eng Kharazmi University International Journal of Supply and Operations Management 23831359 23832525 2018-08-01 5 3 197 206 10.22034/2018.3.2 2762 مقاله پژوهشی How to Estimate the Supplier Fill Rate When the Supply Order and the Supply Lead-time Are Uncertain? Slim Harbi slim.harbi@enicarthage.rnu.tn 1 Mohamed Bahroun bahrounm@hotmail.com 2 Hanen Bouchriha hanen.bouchriha@enit.rnu.tn 3 OASIS Laboratory (ENIT-Tunis), National Engineering School of Carthage, University of Tunis El Manar, Tunisia ACS Laboratory, National Engineering School of Tunis, University of Tunis El Manar, Tunisia OASIS Laboratory (ENIT-Tunis), National Engineering School of Carthage, University of Tunis El Manar, Tunisia Modern retail supply chains are more and more exposed to risks and uncertainties. The supply risk such as the uncertainty of the supplier fill rate (SFR) directly affects the performance of a retail supply chain. The purpose of this paper is to investigate the supply uncertainty, where the order size and the supply lead-time are considered as decision variables. We aim at developing a more realistic approach to predict the SFR. The review of the relevant literature was the first step performed. We pointed out that while the scientific research on supply risk is growing, the literature lacks an accurate support tool that can predict the SFR. Then, a case study has been conducted in order to have a comprehensive view of the real context of SFR parameters. Accordingly, we propose a new approach for predicting SFR using bivariate normal distribution. We illustrate the proposed approach using a numerical example of a real case study in Tunisia. http://www.ijsom.com/article_2762_ccdf6430b2dd8471dd327054ed6011cc.pdf Modern retail supply chain supply risk bivariate distribution Supplier fill rate eng Kharazmi University International Journal of Supply and Operations Management 23831359 23832525 2018-08-01 5 3 207 217 10.22034/2018.3.5 2766 مقاله پژوهشی Two-echelon Supply Chain Model for Deteriorating Items in an Imperfect Production System with Advertisement and Stock Dependent Demand under Trade Credit Sujata Saha sahasujata@outlook.com 1 Tripti Chakrabarti triptichakrabarti@gmail.com 2 Department of Mathematics, Mankar College, Mankar, West Bengal, India Department of Basic Sciences, Techno India University, Kolkata, West Bengal, India This article presents a two-echelon supply chain model for deteriorating items, consisting of a single manufacturer and a single retailer, where the customer's demand to the retailer depends on advertisement and the displayed stock level of the retailer. Due to the imperfect production system, the manufacturer produces a certain quantity of imperfect items with the perfect items. The manufacturer inspects all the products immediately after production and sells the perfect quality items to the retailer. To entice the retailer to purchase more products from him, the manufacturer offers the retailer a trade-credit policy so that the retailer can get a chance to settle his account before the payment for the products. Finally, a cost function of this model has been derived. Numerical examples have been presented to clarify the applicability of this model and sensitivity analysis with respect to the different parameters involved with the model has been presented to study the effect of change of the parameters on the decision variables. http://www.ijsom.com/article_2766_aaef4a4d494f7af1e1a0212502b29429.pdf Supply chain Deterioration Imperfect production Advertisement and stock dependent demand Trade-credit eng Kharazmi University International Journal of Supply and Operations Management 23831359 23832525 2018-08-01 5 3 218 233 10.22034/2018.3.1 2752 مقاله پژوهشی An Adjusted Water Cycle Algorithm for Solving Reliability-redundancy Allocation Problems with Cold-standby Components Mohammad N. Juybari m.najafi3000@yahoo.com 1 Mostafa Abouei Ardakan mabouei2001@gmail.com 2 Hamed Davari-Ardakani hameddavari@gmail.com 3 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Reliability-redundancy allocation problem (RRAP) is one of the most practical methods used to improve system reliability through performing a tradeoff between reliability and redundancy levels of components. RRAP aims to maximize the overall system reliability by creating a balance between the reliabilities of components and the number of redundant components in each subsystem. In RRAP, redundant components operate in a predetermined order under a redundancy strategy. In this paper, cold standby redundancy strategy is considered for the redundant components. Besides, a penalty guided water cycle algorithm is adjusted for solving the problem. The proposed algorithm is implemented on two famous benchmark problems to evaluate the performance of the proposed approach. Numerical results reveal the superiority of the proposed solution method compared to previous studies. http://www.ijsom.com/article_2752_50f1c6e398e9a6b98cece89588dc623b.pdf Reliability-redundancy allocation problem Cold-standby strategy Reliability optimization Water cycle algorithm eng Kharazmi University International Journal of Supply and Operations Management 23831359 23832525 2018-08-01 5 3 234 255 10.22034/2018.3.4 2764 مقاله پژوهشی A New Mathematical Model for Designing a Municipal Solid Waste System Considering Environmentally Issues Masoud Rabbani mrabani@ut.ac.ir 1 Mahdi Mokhtarzadeh mahdi.mokhtarzade@ut.ac.ir 2 Hamed Farrokhi-Asl hamed.farrokhi@ut.ac.ir 3 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran 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. http://www.ijsom.com/article_2764_54e3f2c62bef137cdaf4d4987d34c71f.pdf Location-routing Metaheuristic algorithm Multi-objective problem Municipal solid waste management eng Kharazmi University International Journal of Supply and Operations Management 23831359 23832525 2018-08-01 5 3 256 265 10.22034/2018.3.7 2767 Challenges and Benefits of Industry 4.0: an overview Mamad Mohamed mamad.scm@gmail.com 1 Department of Logistics and Transportation, Superior School of Technology, Ibn Tofail University, Kenitra, Morocco The aim of this article is to present an overview of industry 4.0. Thus our goal in this research is to give a brief perspective of what Industry 4.0 is, its challenges in today context, and to present how we have to design and implement future business organizations. Numerous researchers have mentioned that implementing industry 4.0 is a response to the current challenges in fast changing environments. Indeed, in order to improve flexibility, reduce costs and offer customized products, companies must redesign their production processes appropriately. After an introduction about the new context phenomenon of “Industry 4.0”, we will provide a comprehensive definition about this new concept and explain the research methodology, after that we will present several points of view about challenges and issues of Industry 4.0, then the most relevant and potential benefits of this new industrial paradigm will be described. Lastly, we will end this paper by drawing a conclusion and future research. http://www.ijsom.com/article_2767_1ed3957e521af286722efb31b2772314.pdf Industry 4.0 benefits implementation Challenges eng Kharazmi University International Journal of Supply and Operations Management 23831359 23832525 2018-08-01 5 3 266 282 10.22034/2018.3.6 2765 مقاله پژوهشی An Exploration of Evolutionary Algorithms for a Bi-objective Competitive Facility Location Problem in Congested Systems Naeme Zarrinpoor zarrinpoor@sutech.ac.ir 1 Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran This paper presents a bi-objective competitive facility location model for congested systems in which entering facilities will compete with the competitors’ facilities for capturing the market share. In the proposed model, customers can chose which facility to patronize based on the gravity function that depends on both the quality of service provider and the travel time to facilities. The proposed model attempts to simultaneously maximize the captured demand by each facility and minimize the total waiting times at the system. To solve the model, two multi-objective evolutionary algorithms, involving a multi-objective harmony search algorithm (MOHS) and a non-dominated sorting genetic algorithm-II (NSGA-II), are proposed. The performance of solution procedures are compared in terms of different performance metrics including generational distance, spacing metric, diversification metric, and number of non-dominated solution. Computational results based on different problem sizes show that in general MOHS outperforms NSGA-II. http://www.ijsom.com/article_2765_4c8acc9eef355e7cbf0f6b2114e036c4.pdf Competitive facility location Congested system Gravity function Multi-objective harmony search NSGA- II eng Kharazmi University International Journal of Supply and Operations Management 23831359 23832525 2018-08-01 5 3 283 288 10.22034/2018.3.3 2763 Analytical Dimension to Quality Check in Production Process through Control Charts Sanjiwani Kumar sanjiwani@somaiya.edu 1 Operations Management, KJ Somaiya Institute of Management studies and Research, Mumbai, India Quality control is of paramount importance to any company in improving the product quality. Due to changing industry standards and competitive issues, embracing quality engineering techniques for strong operations support has become of prime importance to maintain and sustain competitive advantage. In this paper researcher intend to analyze the production line of a product, detect assignable variations in process and calculate the capability of the process using statistical Process Control. Methodology: Statistical Process Control (SPC) is a powerful collection of problem-solving tools useful in achieving manufacturing process stability and improving capability through the reduction of variability. Sample size of 50 measurements with subgroup size 5 is considered in plotting these data points using control charts. Since this is a variable data with subgroup size between 2 to 10, data is plotted with the help of X bar and R chart. Also to conclude on the capability of the process and check instability and level shift Process Capability and Process Capability Index is calculated. Result: The analysis of the process reveals that despite of absence of assignable causes of variation and process capability being more than 1, the process capability index was less than 1 concluding that the process mean has shifted which invites more introspection. http://www.ijsom.com/article_2763_7c3863dd6893e62cbbc3d427b74c0500.pdf Quality control Process capability Process capability index X bar and R chart