right quantity and at a low cost, can be regarded as a key to success. Efficient supply chains have an important role in guaranteeing this success. The objective of this paper is to plan a single product, multi-echelon, multi-period closed loop supply chain (CLSC) for high-tech products, and, finally, the decisions made regarding component procurement, production, distribution, recycling and disposal. The considered planning problem is like a Knapsack problem. Therefore, it can be concluded that it is NP-hard. To plan the explored CLSC problem, the time horizon is divided into some equal periods, and planning is done for them. The more the number of divisions or periods and the closer the planning to reality, the more the dimensions of the problem and the more the amount of solving time needed. This is especially true in NP-hard problems. When analytic methods such as the branc and bound method (for solving MILP model) are used, an increase of the problem dimensions leads to a drastic increase in solving time. Thus, in the case of these problems, metaheuristic algorithms should be used to make a near optimal solution. So, four proposed heuristic-based variables, including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and the artificial bee colony (ABC), were implemented in order to solve the mixed integer linear programming model (MILP). Finally, the computational results obtained through these four methods were compared with the solutions obtained by GAMS optimization software. The solution revealed that the DE methodology performs very well in terms of both quality of solution obtained and computational time. The results of this study indicated an approximate solution for selecting active markets among potential markets. Also, for determining the time and quantity of components and products to produce and ship in a CLSC, in general, and for high-tech products, in particular, by dividing the time horizon into many periods, which increases the accuracy of planning.Keywords: Closed loop supply chain, continuous price decrease, metaheuristic algorithm
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فصلنامه مهندسی تصمیم، پیاپی 3 (تابستان 1394)، صص 109 -146برای اینکه سازمانی بتواند در جهت رشد و بهبود بهره وری خود اقدام نماید، لازم است که عوامل موثر در زمینه بهبود بهره وری را شناسایی کرده و سپس بر اساس اهمیت آنها، اقدامات مناسب را به عمل آورد. پژوهش حاضر با هدف تعیین عواملی که به صورت مستقیم و غیر مستقیم بر روی بهره وری نیروی انسانی و حقوق و دستمزد کارکنان به صورت هم زمان تاثیر می گذارند و همچنین بررسی چگونگی تاثیر این دو عامل بر روی یکدیگر، صورت گرفته است. در این راستا با در نظر گرفتن قابلیت بالای شبکه های بیزی در برآورده کردن اهداف این پژوهش و همچنین استفاده از الگوریتم های فراابتکاری برای بالا بردن دقت کار، به کشف و بررسی روابط علت و معلولی بین متغیرهای درونی و مقطعی کارگاه های صنعتی با بیش از 10 نفر کارکن در سال 1386 که به عنوان آخرین اطلاعات بنگاه های صنعتی شامل 13239 بنگاه در اختیار بوده، پرداخته شده است.
نتایج به دست آمده از مدل برآورد شده ی نهایی از میان انواع شبکه های بیزی تست شده، نشان می دهد که علاوه بر اینکه میزان حقوق و دستمزد کارکنان تابعی از ارزش افزوده می باشد، بهره وری نیز تابعی از ارزش افزوده و دستمزد می باشد. همچنین تاثیری که ارزش افزوده بر روی بهره وری دارد حدود 4 برابر بیشتر از تاثیری است که حقوق و دستمزد کارکنان بر روی بهره وری دارد.کلید واژگان: بهره وری نیروی انسانی، حقوق و دستمزد، کارگاه های صنعتی، الگوریتم های فراابتکاری، شبکه ی بیزیIn order to grow and improve productivity of organization, it is necessary to identify effective factors and then, based on their importance, appropriate actions to be taken. The aim of this study is determine the factors that directly and indirectly effect on the productivity of labor and wages, simultaneously, and also examine how these two indicators effect on each other. In this context, due to the ability of the bayesian networks to meet the objectives of this research and, metaheuristic algorithm to achieve maximum efficiency while minimal processing, we will discover and evaluate of casual relationships between internal variables (specifically, wages and labor productivity) of Industrial workhouse with 10 and more than 10 employees in 1386, as the latest data available, that include 13239 firms.
The results of the final version of the model that has been tested through a variety of bayesian networks, show that in addition to the wages of employees is a function of value added, productivity is also a function of added value and wages. Effect of value added on labor productivity is about 4 times higher than the impact of the wages of employees on it.Keywords: Productivity of Human Resources (Labor Productivity), Wages, Industrial Workhouse, Metaheuristic Algorithm, Bayesian Networks
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