Sharif University of TechnologyScientia Iranica1026-309832420250101Capacitated lot-sizing and production sequence problem with setups complexity2264510.24200/sci.2022.57019.5025ENJavadBehnamianDepartment of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, IranSeyed Mohammad TaghiFatemi GhomiDepartment of Industrial Engineering, Amirkabir University of Technology, 15916-34311, Tehran, IranBehroozKarimiDepartment of Industrial Engineering, Amirkabir University of Technology, 15916-34311, Tehran, IranMarziehFadaei MoludiDepartment of Industrial Engineering, Amirkabir University of Technology, 15916-34311, Tehran, IranJournal Article20201104This paper considered the multi-product multi-level multi-period capacitated lot-sizing and sequencing problem with setup carry-over and sequence-dependent family setup times and costs. A formulation of the problem was provided as a mixed-integer nonlinear programming model. To propose this formulation, first, the mixed-integer nonlinear of the problem was linearized, and then converted to a mixed-integer linear program. To solve large-size instances of the problem, then, a lower bound was provided. The results confirmed the efficiency of the proposed model compared to previous models in terms of the runtime and the number of defined variables and constraints. Since this problem is NP-hard and adding other factors such as family setups, setup carry over and sequence-dependent setups increase its complexity, in this paper, a Genetic Algorithm (GA) was applied in large-size dimensions and its results were compared with the proposed lower bound. The numerical results showed that there is no significant difference between the results of the proposed GA and lower bound, and, so, the GA had been<br />able to approach the optimal solution.https://scientiairanica.sharif.edu/article_22645_37fdb2be71aadf4f685b0463c920a7a5.pdfSharif University of TechnologyScientia Iranica1026-309832420250101Multi-objective modeling of relief items distribution network design problem in disaster relief logistics considering transportation system and CO2emission2264610.24200/sci.2022.57132.5080ENMahdiModiriDepartment of Passive Defense, Malek Ashtar University of Technology, Tehran, IranMohammadEskandariDepartment of Passive Defense, Faculty of Passive Defense, Malek Ashtar University of Technology, Tehran, IranSamiraHasanzadehDepartment of Passive Defense, Malek Ashtar University of Technology, Tehran, IranJournal Article20201108The present study aims to propose a multi-objective mixed integer mathematical programming model for designing a relief items distribution network in sustainable disaster relief logistics. The first objective function minimizes the total network costs. Which are divided into two parts: 1. Relief costs including (transportation costs, inventory costs and fixed costs of facilities) 2. Social costs (deprivation cost). The second objective function minimizes the amount of pollution generated by the network. Considering the related literature review, this is the first study that to propose a robust fuzzy optimization approach for relief items distribution network design problem considering environmental (CO2 emission), social (deprivation cost) and economic impacts under reliability and uncertainty. Then, the multi-objective model was solved using the multi-choice goal programming. To indicate the validity of the proposed model, a case study was evaluated based on real data (2019 flood in Sari city, Mazandaran province). Using the proposed model, decision-makers and managers are able to make strategic and tactical decisions with the least cost and time, and in relief planning can enhance the structure of distribution networks and inventory and reduce victims’ dissatisfaction.https://scientiairanica.sharif.edu/article_22646_70465ab4c849a4edf8a79b7746941821.pdfSharif University of TechnologyScientia Iranica1026-309832420250101Robustness of shape parameter for Erlang and Weibull bayesian acceptance sampling plans2267510.24200/sci.2022.56796.4914ENBushraBibiDepartment of Statistics, Quaid-i-Azam University, Islamabad 45320, PakistanSajidAliDepartment of Statistics, Quaid-i-Azam University, Islamabad 45320, PakistanIsmailShahDepartment of Statistics, Quaid-i-Azam University, Islamabad 45320, PakistanJournal Article20200920This article assesses the robustness of shape parameter for Bayesian acceptance sampling plans assuming Erlang and Weibull distributions. In particular, the prior information on the parameter is combined assuming di erent loss functions to derive different sampling plans. The cost model for the group sampling plans is studied by satisfying the constraints of producer's and consumer's risks for the Weibull sampling. The single sampling plan is compared with the group sampling plan and the results suggest that the group sampling plan performs better than the single sampling in terms of cost. It<br />is noticed that the shape parameters of Erlang and Weibull distributions are not robust as claimed in the literature.https://scientiairanica.sharif.edu/article_22675_bcb95298c136f49ab6d2e22d41858c7e.pdfSharif University of TechnologyScientia Iranica1026-309832420250101A multi-product multi-layer urban freight distribution problem solved using a hybrid metaheuristic procedure2268110.24200/sci.2022.57342.5191ENCristian GiovannyGomez-MarinQuality and Production Engineering, Instituto Tecnológico Metropolitano, Medellín, ColombiaConrado AugustoSerna-UranQuality and Production Engineering, Instituto Tecnológico Metropolitano, Medellín, ColombiaJulian AndresZapata-CortesFundación Universitario CEIPA, School of Management, Sabaneta, ColombiaMartin DarioArango-SernaDepartment of Organization Engineering, Universidad Nacional de Colombia, Medellín, ColombiaJournal Article20201216The pick-up and delivery routing problem has received special attention thanks to its application to urban freight distribution processes. However, due to the multiple levels involved in those processes, modeling and analyzing urban distribution networks in urban contexts are complex tasks. As a result, efficient and robust solution methods should be proposed according to the dynamic and uncertain conditions that characterize this type of problems. This article presents a new formulation for the pick-up and delivery problem in a logistics distribution network composed of 3 levels: n: 1: m (n suppliers, 1 Urban Consolidation Center (UCC), and m customers). In addition, an algorithm based on a Greedy Randomized Adaptive Search Procedure (GRASP) heuristic and 2-opt algorithm was implemented here to find solutions to problem, which were compared with the results of the same algorithm for a two-layer Vehicle Routing Problem (VRP) in several instances. Thus, the proposed procedure achieved a 22% improvement over such algorithm.https://scientiairanica.sharif.edu/article_22681_70048afe2cf03c76c399ab629e61e967.pdfSharif University of TechnologyScientia Iranica1026-309832420250101Credit policy for an inventory model of a deteriorating item having variable demand considering default risk2296810.24200/sci.2022.56218.4607ENRituparnaMondal- Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India.
- School of Applied Science and Humanities, Haldia Institute of Technology, Haldia, West Bengal 726157, IndiaPrasenjitPramanikDepartment of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore,
Paschim-Medinipur, West Bengal 721102, IndiaRanjan KumarJanaDepartment of Mathematics, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, IndiaManas KumarMaitiDepartment of Mathematics, Mahishadal Raj College, Mahishadal, Purba-Medinipur, West Bengal 721628, IndiaJournal Article20200630In this study, a supplier-retailer-customer supply chain has been proposed for a deteriorating item with expiration time and dynamic deterioration rate. Here, the supplier adopt full credit policy for the retailer to enhance the retailer's order volume. This facility influences the retailer to provide some partial credit opportunity to the customers to boost the demand. For this credit policy, the retailer always faces a risk due to defaulters, which is termed as default credit risk. The default credit risk is considered in more realistic manner, which depends on the customers' partial credit period and credit amount. The market demand is influenced by customers' credit amount, customers' credit period and retail price of the item. Optimal decision is searched by maximizing the average profit of the system. For the search process, an artificial bee colony algorithm is implemented, tested and used. Illustration of the model is done with some hypothetical examples.https://scientiairanica.sharif.edu/article_22968_b6c4ada96c841d3d419e206743171ecd.pdfSharif University of TechnologyScientia Iranica1026-309832420250101Course timetabling in medical universities given physicians' educational and clinical tasks2257510.24200/sci.2021.57410.5226ENParyaHeidariDepartment of Industrial Engineering, University of Kurdistan, P.O. Box 66177-15175, Sanandaj, IranJamalArkatDepartment of Industrial Engineering, University of Kurdistan, P.O. Box 66177-15175, Sanandaj, IranBehzadMohsenpourDepartment of Infectious Disease, Kurdistan University of Medical Sciences, Sanandaj, IranJournal Article20201225The physician assignment and course timetabling problem at medical universities is a generalized version of the academic timetabling problem. This problem entails assigning courses, educational and clinical tasks to physician faculty members over a semester or academic year. The problem of timetabling academic courses and scheduling physicians in a hospital has been investigated independently in previous studies in this field. These two fields of research are brought together in this article through the presentation of a multi-objective Mixed-Integer Linear Programming (MILP) model. The proposed model is based on two optimization criteria: minimizing workload imbalance and maximizing physician preferences. The model is applied to a case study involving the assignment of physicians to courses, educational and clinical tasks at Kurdistan University of Medical Sciences' Department of Infectious Diseases. Pareto solutions are obtained using an enhanced version of the augmented epsilon constraint implemented in the General Algebraic Modeling System (GAMS) optimization software; one is selected as the most desirable solution using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The proposed model is generic and could be adapted for use in other departments or medical schools.https://scientiairanica.sharif.edu/article_22575_214d902b7648c1385384642aadce5564.pdfSharif University of TechnologyScientia Iranica1026-309832420250101Assessing human performance influencing factors through LINMAP and Bayesian belief networks2281510.24200/sci.2022.56137.4571ENMahdiKarbasianDepartment of Industrial Engineering, Malek Ashtar University of Technology, Tehran, IranBehroozKhaliliDepartment of Industrial Engineering, Malek Ashtar University of Technology, Tehran, IranCheshmavarAfraseabDepartment of Mechanical and Aerospace Engineering, Malek Ashtar University of Technology, Tehran, IranMazdakKhodadadi KarimvandDepartment of Industrial Engineering, Na.C., Islamic Azad University, Najafabad, IranJournal Article20200608This study aims to identify and rank the Performance Influencing Factors (PIFs), which cause errors in human operations, by analyzing the failure weights and ranks of the tasks performed by every operator. Assessing these factors can mitigate human errors (HEs) and improve safety, efficiency, and job satisfaction. The Linear programming techniques for Multidimensional Analysis of Preference (LINMAP) and Bayesian Belief Networks (BBNs) were employed to analyze an aircraft tire manufacturing industry. In this method, all operators of workshops were evaluated. According to the data analysis, each operator’s tasks were weighted, and the potential error rate of each task was determined. PIFs for each workshop were then ranked and prioritized so that the most effective factors could easily be distinguished in order to identify the tasks where the operators had the highest rates of failure. The probability of HE was then obtained. In a predictive model, it is possible to determine when an error occurs and which factors are the most effective in its occurrence. This paper proposes an approach to the easy, inexpensive, and rapid classification of PIFs by determining their correlations through conditional possibilities. The proposed approach is capable of classifying not only PIFs but also the PIF-related tasks with the greatest effects.https://scientiairanica.sharif.edu/article_22815_97ebf8ca5fd7ccc4dd1e2e827dc2c7bb.pdfSharif University of TechnologyScientia Iranica1026-309832420250101A hybrid robust optimization and simulation model to establish temporary emergency stations for earthquake relief2286110.24200/sci.2022.57123.5075ENSogolMousaviDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranSeyed MojtabaSajadiSchool of Strategy and Leadership, Faculty of Business and Law, Coventry University, Coventry, UKAkbarAlemTabrizDepartment of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, IranSeyyed EsmailNajafiDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranJournal Article20201126Earthquakes pose a constant threat to human communities. A key step in improving preparedness against such disasters is to determine the optimal location of Temporary Emergency Stations (TESs) and allocate them to affected areas. Decisions in the preparedness phase ensure optimal performance by TESs and minimize potential delays in rescue operations. During crises, TESs have a significant role in minimizing human causalities. In this research, a robust simulation-optimization approach is proposed to ensure appropriate planning in the preparedness phase. We develop a mathematical model for simultaneous and hierarchical location-allocation of the injured to the available medical facilities under disaster conditions. Since natural disasters are inherently unpredictable, the uncertainty of the data should inevitably be taken into account. We thus employ a Robust Optimization (RO) technique to tackle the uncertainty in the number of the injured and use simulation to create the first seven days of the crisis and determine the optimal capacities of medical facilities. The findings indicate that by eliminating the unnecessary transfer of mildly-injured victims to high-level medical facilities, the model causes a 15% reduction in treatment costs.https://scientiairanica.sharif.edu/article_22861_104b68446ad787e4d0eeba3d55e1c4cc.pdfSharif University of TechnologyScientia Iranica1026-309832420250101An integrated decision-making framework for selecting the best strategies of water resources management in pandemic emergencies2309510.24200/sci.2023.57127.5077ENMahdiehTavakoliSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranS. AliTorabiSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranMohsenGhanavati-NejadSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranSinaNayeriSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranJournal Article20201115In recent years, due to COVID 19 pandemic that has resulted in an unpredictable increase in water consumption, the global concerns about water resources management have been increased. Furthermore, it seems essential to focus on strategies enabling to decrease water consumption. So, the aim of this study is to identify and prioritize the potential strategies of water resources management during such pandemic. To do so, we develop a hybrid decision-making approach. At first, the potential strategies are identified by Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis while the relevant criteria are identified based on the literature review and experts’ opinions. Afterwards, potential interrelationships between criteria are determined using fuzzy DEMATEL. Then, an integrated FBWM-FANP method is applied to calculate the global weights of criteria. Eventually, the fuzzy VIKOR is utilized to rank the potential strategies. Based on the obtained results, efficiency and economic measures are the most important criteria for selecting the strategies related to water resource management in COVID-19 pandemic. The strategy of advertising and informing about correct water consumption is the best strategy which indicates the power of advertising while it could be economic and efficient either.https://scientiairanica.sharif.edu/article_23095_cd9516868afb88e7695a971ce8623dcc.pdf