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