Sharif University of TechnologyScientia Iranica1026-3098321220250501Zoning constrained machine layout problem with mutual clearances2405910.24200/sci.2022.57877.5453ENZeynepUrukDepartment of Industrial Engineering, Sakarya University, Sakarya, Turkey.NevraKazanciDepartment of Industrial Engineering, Sakarya University, Sakarya, Turkey.Journal Article20210316In this paper, a single row machine layout problem is considered with zoning constraints and mutual clearances under an enhanced objective of minimizing material flow cost and machine installation cost. The problem is restricted by positive and negative zoning constraints to represent real-life problems. Moreover, the clearances needed between machine pairs are divided into two types, which are must and extra clearances. Extra clearances are reduced through mutual use between adjacent machines to decrease material flow costs. Objective function also considers fixed costs of locating machines which usually neglected in machine layout problems in literature but a necessity in real-life problems. Two mathematical models, namely nonlinear and linear mixed integer programs, are formulated to solve the problem optimally and to compare the effect of linearity and nonlinearity in mathematical programming formulations in terms of solution quality and time. The mathematical models are not effective in terms of time for large problem instances; therefore, a genetic algorithm is proposed to generate high-quality solutions within a reasonable time. It is shown that the genetic algorithm outperforms both the nonlinear and linear mathematical models with lower cost and shorter time.https://scientiairanica.sharif.edu/article_24059_600d5fa630e5a8f78ef25121dc136a20.pdfSharif University of TechnologyScientia Iranica1026-3098321220250501A modified Russell measure for estimating efficiency changes in the presence of the undesirable outputs and stochastic data2415110.24200/sci.2021.58051.5538ENSeyedeh SaraHosseiniDepartment of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran.RezaFarzipoor SaenDepartment of Operations Management and Business Statistics, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman.RezaKazemi MatinDepartment of Mathematics, College of Science, Sultan Qaboos University, Muscat, Oman.Journal Article20210405Although Data Envelopment Analysis (DEA) assumes deterministic data, a great volume of data might be stochastic. The Global Malmquist Productivity Index (GMPI) is a highly effective instrument for productivity analysis in DEA. This paper extends GMPI in the presence of stochastic data. Our new stochastic DEA model is a Chance-Constrained Programming (CCP) model, which is converted to a deterministic programming problem with a linear objective function and quadratic constraints. For efficiency evaluation purposes, in this paper, the weak disposability principle is used to model Russell’s measure in the presence of undesirable outputs. The main contribution of this paper is to develop a global Russell model with stochastic data. A case study is presented to illustrate the applicability of the proposed models.https://scientiairanica.sharif.edu/article_24151_9b6e62858e82a755fcce70013c328717.pdfSharif University of TechnologyScientia Iranica1026-3098321220250501Multi-objective low-carbon hybrid flow-shop scheduling via an improved teaching-learning-based optimization algorithm2416510.24200/sci.2022.58317.5665ENWenjieWangKey Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education), National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China.XueshengZhouCollege of Engineering, Nanjing Agricultural University, Nanjing, China.GuangdongTianKey Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education), National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China.Amir M.Fathollahi-FardDepartment of Electrical Engineering, École de Technologie Supérieure, University of Québec, Montréal, Canada.PengWuSchool of Econ & Management, Fuzhou University, Fuzhou, China.ChaoyongZhangState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China.ChengwenLiuDepartment of Shop Management in Ruixing Group Co., Ltd., Taian, China.ZhiwuLiInstitute of Systems Engineering, Macau University of Science and Technology, Macau, China.School of Electro-Mechanical Engineering, Xidian University, Xi’an, China.Journal Article20210512In this article, for achieving effective and environmentally friendly production scheduling, we investigate a Multi-objective Low-carbon Hybrid Flow-shop Scheduling Problem (MLHFSP) with the consideration of machines with varied energy usage ratios. The problem is formulated by a multi-objective mathematical model with two optimization objectives, i.e., minimizing Total Carbon Emission (TCE) and makespan (Cmax). We primarily analyse the formation of TCE and derive its mathematical expression. MLHFSP is Non-deterministic Polynomial-hard (NP-hard), therefore, to tackle the model, an Improved multi-objective Teaching-Learning-Based Optimization (ITLBO) algorithm is proposed. The ITLBO algorithm mainly contains global search-based teaching phase and local search-based learning phase. In ITLBO, a solution is represented by two vectors, i.e., job sequence vector and machine assignment vector. Sigma method is utilized to quantify each individual, and to avoid local optimum, Sequential Neighbourhood Search (SNS) method is also adopted. Experimental results validate the feasibility and effectiveness of the proposed ITLBO in addressing MLHFSP. The research findings help manufacturing engineers to seek a sophisticated balance between carbon emission reduction and makespan reduction.https://scientiairanica.sharif.edu/article_24165_8f25327bf67db45be41e7c8b4769d0ee.pdfSharif University of TechnologyScientia Iranica1026-3098321220250501Improved CEM–RBS control charts for monitoring the process mean using ranked–based sampling designs2416810.24200/sci.2022.57495.5268ENMuhammadTayyabDepartment of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.WasifYasinDepartment of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.AsmaArshadDepartment of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.MuhammadNoor-ul-AminDepartment of Statistics, COMSATS University Islamabad, Lahore Campus, Pakistan.MuhammadHanifDepartment of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.Journal Article20210108In this study, different Ranked-Based Sampling (RBS) schemes are used to design a sensitive control chart to monitor the small or moderate shifts in the process mean, and are named combined Exponentially Weighted Moving Average (EWMA) Moving Average (MA) RBS (CEM-RBS) control charts. The Average Run-Length (ARL) and the Standard Deviation of the Run-Length (SDRL) are computed through Monte Carlo (MC) simulation runs to evaluate the performance of the proposed charts in comparison with the existing charts such as MA, EWMA, EWMA-MA, and the EWMA under RBS control charts. It is proved through a comparative study that the proposed CEM–RBS charts indicate a significant improvement in the performance of the EWMA-MA chart by using the RBS concept. A real dataset-based example is also included to explain the concept in detail.https://scientiairanica.sharif.edu/article_24168_66629dc988951697474e99165f5692de.pdfSharif University of TechnologyScientia Iranica1026-3098321220250501Reliability-redundancy allocation problem of a queueing system considering energy consumption2416910.24200/sci.2022.58080.5562ENKamyarSabri-LaghaieDepartment of Industrial Engineering, Urmia University of Technology, Urmia, Iran.MahdiFathiDepartment of Information Technology and Decision Sciences, G. Brint Ryan College of Business, University of North Texas, Denton, Texas, USA.Journal Article20210413In Reliability-Redundancy Allocation Problem (RRAP), the reliability and redundancy of components in a given system configuration are determined while considering some problem-specific constraints. RRAP can be applied in various industries. Moreover, queueing systems are among the most common systems in the manufacturing and service industries. Failure in queueing systems can result in unwanted severe damages. Reliability analysis of queueing systems should be conducted concerning their performance measures. Therefore, a RRAP of a queueing system considering queueing costs is studied in this article. The proposed cost function includes queueing, repair, and energy consumption costs. A Memetic Algorithm (MA) is used to obtain optimal redundancy and failure rates of components and the system’s service rate, which affects the energy consumption level. Extensive numerical experiments and sensitivity analyses are performed to present the problem’s applicability and the proposed algorithm.https://scientiairanica.sharif.edu/article_24169_ab935b81379c7cca9ca701a8fa8650fb.pdfSharif University of TechnologyScientia Iranica1026-3098321220250501Simulation optimization approach for dynamic and stochastic closed loop supply chain network2417510.24200/sci.2022.58224.5626ENAyşe TuğbaDosdoğruDepartment of Industrial Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey.AsliBoru İpekDepartment of Industrial Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey.MustafaGöçkenDepartment of Industrial Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey.ErenÖzceylanDepartment of Industrial Engineering, Gaziantep University, Gaziantep, Turkey.Journal Article20210511In this paper, four Simulation Optimization (SO) models are developed by combining simulation and Genetic Algorithm (GA). In the proposed models, optimal values of inventory control parameters and the number of facilities to be opened are determined simultaneously for periodic review and continuous review systems, respectively. Furthermore, single-product and multi-components of Closed-Loop Supply Chain (CLSC) network are created considering two different objective functions of review systems to gain a sustainable competitive advantage for companies. We seek to offer valuable insights for creating robust and user-friendly CLSC network where the forward network includes suppliers, plants, retailers, and customers, and reverse network includes collection centers, disassembly centers, refurbishing centers, and disposal center. The results of this study demonstrated that four SO models have a significant potential to satisfy the customer’s needs since the average service level of the models is at least 81.8%. The total supply chain cost can be decreased at least 3% and at most 22% on average with the proposed continuous review model whose objective is the minimization of differences between the total overordering cost and the total underordering cost (C-D). Furthermore, the total lost sales cost can be improved at least 15% and at most 89% on average with the C-D model.https://scientiairanica.sharif.edu/article_24175_4f372af992d5addcf0ddfbd0868aa95c.pdfSharif University of TechnologyScientia Iranica1026-3098321220250501Optimizing a bi-objective multi-period fish closed-loop supply chain network design by three multi-objective meta-heuristic algorithms2422110.24200/sci.2021.57930.5477ENMaedehFasihiDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.RezaTavakkoli-MoghaddamSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.Seyyed EsmaeilNajafiDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.0000-0002-8734-5436MostafaHajiaghaei-KeshteliTecnologico de Monterrey, Escuela de Ingenieríay Ciencias, Puebla, Mexico.Journal Article20210318Attention to a food Supply Chain (SC) has increased recently due to population growth and increased demand for food. Aquaculture development is advantageous as fish is a crucial constituent of the food basket of households. This study first presents a new bi-objective and multi-period mathematical model of a fish Closed-Loop Supply Chain (CLSC). The model is addressed by utilizing the Multi-Objective Keshtel Algorithm (MOKA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Simulated Annealing (MOSA). The Taguchi method is employed to tune these metaheuristics to attain superior performance, and the ε-constraint method is used in solving small-sized problems to validate them. The results show that the exact method cannot solve large-sized problems.<br />The solutions are compared in terms of different performance metrics. Using the ‘Filtering/Displaced Ideal Solution’ (F/DIS) method, NSGA-II and MOKA with a direct distance of 0.4228 and 0.8976 have the first and second performance ranks, respectively. Also, a case study including a trout CLSC in the north of Iran is investigated. The results and the case study show that the developed model can be applied to the proposed solution approach.https://scientiairanica.sharif.edu/article_24221_fd1a7fb9d1ed55acd080a46103f7964a.pdf