Showing 140 results for Ali
Inna Irtyshcheva, Yevheniya Boiko, Olena Pavlenko, Iryna Kramarenko, Kseniia Chumakova, Natalia Hryshyna, Olena Ishchenko, Anastasiia Zubko,
Volume 34, Issue 1 (IJIEPR 2023)
Abstract
The research is devoted to the theoretical and applied
organizational bases to held of the comparative analysis of the economic development of the regions of the Black Sea region. The main purpose of the article is the process of comparative
analysis of economic development of the Black Sea region. The article tests the authors' hypothesis about the adequacy of the indicators defined for evaluation through the proposed number of relative indicators, which in the complex will characterize the achievements of the region in ensuring the economic stability of the regional system, quality of transformation processes and indirectly the conditions created by public authorities for economic development. There is confirmed the dependence of the use of the proposed methodological approaches and the constructed comparative profile of the regions of the region, which can be useful for identifying the strengths and weaknesses of the region, outlining key issues and developing regional development plans and programs. It is determined that the largest vector length in the Mykolaivska region, which indicates that in the region on a number of economic indicators achieved higher results than in other regions of the Black Sea region and on average in other regions of Ukraine during the study period.
Mohd Hafizul Ismail, Nurashikin Saaludin, Basyirah Che Mat, Siti Nur Dina Haji Mohd Ali,
Volume 34, Issue 1 (IJIEPR 2023)
Abstract
The COVID-19 pandemic forced Malaysian Higher Education Institutions to pursue online and distance learning. This study aimed to gain insight into the pre-university students’ acceptance and intention to use the Microsoft Teams (MS Teams) application for online learning platforms during the pandemic. This group of students was chosen because they had just finished high school and their transition from the school system to the university system with online learning will pose many difficulties. The theoretical framework for this study was developed using the Technology Acceptance Model (TAM) with additional facilitating conditions and computer self-efficacy as the external elements. The participants were 180 pre-university students from Universiti Kuala Lumpur Malaysian Institute of Information Technology who had experience using MS Teams during their first semester. With SPSS, the predictive factors on the acceptance of students toward online learning have been explained. The findings also indicate that the proposed TAM-based scale successfully explained the factors predicting intention to use MS Teams during the pandemic. The findings assist researchers and practitioners in developing a more comprehensive view of pre-university students’ acceptance and intention to use MS Teams. Finally, several recommendations have been made, including the implications and limitations of the study at the end of this paper to reference future research.
Ali Salmasnia, Mohammad Reza Maleki, Esmaeil Safikhani,
Volume 34, Issue 2 (IJIEPR 2023)
Abstract
In some applications, the number of quality characteristics is larger than the number of observations within subgroups. Common multivariate control charts to monitor the variability of such high-dimensional processes are unsuitable because the sample covariance matrix is not positive semi-definite and invertible. Moreover, the impact of gauge imprecision on detection capability of multivariate control charts under high-dimensional setting has been clearly neglected in the literature. To overcome these shortcomings, this paper develops a ridge penalized likelihood ratio chart for Phase II monitoring of high-dimensional process in the presence of measurement system errors. The developed control chart departures from the assumption of sparse variability shifts in which the assignable cause can only affects a few elements of the covariance matrix. Then, to compensate for the adverse impact of gauge impression, the developed chart is extended by employing multiple measurements on each sampled item. Simulation studies are carried out to study the impact of imprecise measurements on detectability of the developed monitoring scheme under different shift patterns. The results show that the gauge inability negatively affects the run-length distribution of the developed control chart. It is also found that the extended chart under multiple measurements strategy can effectively reduce the error impact.
Nor Mazlina Ghazali, Aqilah Yusoff, Wan Marzuki Wan Jaafar, Salleh Amat, Edris Aden, Azzahrah Anuar,
Volume 34, Issue 2 (IJIEPR 2023)
Abstract
The research aimed to determine the best components of Malaysia-Counsellor Performance Indicator in measuring the counsellor’s performance in Malaysia. This is the first development phase of the M-CPI. This study involved two type of research designs; quantitative and qualitative approach (Mixed Method). The quantitative data has been obtained from 102 respondents and interview with eight (8) counsellors from different settings. Stratified random sampling technique was utilized to select the respondent and proportional stratification was used to determine the sample size of each stratum. A Need Assessment questionnaire has been developed by the researchers as well as the protocol interview. These two instruments were developed based on the literature reviews of previous instruments that have been invented from the western perspective to measure the performance and competency of counsellors. The results of the study were analysed using the descriptive analysis and thematic analysis. Findings have shown that majority counsellors possessed knowledge and skills in conducting counselling session. Most counsellors in the study demonstrated good interpersonal relationship, interaction, multicultural and religiosity and ethics and professionalism. Through this study, to measure the performance of counsellors, the researchers have found that they must equip themselves with knowledge, skill, interpersonal relationship, interaction, multicultural and religiosity and ethics and professionalism aspects. Based on the interview data, there were new components that have been identified to be added in the Malaysia Counsellor Performance Indicator (M-CPI) which include knowledge (theoretical and knowledge transfer), skills (case management, practical skills and academic/professional writing), interpersonal relationship and interaction, cultural and religiosity, professional roles and expertise, ethics and legality, attitudes and personality, referral and articulate philosophy of profession. In future, research should also focus on the validity and reliability of the components listed in the M-CPI.
Mahdi Rezaei, Ali Salmasnia, Mohammad Reza Maleki,
Volume 34, Issue 3 (IJIEPR 2023)
Abstract
This article develops an integrated model of transmitting strategies and operational activities to enhance the efficiency of supply chain management. As the second objective, this paper aims to improve supply chain performance management (SCPM) by employing proper decision-making approaches. The proposed model optimizes the performance indicator based on SCOR metrics. A process-based method is utilized for high-level decisions, while a mathematical programming method is proposed for low-level decisions. The suggested operational model takes some major supply chain properties such as multiple suppliers, multiple plants, multiple materials, and multiple produced items over several time periods into account. To solve the operational multi-objective optimization model, a goal programming approach is applied. The computational results are explained in terms of a numerical example, and a sensitivity analysis is performed to investigate how the performance of the supply chain is influenced by strategic scenario planning.
Ammar Fadhil Al-Maliki, Moharam Habibnejad Korayem,
Volume 34, Issue 3 (IJIEPR 2023)
Abstract
A computational approach is presented to obtain the optimal path of the end-effector for the 10 DOF bipedal robot to increase its load carrying capacity for a given task from point to point. The synthesizing optimal trajectories problem of a robot is formulated as a problem of trajectory optimization. An Iterative Linear Programming method (ILP) is developed for finding a numerical solution for this nonlinear trajectory. This method is used for determining the maximum dynamic load carrying capacity of bipedal robot walking subjected to torque actuators, stability and jerk limits constraints. First, the Lagrangian dynamic equation should be written to be suitable for the load dynamics which together with kinematic equations are substantial for determining the optimal trajectory. After that, a representation of the state space of the dynamic equations is introduced also the linearized dynamic equations are needed to obtain the numerical solution of the trajectory optimization followed by formulation for the optimal trajectory problem with a maximum load. Finally, the method of ILP and the computational aspect is applied to solve the problem of trajectory synthesis and determine the dynamic load carrying capacity (DLCC) to the bipedal robot for each of the linear and circular path. By implementing on an experimental biped robot, the simulation results were validated.
Ali Qorbani, Yousef Rabbani, Reza Kamranrad,
Volume 34, Issue 4 (IJIEPR 2023)
Abstract
Prediction of unexpected incidents and energy consumption are some industry issues and problems. Single machine scheduling with preemption and considering failures has been pointed out in this study. Its aim is to minimize earliness and tardiness penalties by using job expansion or compression methods. The present study solves this problem in two parts. The first part predicts failures and obtains some rules to correct the process, and the second includes the sequence of single-machine scheduling operations. The failure time is predicted using some machine learning algorithms includes: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, and k-nearest neighbors. Results of comparing the algorithms, indicate that the decision tree algorithm outperformed other algorithms with a probability of 70% in predicting failure. In the second part, the problem is scheduled considering these failures and machine idleness in a single-machine scheduling manner to achieve an optimal sequence, minimize energy consumption, and reduce failures. The mathematical model for this problem has been presented by considering processing time, machine idleness, release time, rotational speed and torque, failure time, and machine availability after repair and maintenance. The results of the model solving, concluded that the relevant mathematical model could schedule up to 8 jobs within a reasonable time and achieve an optimal sequence, which could reduce costs, energy consumption, and failures. Moreover, it is suggested that further studies use this approach for other types of scheduling, including parallel machine scheduling and flow job shop scheduling. Metaheuristic algorithms can be used for larger dimensions.
Hamed Alizadeh, Ali Khavanin, Farahnaz Khajehnasiri, Niloofar Valizadeh, Ali Salehi Sahlabadi,
Volume 34, Issue 4 (IJIEPR 2023)
Abstract
Background: The lighting of the work environment and its quantitative and qualitative characteristics, such as the intensity of the light and the color temperature, as a physical characteristic, have a great impact on the mental health, behavior and performance of people. The physical factors of the work environment, the personality type and behavioral characteristics of people are effective in their efficiency and productivity. Methods: The current research is an interventional and laboratory research which was done in 2022, 35 male students of Tarbiat Modares University were studied. This study was designed in 3 locations with different lighting systems of LED lamps with color temperature of 3000, 4000 and 5000 degrees Kelvin. Stroop test software was used to check cognitive activities and Neo questionnaire was used to determine personality type. Results: The results showed that the average reaction time when facing the LED lamp with a color temperature of 4000 degrees Kelvin in the group of consonant words was the lowest (average response time 601.22 milliseconds) and at a color temperature of 3000 degrees Kelvin in the group of dissonant words the highest value (average 88. 645 milliseconds). The average number of errors in the group of dissonant words was the highest when faced with a color temperature of 3000 degrees Kelvin (the average number of errors was 10.8), the lowest amount of errors was observed in the group of consonant words at a color temperature of 5000 degrees Kelvin (the average number of errors was 2.71 ). Also, according to the obtained results and checking the interference score of the people, which shows the level of their selective attention, it was found that the average interference score at the color temperature of 3000 degrees Kelvin is the highest (average 6.05) and when faced with the color temperature of 4000 degrees Kelvin The lowest value was (average 4.14). The results of investigating the relationship between cognitive activities and the personality type of the subjects studied at different color temperatures showed that there was a negative and significant correlation between the interference score of the personality type of the subjects at a temperature of 3000 degrees Kelvin (P value = 0.33). Also, by examining this relationship at a color temperature of 5000 degrees Kelvin, it was found that there is a negative and significant correlation between the interference score and the interference time (another parameter affecting selective attention) with the personality type of people (P value = 0.42 and 0.38, respectively). = P value) Conclusions: The results of this study showed that the LED lighting system with high color temperature can be effective on people's cognitive performance by reducing errors and increasing attention and reaction time. In order to improve people's cognitive performance, it is suggested to use lighting system with high color temperature in sensitive places.
Yulin Wan, Md Nor Khalil Bin, Biyuan Lyu, Wei Feng,
Volume 34, Issue 4 (IJIEPR 2023)
Abstract
This study aims to conduct a comprehensive analysis of the existing literature on social presence in order to identify significant research works, contribute valuable insights into emerging research areas, and provide an overview of global research trends. The study also aims to assist future researchers in locating relevant information aligned with their research interests. The research employs bibliometric analysis to examine 1962 journal articles published between 1958 and 2022, focusing on various aspects such as publication outputs, co-authorships among authors and affiliated countries, and co-occurrences of author keywords referenced in the Scopus database. Additionally, the study identifies the most active institutions, productive journals, and prolific authors in the field of social presence. Notably, the analysis reveals a consistent increase in cumulative publication numbers from 2014 to 2018, with an annual increment of 100 articles. More than 55% of the total publications originate from researchers based in China and the United States. Moreover, among the top 15 countries, four of their most prolific universities are ranked among the world's top 100 universities. The findings of the bibliometric study highlight that research on social presence predominantly focuses on captivating themes such as e-learning, social media, computer-mediated communication (CMC), and communities of inquiry (CoI). The primary objective of the study is to identify shifts in publication outputs, co-authorships, affiliated countries, and author keywords, thereby unveiling prevailing publication trends within the field of social presence. The scope of the study primarily centres on the identification of trends through bibliometric analysis. The study's findings indicate an upward trend in the publication of articles concerning social presence, which is expected to continue. Furthermore, co-authorship and co-occurrence investigations are undertaken to assess leading countries and frequently employed keywords in the literature.
Ali Mostafaeipour, Ghasem Ghorbannia Ganji, Hasan Hosseini-Nasab, Ahmad Sadegheih,
Volume 34, Issue 4 (IJIEPR 2023)
Abstract
Compared to coal and other fossil fuels, renewable energy (RE) sources emit significantly less carbon dioxide (CO2). In this sense, switching to such sources brings many positive effects to the environment through mitigating climate change, so the terms green energy and clean energy, have been derived from these constructive environmental impacts. Given the utmost importance of RE development, the primary objective of this study was to identify and prioritize the effective RE development strategies in Mazandaran Province, Iran, using different methods, including the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, along with other decision-making techniques. Recruiting a team of 11 industrial and academic experts, the strategies to implement in this region were developed in line with the RE development plans. For this purpose, the Multi-Criteria Decision-Making (MCDM) methodologies were utilized within the gray fuzzy environment to manage the existing uncertainties. The Gray-Additive Ratio Assessment System (Gray ARAS) was further applied to rank the main factors at each level. According to the SWOT analysis and the Stepwise Weight Assessment Ratio Analysis (SWARA) outcomes, among the major factors shaping RE development in Mazandaran Province, Iran, the economic criterion, with the final weight of 0.24, was ranked first; and then the geographical and environmental criteria, having the final weights of 0.23 and 0.19, were put in the second and third places, respectively. In this regard, appropriate location, with the final weight of 0.226, was ranked first; and subsequently pollution reduction and energy production costs, receiving the final weights of 0.103 and 0.094, were the second and third sub-criteria, respectively. As a final point, the validation results based on the Gray-Weighted Aggregated Sum Product Assessment (Gray-WASPAS) and ranking obtained through the Gray-ARAS were confirmed.
Malihe Masoumi, Javad Behnamian,
Volume 35, Issue 1 (IJIEPR 2024)
Abstract
Due to the many applications of the travelling salesman problem, solving this problem has been considered by many researchers. One of the subsets of the travelling salesman problem is the metric travelling salesman problem in which a triangular inequality is observed. This is a crucial problem in combinatorial optimization as it is used as a standard problem as a basis for proving complexity or providing solutions to other problems in this class. The solution is used usually in logistics, manufacturing and other areas for cost minimization. Since this is an NP-hard problem, heuristic and meta-heuristic algorithms seek near-optimal solutions in polynomial time as numerical solutions. For this purpose, in this paper, a heuristic algorithm based on the minimum spanning tree is presented to solve this problem. Then, by generating 20 instances, the efficiency of the proposed algorithm was compared with one of the most famous algorithms for solving the travelling salesman problem, namely the nearest neighbour algorithm and the ant colony optimization algorithm. The results show that the proposed algorithm has good convergence to the optimal solution. In general, the proposed algorithm has a balance between runtime and the solution found compared to the other two algorithms. So the nearest neighbour algorithm has a very good runtime to reach the solution but did not have the necessary convergence to the optimal solution, and vice versa, the ant colony algorithm converges very well to the optimal solution, but, its runtime solution is very longer than the proposed algorithm.
Sunday Elijah, Hanny Zurina Hamzah, Law Siong Hook, Shivee Ranjanee Kaliappan,
Volume 35, Issue 1 (IJIEPR 2024)
Abstract
This article analyses what determines remittance inflows into Malaysia. Using Autoregressive distributive lag (ARDL) approach, the study used time-series data for the period 1987-2018. The study the validated theory that says remittance inflows ought to be encouraged through determinants such as real wages, inflation, financial development, exchange rate among others. Variables like exchange rate, inflation, gross domestic product growth, financial development and real wages significantly determine the remittance received into Malaysia. Precisely, inflation and real wages significantly impacted and positively encouraged remittance inflows into Malaysia from abroad. On the other hand, remittance inflows reacted negatively to gross domestic products growth, exchange rate and financial development. Furthermore, the significance of the determinants differs. Precisely, real wages happen to be additionally responsive in comparison to inflation and the reason is that its elasticity is greater. In addition, both inflation and real wages have great impact in Malaysia. This study recommends that the determinants of migrants’ remittances in the country should be given attention which will strongly aid in employing remittances for the reduction of poverty, rising investment at the national level and therefore, aid in boosting growth and enhancing sustainable development to Malaysia.
Halim Dwi Putra, Iphov Kumala Sriwana , Husni Amani ,
Volume 35, Issue 1 (IJIEPR 2024)
Abstract
The construction industry is one of the high-demand industries related to business and projects. Robust materials management that is subject to inventory management is the highest factor to enhance the Supply chain management (SCM) performance that will indicate the project's success within the complexity of the project. This research aims to measure the performance of Supply Chain Management at PT Cahaya Amal Taqwa as a new housing developer who focuses on subsidized housing that faces a project delay because they have less data documentation and analysis from previous projects. The issue is most newcomer construction projects never analyze and measure their supply chain management (SCM) which leads them to confusion about the project improvement. The research uses the Supply Chain Operational Reference (SCOR) method to know how much inventory management impacts supply chain management performance and how it overcomes the issues. Most studies only measure the SCM performance and show which aspects need to be developed without any scheme of solution offered. This research presents the scheme of improvement for the inventory model and provides forecasting for the whole SCM performance after the implementation of a new model of inventory management. The findings confirm that inventory management significantly impacts the whole supply chain management performance in the construction industry. The development of a solution system brought comprehensive results by classifying KPIs for inventory management and an interdependence network was created to define the new model of inventory system for the solution. This research proves that improving an aspect will impact significantly the whole SCM performance instead of improving KPIs one by one.
Ali Salmasnia, Elahe Heydarnezhad, Hadi Mokhtari,
Volume 35, Issue 2 (IJIEPR 2024)
Abstract
Abstract. One of the important problems in managing construction projects is selecting the best alternative for activities' execution to minimize the project's total cost and time. However, uncertain factors often have negative effects on activity duration and cost. Therefore, it is crucial to develop robust approaches for construction project scheduling to minimize sensitivity to disruptive noise factors. Additionally, existing methods in the literature rarely focus on environmentally conscious construction management. Achieving these goals requires incorporating the project scheduling problem with multiple objectives. This study proposes a robust optimization approach to determine the optimal construction operations in a project scheduling problem, considering time, cost, and environmental impacts (TCE) as objectives. An analytical algorithm based on Benders decomposition is suggested to address the robust problem, taking into account the inherent uncertainty in activity time and cost. To evaluate the performance of the proposed solution approach, a computational study is conducted using real construction project data. The case study is based on the wall of the east coast of Amirabad port in Iran. The results obtained using the suggested solution approach are compared to those of the CPLEX solver, demonstrating the appropriate performance of the proposed approach in optimizing the time, cost, and environment trade-off problem.
Amin Amini, Alireza Alinezhad, Davood Gharakhani,
Volume 35, Issue 2 (IJIEPR 2024)
Abstract
The selection of a sustainable supplier is a multi-criteria decision-making issue that covers a range of criteria (quantitative-qualitative). Selecting the most eco-friendly suppliers requires balancing tangible and intangible elements that may be out of sync. The problem gets more complicated when volume discounts are taken into account, as the buyer needs to decide between two issues: 1) What are the best sustainable suppliers? 2) Which amount needs to be bought from each of the selected eco-friendly suppliers? In current study a combined attitude of best-worst method (BWM) ameliorated via multi-objective mixed integer programming (MOMIP) and rough sets theory is developed. The aim of this work is to contemporaneously ascertain the order quantity allocated to these suppliers in the case of multiple sourcing, multiple products with multiple criteria and with capacity constraints of suppliers and the number of suppliers to employ. In this situation, price reductions are offered by suppliers based on add up commerce volume, not on the amount or assortment of items acquired from them. Finally, a solution approach is proposed to solve the multi-objective model, and the model is demonstrated using a case study in Iran Khodro Company (IKCO). The results indicate that ISACO is the most sustainable supplier and the most orders are assigned to this supplier.
Fakhri Ikhwanul Alifin, Bermawi Priyatna Iskandar, Nadia Fasa, Fransisca Debora,
Volume 35, Issue 2 (IJIEPR 2024)
Abstract
This study develops warranty cost models for repairable products subject to Lemon Laws, encompassing Critical and Non-Critical components forming a multi-component system. Failures can arise naturally or be induced by other components (i.e., failure interaction), defining a lemon if recurrent failures reach a threshold (k) during the warranty period. A lemon declaration triggers a refund or replacement by the manufacturer. Four warranty cost models are proposed from the manufacturer's standpoint, considering failure mechanisms. Increasing failure thresholds in the warranty scheme substantially decreases warranty cost rates. For instance, a threshold (k) of 5 in refund and replacement schemes yields the lowest cost rates of 33.7159 and 25.8249, respectively. Failure interactions escalate total warranty costs; for instance, in a refund scheme (k = 5), costs with failure interaction reach 31.0169 compared to 28.7603 without. Similar trends apply to replacement schemes. Moreover, a lower warranty cost rate will extend the period, indicating regulation fulfillment due to a closer warranty period to the Lemon period. Sensitivity analysis also underscores the role of higher reliability in reducing warranty costs and complying with Lemon Laws. Finally, maintenance strategies and product reliability are emphasized to fulfill Lemon Laws with minimal costs, i.e., fewer warranty claims.
Maryam Arshi, Abdollah Hadi-Vencheh, Adel Aazami, Ali Jamshidi,
Volume 35, Issue 4 (IJIEPR 2024)
Abstract
Linguistic variables (LVs) provide a reliable expression of cognitive information. By inheriting the advantages of LVs, we can express uncertain and incomplete cognitive information in multiple attribute decision-making (MADM), and they do so better than existing methods. In the decision-making process, we can consider decision experts’ (DEs’) bounded rationality, such as cognition toward loss caused by the DEs’ cognitive limitations during the decision process. Therefore, it is necessary to propose a novel cognitive decision approach to handle MADM problems in which the cognitive information is expressed by LVs. In this paper, we employ LVs to represent uncertain and hesitant cognitive information. Then, we propose a mathematical programming approach to solve the MADM problems where attributes or cognitive preferences are not independent. Moreover, the validity and superiority of the presented approach are verified by dealing with a practical problem.
Faikul Umam, Hanifudin Sukri, Ach Dafid, Firman Maolana, Mycel Natalis Stopper Ndruru,
Volume 35, Issue 4 (IJIEPR 2024)
Abstract
Robots are one of the testbeds that can be used as objects for the application of intelligent systems in the current era of Industry 4.0. With such systems, robots can interact with humans through perception (sensors) like cameras. Through this interaction, it is expected that robots can assist humans in providing reliable and efficient service improvements. In this research, the robot collects data from the camera, which is then processed using a Convolutional Neural Network (CNN). This approach is based on the adaptive nature of CNN in recognizing visuals captured by the camera. In its application, the robot used in this research is a humanoid model named Robolater, commonly known as the Integrated Service Robot. The fundamental reason for using a humanoid robot model is to enhance human-robot interaction, aiming to achieve better efficiency, reliability, and quality. The research begins with the implementation of hardware and software so that the robot can recognize human movements through the camera sensor. The robot is trained to recognize hand gestures using the Convolutional Neural Network method, where the deep learning algorithm, as a supervised type, can recognize movements through visual inputs. At this stage, the robot is trained with various weights, backbones, and detectors. The results of this study show that the F-T Last Half technique exhibits more stable performance compared to other techniques, especially with larger input scales (640×644). The model using this technique achieved a mAP of 91.6%, with a precision of 84.6%, and a recall of 80.6%.
Mehdi Dadehbeigi, Ali Taherinezhad, Alireza Alinezhad,
Volume 36, Issue 1 (IJIEPR 2025)
Abstract
Today, data mining and machine learning are recognized as tools for extracting knowledge from large datasets with diverse characteristics. With the increasing volume and complexity of information in various fields, decision-making has become more challenging for managers and decision-making units. Data Envelopment Analysis (DEA) is a tool that aids managers in measuring the efficiency of the units under their supervision. Another challenge for managers involves selecting and ranking options based on specific criteria. Choosing an appropriate multi-criteria decision-making (MCDM) technique is crucial in such cases. With the spread of COVID-19 and the significant financial, economic, and human losses it caused, data mining has once again played a role in improving outcomes, predicting trends, and reducing these losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models. By evaluating decision-making units and utilizing available data, decision-makers are better equipped to make effective decisions in this area. Computational results are presented in detail and discussed in depth.
Khalil Abbal, Mohammed El Amrani, Youssef Benadada,
Volume 36, Issue 1 (IJIEPR 2025)
Abstract
In this paper, we study the Multi-Level Multi-Capacitated Facility Location Problem (ML-MCLP), which was first introduced in 2022 as a double generalization of the Capacitated P-Median Problem (CPMP). The objective of this problem is to determine the optimal facilities to open at each level, and their appropriate capacities to meet customer demands, while minimizing assignment costs. We adopt the Benders Decomposition exact approach, complemented by modern acceleration techniques to enhance convergence speed. The performance of the accelerated BD algorithm is evaluated using a dataset generated based on justified difficulty criteria and data generation methods from the literature. The results showed that hybridization of acceleration techniques, such as subproblem reformulation and cut selection, significantly improves convergence. However, decomposition-based technique proved to be inefficient, particularly due to the structure of the ML-MCLP, and was therefore excluded.
