ORIGINAL_ARTICLE How to Estimate the Supplier Fill Rate When the Supply Order and the Supply Lead-time Are Uncertain? 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 2018-08-01 197 206 10.22034/2018.3.2 Modern retail supply chain supply risk bivariate distribution Supplier fill rate Slim Harbi slim.harbi@enicarthage.rnu.tn 1 OASIS Laboratory (ENIT-Tunis), National Engineering School of Carthage, University of Tunis El Manar, Tunisia LEAD_AUTHOR Mohamed Bahroun bahrounm@hotmail.com 2 ACS Laboratory, National Engineering School of Tunis, University of Tunis El Manar, Tunisia AUTHOR Hanen Bouchriha hanen.bouchriha@enit.rnu.tn 3 OASIS Laboratory (ENIT-Tunis), National Engineering School of Carthage, University of Tunis El Manar, Tunisia AUTHOR Aastrup, J. and Kotzab, H. (2009). Analyzing out-of-stock in independent grocery stores: an empirical study, International Journal of Retail and Distribution Management, Vol. 37 (9), pp. 765–789. 1 Bahroun, M. and Harbi, S. (2015). Risk management in the modern retail supply chain: Lessons from a case study and literature review, 6th IESM Conference, October 2015, Seville, Spain. 2 Ben Hmida, F., Khalfa, S. and Chelbi, A. (2010). Estimation du budget alloué à la garantie bidimensionnelle d’un produit, MOSIM’10, Hammamet, Tunisia, Mai 2010. 3 Burke, G. J., Carrillo, J. E. and Vakharia, A. J. (2009). Sourcing decisions with stochastic supplier reliability and stochastic demand, Production and Operations Management, Vol. 18(4), pp. 475-484. 4 Chelbi, A., Soussi, M. A. and Ait-Kadi, D. (2009). Bivariate renewal function computational algorithm, International Conference on Industrial Engineering and Systems Management, Montréal, Canada, mai 2009. 5 Chen, M., Xia, Y. and Wang, X. (2010). Managing supply uncertainties through Bayesian information update, IEEE Transactions on Automation Science and Engineering, Vol. 7 (1), pp. 24-36. 6 Chen, J., Lin D. K., and Thomas. D. J. (2003). On the item fill rate for a finite horizon, Operations Research Letters, Vol. 31 (2), pp. 199-123. 7 Chen, F., Li J. and Zhang. H. (2013). Managing downstream competition via capacity allocation, Production and Operations Management, Vol. 22 (2), pp. 426-446. 8 Disney, M.S, Gaalman, G.J.C., Hedenstierna, C. T. and Hosoda, T., (2015). Fill rate in a periodic review order-up-to policy under auto-correlated normally distributed, possibly negative, demand, International Journal of Production Economics, Vol. 170 (2015), pp. 501–512. 9 Du, S., Zhu, Y., Nie, T. Yu, H. (2018). Loss-averse preferences in a two-echelon supply chain with yield risk and demand uncertainty, Oper Res Int J , Vol. 18 , pp. 361-388. 10 Ehrenthal Joachim, C.F. and Stölzle, W., (2013). An examination of the causes for retail stockouts, International Journal of Physical Distribution and Logistics Management, Vol. 43 (1), pp. 54-69. 11 Fernie, J., Sparks, L. and McKinnon, A. C. (2010). Retail logistics in the UK: past, present and future", International Journal of Retail and Distribution Management, Vol. 38 (11/12), pp. 894-914. 12 Gualandris, J., and Kalchschmidt, M., (2015). Supply risk management and competitive advantage: a misfit model, The International Journal of Logistics Management, Vol. 26 (3), pp. 459-478. 13 Gumus, M., Ray S. and Gurnani. H. (2012). Supply-side story: Risks, guarantees, competition, and information asymmetry, Management Science, Vol. 58 (9), pp. 1694-1714. 14 Gupta, D. and Cooper, W. L., (2005). Stochastic comparisons in production yield management, Operations research, Vol. 53 (2), pp. 377–384. 15 Gurnani, H., Ramachandran, K., Ray, S. and Xia, Y. (2013). Ordering behavior under supply risk: An experimental investigation, Manufacturing and Service Operations Management, Vol. 16 (1), pp. 61-75. 16 Guijarro T. E., Cardós, M. and Babiloni, E., (2012). On the exact calculation of the fill rate under discrete demand patterns, European Journal of Operational Research, Vol. 218 (2), pp. 442–444. 17 Hamdi, F., Ghorbel, A., Masmoudi, F. Lionel Dupont (2018). Optimization of a supply portfolio in the context of supply chain risk management: literature review, Journal of Intelligent Manufacturing, Vol. 29(4), pp. 763–788 18 Hosseinia, A. and Zare Mehrjerdi Y. (2016). The Bullwhip Effect on the VMI-Supply Chain Management via System Dynamics Approach: The Supply chain with Two Suppliers and One Retail Channel, Int J Supply Oper Manage (IJSOM), Vol.3(2), pp. 1301-1317. 19 Hopp, W. J., Iravani, S. M. R. and Liu, F. (2009). Managing white-collar work: An operations-oriented survey, Production and Operations Management, Vol. 18 (1), pp. 1-32. 20 Hübner, A. H., Kuhn, H., and Sternbeck, M. G. (2013). Demand and supply chain planning in grocery retail: an operations planning framework, International Journal of Retail and Distribution Management, Vol. 41 (7), pp. 512-530. 21 Käki A, Liesiö J., Salo A. and Talluri S., (2015). Newsvendor decisions under supply uncertainty, International Journal of Production Research, Vol. 53 (5), pp. 1544-1560. 22 Keren, B. (2009). The single-period inventory problem: Extension to random yield from the perspective of the supply chain, Omega, Vol. 37 (4), pp. 801–810. 23 Ketkar M., Vaidya O.S. (2018). A Flexible Approach to Mitigation of Supply Risk through Scenario Modelling. In: Sushil, Singh T., Kulkarni A. (eds) Flexibility in Resource Management. Flexible Systems Management. Springer, Singapore. 24 Larsen, C. and Thorstenson, A., (2014). The order and volume fill rates in inventory control systems, International Journal of Production Economics, Vol. 147, pp. 13–19. 25 Liang, L. and Atkins D. (2013). 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Sourcing under supply disruption with capacity-constrained suppliers, Journal of Advances in Management Research, Vol. 10 (2), pp. 192–205. 31 Schmitt. A.J. (2008). Using Stochastic Supply Inventory Models to Strategically Mitigate Supply Chain Disruption Risk, Logistics Spectrum, Vol. 42 (4), pp. 22-27. 32 Schmitt, A.J., Snyder, L.V. and Shen, Z.M. (2010). Inventory Systems with Stochastic Demand and Supply: Properties and Approximations, European Journal of Operational Research, Vol. 206, pp. 313-328. 33 Sundararajan, R. and Uthayakumar, R. (2015). EOQ model for delayed deteriorating items with shortages and trade credit policy, Int J Supply Oper Manage (IJSOM), Vol.2 (2), pp. 759-783.  34 Song, J. and Zipkin P. (2009). Inventories with multiple supply sources and networks of queues with overflow bypasses, Management Science, Vol. 55, pp. 362-372. 35 Sieke, M. A., Seifert, R. W. and Thonemann. U. W. (2012). Designing service level contracts for supply chain coordination”, Production and Operations Management, Vol. 21 (4), pp. 698-714. 36 Tang, S.Y. and Kouvelis. P. (2011). Supplier diversification strategies in the presence of yield uncertainty and buyer competition, Manufacturing and Service Operations Management, Vol. 13 (4), pp. 439-451. 37 Tannous A. and Seongno Yoon (2018). Summarizing Risk, Sustainability and Collaboration in Global Supply Chain Management, Int J Supply Oper Manage (IJSOM), Vol.5 (2), pp. 192-196. 38 Tempelmeier, H. (2000). Inventory service-levels in the customer supply chain, OR Spectrum, Vol. 22, pp. 361-380. 39 Teller, C., Kotzab, H., Grant, D., and Holweg, C., (2016). The importance of key supplier relationship management in supply chains, International Journal of Retail and Distribution Management, Vol. 44 (2), pp. 1-16. 40 Wang, Y., W. Gilland, B. Tomlin. (2010). 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(2001), A review of bivariate gamma distributions for hydrological application, Journal of Hydrology, Vol.246, pp. 1–18. 46 Yue S. (2001). A bivariate gamma distribution for use in multivariate flood frequency analysis, Hydrological Processes, Vol. 15, pp. 1033–1045. 47 Zipkin. P. H. (2000). Foundations of Inventory Management, McGraw-Hill, New York, 2000. 48 Zsidisin, G.A (2003). Managerial Perceptions of Supply Risk, Journal of Supply Chain Management, Vol.39 (1), pp. 14-25. 49
ORIGINAL_ARTICLE Two-echelon Supply Chain Model for Deteriorating Items in an Imperfect Production System with Advertisement and Stock Dependent Demand under Trade Credit 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 2018-08-01 207 217 10.22034/2018.3.5 Supply chain Deterioration Imperfect production Advertisement and stock dependent demand Trade-credit Sujata Saha sahasujata@outlook.com 1 Department of Mathematics, Mankar College, Mankar, West Bengal, India LEAD_AUTHOR Tripti Chakrabarti triptichakrabarti@gmail.com 2 Department of Basic Sciences, Techno India University, Kolkata, West Bengal, India AUTHOR Annadurai, K. (2013). Integrated Inventory Model for Deteriorating Items with Price-Dependent Demand under Quantity-Dependent Trade Credit. International Journal of Manufacturing Engineering, Vol. 2013, pp. 1–8. 1 Annadurai, K., and Uthayakumar, R. (2013). Two-echelon inventory model for deteriorating items with credit period dependent demand including shortages under trade credit. Optimization Letters, Vol. 7(6), pp. 1227–1249. 2 Chiu, S. W., Gong, D. C., and Wee, H. M. (2004). Effects of random defective rate and imperfect rework process on economic production quantity model. Japan Journal of Industrial and Applied Mathematics, Vol. 21(3), pp. 375–389. 3 Jaggi, C. K., Tiwari, S., and Goel, S. K. (2017). Credit financing in economic ordering policies for non-instantaneous deteriorating items with price dependent demand and two storage facilities. Annals of Operations Research, Vol.  248(1–2), pp. 253–280. 4 Khalilpourazari, S., and Pasandideh, S. H. R. (2016). Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate. In Proceedings of the 12th International Conference on Industrial Engineering, ICIE 2016 (pp. 36–40). 5 Khalilpourazari, S., and Pasandideh, S. H. R. (2018). Multi-objective optimization of multi-item EOQ model with partial backordering and defective batches and stochastic constraints using MOWCA and MOGWO. Operational Research, pp. 1–33. 6 Khalilpourazari, S., Pasandideh, S. H. R., and Ghodratnama, A. (2018). Robust possibilistic programming for multi-item EOQ model with defective supply batches: Whale Optimization and Water Cycle Algorithms. Neural Computing and Applications, pp. 1–28. 7 Kumar, M., Chauhan, A., and Kumar, R. (2012). A Deterministic Inventory Model for Deteriorating Items with Price Dependent Demand and Time Varying Holding Cost under Trade Credit. International Journal of Soft Computing and Engineering, Vol. 2(1), pp. 99–105. 8 Li, L., Wang, Y., and Yan, X. (2013). Coordinating a supply chain with price and advertisement dependent stochastic demand. The Scientific World Journal, Vol.2013, pp. 1-12. 9 Liao, J. J., Huang, K. N., and Chung, K. J. (2012). Lot-sizing decisions for deteriorating items with two warehouses under an order-size-dependent trade credit. International Journal of Production Economics, Vol. 137(1), pp. 102–115. 10 Mahata, G. C. (2012). An EPQ-based inventory model for exponentially deteriorating items under retailer partial trade credit policy in supply chain. Expert Systems with Applications, Vol. 39(3), pp. 3537–3550. 11 Mahata, G. C., and De, S. K. (2016). An EOQ inventory system of ameliorating items for price dependent demand rate under retailer partial trade credit policy. OPSEARCH, Vol. 53(4), pp. 889–916. 12 Maihami, R., Karimi, B., and Ghomi, S. M. T. F. (2017). Pricing and Inventory Control in a Supply Chain of Deteriorating Items: A Non-cooperative Strategy with Probabilistic Parameters. International Journal of Applied and Computational Mathematics, Vol. 3(3), pp. 2477–2499. 13 Manna, A., Dey, J., and Mondal, S. (2014). Three-layer supply chain in an imperfect production inventory model with two storage facilities under fuzzy rough environment. Journal of Uncertainty Analysis and Applications, Vol. 2(1), pp. 1-31. 14 Min, J., Zhou, Y. W., and Zhao, J. (2010). An inventory model for deteriorating items under stock-dependent demand and two-level trade credit. Applied Mathematical Modelling, Vol. 34(11), pp. 3273–3285. 15 on Specifications for Pharmaceutical Preparations, W. H. O. E. C., & others. (2014). Good Manufacturing Practices for Pharmaceutical Products: Main Principles, Annex 2, Forty-Eighth Report. WHO Technical Report Series, Vol. 986, pp. 77–135. 16 Pal, A. K., Bhunia, A. K., and Mukherjee, R. N. (2006). Optimal lot size model for deteriorating items with demand rate dependent on displayed stock level (DSL) and partial backordering. European Journal of Operational Research, Vol. 175(2), pp. 977–991. 17 Pal, S., and Mahapatra, G. S. (2017). A manufacturing-oriented supply chain model for imperfect quality with inspection errors, stochastic demand under rework and shortages. Computers and Industrial Engineering, Vol. 106, pp. 299–314. 18 Palanivel, M., and Uthayakumar, R. (2014). A production-inventory model with variable production cost and probabilistic deterioration. Asia Pacific Journal of Mathematics, Vol. 1(2), pp. 197–212. 19 Panda, D., Kar, S., Maity, K., and Maiti, M. (2008). A single period inventory model with imperfect production and stochastic demand under chance and imprecise constraints. European Journal of Operational Research, Vol. 188(1), pp. 121–139. 20 Roy, M. Das, Sana, S. S., and Chaudhuri, K. (2014). An economic production lot size model for defective items with stochastic demand, backlogging and rework. IMA Journal of Management Mathematics, Vol. 25(2), pp. 159–183. 21 Saha, S., and Chakrabarti, T. (2017). Fuzzy Inventory Model for Deteriorating Items in a Supply Chain System with Price Dependent Demand and Without Backorder. American Journal of Engineering Research (AJER), Vol. 6(6), pp. 183–187. 22 Saha, S., and Chakrabarti, T. (2018a). A Two-Echelon Supply Chain Model for Deteriorating Product with Time-Dependent Demand , Demand-Dependent Production Rate and Shortage. IOSRJournal of Engineering (IOSRJEN), Vol. 8(1), pp. 33–38. 23 Saha, S., and Chakrabarti, T. (2018b). An EPQ Model for Deteriorating Items with Probabilistic Demand andVariable Production Rate. American Journal of Engineering Research (AJER), Vol. 6, pp. 153–161. 24 Sana, S. S. (2010). An economic production lot size model in an imperfect production system. European Journal of Operational Research, Vol. 201(1), pp. 158–170. 25 Sarkar, B. (2013). A production-inventory model with probabilistic deterioration in two-echelon supply chain management. Applied Mathematical Modelling, Vol. 37(5), pp. 3138–3151. 26 Sarkar, B., Cárdenas-Barrón, L. E., Sarkar, M., and Singgih, M. L. (2014). An economic production quantity model with random defective rate, rework process and backorders for a single stage production system. Journal of Manufacturing Systems, Vol. 33(3), pp. 423–435. 27 Shah, N. H., Shah, B. J., and Shah, A. D. (2013). Deteriorating inventory model with finite production rate and two-level of credit financing for stochastic demand, Vol. 50(3), pp. 358–371. 28 Soni, H. N. (2013). Optimal replenishment policies for deteriorating items with stock sensitive demand under two-level trade credit and limited capacity. Applied Mathematical Modelling, Vol. 37(8), pp. 5887–5895. 29 Sundara Rajan, R., and Uthayakumar, R. (2017). Optimal pricing and replenishment policies for instantaneous deteriorating items with backlogging and trade credit under inflation. 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ORIGINAL_ARTICLE An Adjusted Water Cycle Algorithm for Solving Reliability-redundancy Allocation Problems with Cold-standby Components 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 2018-08-01 218 233 10.22034/2018.3.1 Reliability-redundancy allocation problem Cold-standby strategy Reliability optimization Water cycle algorithm Mohammad N. Juybari m.najafi3000@yahoo.com 1 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran AUTHOR Mostafa Abouei Ardakan mabouei2001@gmail.com 2 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran LEAD_AUTHOR Hamed Davari-Ardakani hameddavari@gmail.com 3 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran AUTHOR Abouei Ardakan, M., and Rezvan, M. T. (2018). Multi-objective optimization of reliability–redundancy allocation problem with cold-standby strategy using NSGA-II. Reliability Engineering & System Safety, Vol. 172, pp. 225–238. 1 Abouei Ardakan, M., Sima, M., Zeinal Hamadani, A., and Coit, D. W. (2016). A novel strategy for redundant components in reliability--redundancy allocation problems. IIE Transactions, Vol. 48(11), pp. 1043–1057. 2 Afonso, L. D., Mariani, V. C., and dos Santos Coelho, L. (2013). Modified imperialist competitive algorithm based on attraction and repulsion concepts for reliability-redundancy optimization. Expert Systems with Applications, Vol. 40(9), pp. 3794–3802. 3 Ardakan, M. A., and Hamadani, A. Z. (2014a). Reliability–redundancy allocation problem with cold-standby redundancy strategy. Simulation Modelling Practice and Theory, Vol. 42, pp. 107–118. 4 Ardakan, M. A., and Hamadani, A. Z. (2014b). Reliability optimization of series–parallel systems with mixed redundancy strategy in subsystems. Reliability Engineering & System Safety, Vol. 130, pp. 132–139. 5 Ardakan, M. A., Hamadani, A. Z., and Alinaghian, M. (2015). Optimizing bi-objective redundancy allocation problem with a mixed redundancy strategy. ISA Transactions, Vol. 55, pp. 116–128. 6 Chen, T.-C. (2006). IAs based approach for reliability redundancy allocation problems. Applied Mathematics and Computation, Vol. 182(2), pp. 1556–1567. 7 COIT, D. W. (2001). Cold-standby redundancy optimization for nonrepairable systems. IIE Transactions, Vol. 33(6), pp. 471–478. 8 Dhingra, A. K. (1992). Optimal apportionment of reliability and redundancy in series systems under multiple objectives. IEEE Transactions on Reliability, Vol. 41(4), pp. 576–582. 9 dos Santos Coelho, L. (2009). An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications. Reliability Engineering & System Safety, Vol. 94(4), pp. 830–837. 10 Elsayed, E. A. (2012). Reliability engineering (Vol. 88). John Wiley & Sons. 11 Eskandar, H., Sadollah, A., Bahreininejad, A., and Hamdi, M. (2012). Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, Vol. 110–111, 151–166. 12 Habib, A., Alsieidi, R., and Youssef, G. (2009). Reliability analysis of a consecutive r-out-of-n: F system based on neural networks. Chaos, Solitons & Fractals, Vol. 39(2), pp. 610–624. 13 Hikita, M., Nakagawa, Y., Nakashima, K., and Narihisa, H. (1992). Reliability optimization of systems by a surrogate-constraints algorithm. IEEE Transactions on Reliability, Vol. 41(3), pp. 473–480. 14 Hsieh, T.-J., and Yeh, W.-C. (2012). Penalty guided bees search for redundancy allocation problems with a mix of components in series–parallel systems. Computers & Operations Research, Vol. 39(11), pp. 2688–2704. 15 Hsieh, Y.-C., Chen, T.-C., and Bricker, D. L. (1998). Genetic algorithms for reliability design problems. Microelectronics Reliability, Vol. 38(10), pp. 1599–1605. 16 Hsieh, Y.-C., and You, P.-S. (2011). An effective immune based two-phase approach for the optimal reliability–redundancy allocation problem. Applied Mathematics and Computation, Vol. 218(4), pp. 1297–1307. 17 Kim, H. (2018). Maximization of system reliability with the consideration of component sequencing. Reliability Engineering & System Safety, Vol. 170(Supplement C), pp. 64–72. 18 Kuo, W. (2001). Optimal reliability design: fundamentals and applications. Cambridge university press. 19 Kuo, W., Lin, H.-H., Xu, Z., & Zhang, W. (1987). Reliability optimization with the Lagrange-multiplier and branch-and-bound technique. IEEE Transactions on Reliability, Vol. 36(5), pp. 624–630. 20 Liang, Y.-C., and Smith, A. E. (2004). An ant colony optimization algorithm for the redundancy allocation problem (RAP). IEEE Transactions on Reliability, Vol. 53(3), pp. 417–423. 21 Mellal, M. A., and Zio, E. (2016). A penalty guided stochastic fractal search approach for system reliability optimization. Reliability Engineering & System Safety, Vol. 152(Supplement C), pp. 213–227. 22 Nahas, N., and Nourelfath, M. (2005). Ant system for reliability optimization of a series system with multiple-choice and budget constraints. Reliability Engineering & System Safety, Vol. 87(1), pp. 1–12. 23 Peiravi, A., Karbasian, M., and Abouei Ardakan, M. (2017). K-mixed strategy: A new redundancy strategy for reliability problems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 1748006X17736166. Article in press. 24 Ramirez-Marquez, J. E., Coit, D. W., and Konak, A. (2004). Redundancy allocation for series-parallel systems using a max-min approach. Iie Transactions, Vol. 36(9), pp. 891–898. 25 Tavakkoli-Moghaddam, R., Safari, J., and Sassani, F. (2008). Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm. Reliability Engineering & System Safety, Vol. 93(4), pp. 550–556. 26 Valian, E., Tavakoli, S., Mohanna, S., and Haghi, A. (2013). Improved cuckoo search for reliability optimization problems. Computers & Industrial Engineering, Vol. 64(1), pp. 459–468. 27 Valian, E., and Valian, E. (2013). A cuckoo search algorithm by Lévy flights for solving reliability redundancy allocation problems. Engineering Optimization, Vol. 45(11), pp. 1273–1286. 28 Wang, L., and Li, L. (2012). A coevolutionary differential evolution with harmony search for reliability–redundancy optimization. Expert Systems with Applications, Vol. 39(5), pp. 5271–5278. 29 Wu, P., Gao, L., Zou, D., and Li, S. (2011). An improved particle swarm optimization algorithm for reliability problems. ISA Transactions, Vol. 50(1), pp. 71–81. 30 Yeh, W.-C., and Hsieh, T.-J. (2011). Solving reliability redundancy allocation problems using an artificial bee colony algorithm. Computers & Operations Research, Vol. 38(11), pp. 1465–1473. 31 Zou, D., Gao, L., Li, S., and Wu, J. (2011). An effective global harmony search algorithm for reliability problems. Expert Systems with Applications, Vol. 38(4), pp. 4642–4648. 32
ORIGINAL_ARTICLE A New Mathematical Model for Designing a Municipal Solid Waste System Considering Environmentally Issues 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 2018-08-01 234 255 10.22034/2018.3.4 Location-routing Metaheuristic algorithm Multi-objective problem Municipal solid waste management Masoud Rabbani mrabani@ut.ac.ir 1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran LEAD_AUTHOR Mahdi Mokhtarzadeh mahdi.mokhtarzade@ut.ac.ir 2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran AUTHOR Hamed Farrokhi-Asl hamed.farrokhi@ut.ac.ir 3 School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran AUTHOR Alumur, S. and Kara, B. Y. (2007). A new model for the hazardous waste location-routing problem. Computers & Operations Research, Vol. 34(5), pp. 1406–1423. 1 Aremu, A. S. (2013). In-town tour optimization of conventional mode for municipal solid waste collection. Nigerian Journal of Technology, Vol. 32(3), pp. 443–449. 2 Asefi, H. and Lim, S. (2017). A novel multi-dimensional modeling approach to integrated municipal solid waste management. Journal of Cleaner Production, Vol. 166, pp. 1131–1143. 3 Badran, M. F. and El-Haggar, S. M. (2006). Optimization of municipal solid waste management in Port Said–Egypt. Waste Management, Vol. 26(5), pp. 534–545. 4 Bovea, M. D., Ibáñez-Forés, V., Gallardo, A. and Colomer-Mendoza, F. J. (2010). Environmental assessment of alternative municipal solid waste management strategies. A Spanish case study. Waste Management, Vol. 30(11), pp. 2383–2395. 5 Chatzouridis, C. and Komilis, D. (2012). A methodology to optimally site and design municipal solid waste transfer stations using binary programming. Resources, Conservation and Recycling, Vol. 60, pp. 89–98. 6 Chen, C.-F., Wu, M. C. and Lin, K.-H. (2013). 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ORIGINAL_ARTICLE Challenges and Benefits of Industry 4.0: an overview 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 2018-08-01 256 265 10.22034/2018.3.7 Industry 4.0 benefits implementation Challenges Mamad Mohamed mamad.scm@gmail.com 1 Department of Logistics and Transportation, Superior School of Technology, Ibn Tofail University, Kenitra, Morocco LEAD_AUTHOR Almada-Lobo, F. (2016). The Industry 4.0 Revolution and the Future of Manufacturing Execution Systems (MES). Journal of Innovation Management, Vol. 3(4), pp. 16–21. 1 Andersson, P., and Mattsson, G. L. (2015). Service Innovations Enabled by the Internet of Things. IMP Journal, Vol. 9(1), pp. 85–106. 2 Andrea B., and Jiří T. (2017). Requirements for Education and Qualification of People in Industry 4.0.  27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, Modena, Italy, pp. 2195 – 2202 3 Arroyo, L., Murillo, D., and Val, E. (2017). Trustful and Trustworthy: Manufacturing Trust in the Digital Era. Barcelona: ESADE Roman Llull University Institute for Social Innovation; EY Fundación Espana. Campus barcelona sant cugat Barcelona (Spain) 2017. 4 Baena, F., Guarin, A., Mora, J., Sauza, J., and Retat, S. (2017). Learning factory: The path to ındustry 4.0. Procedia Manufacturing, Vol. 9, pp. 73–80. 5 Barbara M., Gabriele B., Stefano U., Domenico S., Stefano F. (2017). How will change the future engineers' skills in the Industry 4.0 framework? A questionnaire survey. 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, Modena, Italy, pp. 1501-1509. 6 Cordes, F., and Stacey, N. (2017). Is UK Industry Ready for the Fourth Industrial Revolution? Boston, MA: The Boston Consulting Group. Boston, MA, USA, 2017. 7 Deloitte. (2015). Industry 4.0. Challenges and solutions for the digital transformation and use of exponential technologies. 45774A Deloitte Zurich Switzerland 2015. 8 Dennis K., Nicolina P., and Yves-Simon G. (2017). Textile Learning Factory 4.0 – Preparing Germany’s Textile Industry for the Digital Future. 7th Conference on Learning Factories, CLF 2017 Procedia Manufacturing, Vol. 9, pp 214 – 221 9 Fabian S. (2015). The Dynamics of the Digitalization and its implications for companies’ future Enterprise Risk Management systems and organizational structures. MSc. in Business, Language and Culture: Leadership and Management Studies Copenhagen Business School, 2015. 10 Foidl H. and Felderer M. (2016). Research Challenges of Industry 4.0 for Quality Management. in Innovations in Enterprise Information Systems Management and Engineering, Springer, pp. 121–137. 11 Hans-Christian Pfohl, Burak Yahsi and Ta. (2015). The Impact of Industry 4.0 on the Supply Chain. Proceedings of the Hamburg Inter Innovations and Strategies for Logistics, Vol 20 (2015) pp. 30 – 58. 12 Hendrik U., Frank B., Egon M. (2017). Context related information provision in Industry 4.0 environments. 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, Modena, Italy, pp.796-805 13 Hermann, M., Pentek, T. and Otto, B. (2015). Design principles for Industrie 4.0 scenarios: A literature review. HICSS '16 Proceedings of the 2016 49th Hawaii International Conference on System Sciences Vol. 49 (2016), pp 3928-3937. 14 Hofmann, E., and Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, Vol. 89, pp. 23-34. 15 Roeder I., Wang W.M., Muschard B. (2017). In: R. Stark, G. Seliger, J. Bonvoisin (Eds.), “Sustain. Manuf. Challenges, Solut. Implement. Perspect”. Springer International Publishing, Cham, pp. 255–276. 16 Iyer, A. (2018). 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ORIGINAL_ARTICLE An Exploration of Evolutionary Algorithms for a Bi-objective Competitive Facility Location Problem in Congested Systems 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 2018-08-01 266 282 10.22034/2018.3.6 Competitive facility location Congested system Gravity function Multi-objective harmony search NSGA- II Naeme Zarrinpoor zarrinpoor@sutech.ac.ir 1 Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran LEAD_AUTHOR Aboolian, R., Berman, O. and Krass, D. (2007). Competitive facility location and design problem. European Journal of Operational Research, Vol. 182, pp. 40–62. 1 Ahmadi-Javid, A., Seyedi, P. and Syam, S.S. (2017). A survey of healthcare facility location. Computers & Operations Research, Vol.79, pp. 223–263. 2 Benati S. (1999). 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ORIGINAL_ARTICLE Analytical Dimension to Quality Check in Production Process through Control Charts 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 2018-08-01 283 288 10.22034/2018.3.3 Quality control Process capability Process capability index X bar and R chart Sanjiwani Kumar sanjiwani@somaiya.edu 1 Operations Management, KJ Somaiya Institute of Management studies and Research, Mumbai, India LEAD_AUTHOR Black K. (2009). Business Statistics for Contemporary Decision Making, pp. 704, Fourth ed., Noida: John Wiley & Sons (Asia) Pte. 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