A JAVA APPLICATION FOR OPTIMIZATION OF STOCK ALLOCATION IN CONTINGENCY LOGISTICS NETWORKS: COLONOR

Yıl 2019, Cilt: 11 Sayı: 21, 540 - 553, 28.11.2019

Mehmet Miman Hüseyin Alper Tuna Mustafa Deste Gencay Sarıışık

https://doi.org/10.20990/kilisiibfakademik.532274

Öz

Contingencies are
unexpected events or crises that cause a major threat for security and safety
of a particular population. Since they are unexpected events, the demand to
perform contingency operations as well as the supply that can be provided for
this can be modelled through probability distributions. Furthermore, before
contingencies occur one may want to hold stocks beforehand. Based on
interference theory between demand, supply and stocks, one can obtain a
reliability of that site, i.e. probability that the site can perform the
operations assigned based on the availability of resources for these
operations. This study develops a software design as a java application,
COLONOR, which optimizes the stock allocations, i.e. maximizes the reliability
of contingency logistics networks with a given budget and total stocks to
allocate. It assumes exponential demands and supplies, and the network
structure is such that the sites are arranged either in series or parallel. The
software can employ either genetic algorithm or total enumeration techniques to
solve the resulting non-linear, non-separable and non-convex mathematical model
and enables the users to specify the problem’s parameters such as demand and
supply rates, number of sites and network structure as well as the solution
approach.

Anahtar Kelimeler

Contingency Logistics Networks, Stock Allocation, Genetic Algorithm, Contingency Logistics Networks Optimizer

A JAVA APPLICATION FOR OPTIMIZATION OF STOCK ALLOCATION IN CONTINGENCY LOGISTICS NETWORKS: COLONOR

Öz

Beklenmedik durumlar belirli bir nüfusun emniyeti ve
güvenliği için tehdit oluşturan öngörülmemiş olaylar veya krizlerdir. Bunlar
öngörülemediği için beklenmedik durumlar için gerçekleştirilecek operasyonlar
için ihtiyaç ve o anda bunlar için sağlanabilecek tedarik olasılık dağılımları
ile modellenebilir. Bununla birlikte beklenmedik durum oluşmadan, önceden
stoklar tutulmak istenebilir. İhtiyaç, talep ve stok arasındaki etkileşim
teorisine göre beklenmedik durumlarda görevli üssün güvenilirliği, yani bu
anlarda gerçekleştirilmesi gereken operasyonların gerekli malzemenin
mevcudiyetine göre gerçekleştirilebilme olasılığı, hesaplanabilir. Bu çalışma
verilen bütçe ve dağıtılacak stok sayısı ile stokların dağıtımını beklenmedik
durumlar lojistik ağlarının güvenirliğini ençoklayacak şekilde en iyileyen bir
yazılımı java uygulaması olarak, COLONOR, geliştirmektedir.   Uygulama üssel ihtiyaç ve tedarik
varsaymakta olup, görev üslerinin seri veya paralel şekilde konumlandırıldığını
kabul etmektedir. Yazılım neticelenen doğrusal olmayan, ayrılmayan, konveks
olmayan matematiksel modeli çözmek için genetik algoritma veya toplam deneme
tekniklerini kullanmaktadır ve kullanıcıların talep ve tedarik oranları,
operasyonel üs sayısı, ağ yapısı (seri/paralel) ve çözüm tekniği (genetik
algoritma/toplam deneme) gibi problem parametrelerini belirtmesine olanak
sağlamaktadır. 

Anahtar Kelimeler

Beklenmedik Durumlar Lojistik Ağları, Stok Dağıtımı, Genetik Algoritma, Beklenmedik Durumlar Lojistik Ağlar Eniyileyicisi

Kaynakça

• Benyoucef, L., Xie, X. and Tanonkou, G.A. (2013). “Supply chain network design with unreliable suppliers: a Lagrangian relaxation-based approach”, International Journal of Production Research, 51(21): 6435-6454.
• Dağ, E. and Miman, M. (2014). “Beklenmedik durumlar lojistiğinin optimizasyonunda ütopya uzaklık metodu” III. Ulusal Lojistik ve Tedarik Zinciri Kongresi, Trabzon, Türkiye, 680-688.
• Dağ, E. and Miman, M. (2015). “Multi-objective optimization of contingency logistics networks with distorted risks”, International Conference on Value Chain Sustainability, İstanbul, Turkey, 487-492.
• Kang, Y., Batta, R. and Kwon, C. (2014). “Value-at-risk model for hazardous material transportation”, Annals of Operations Research, 222(1): 361-387.
• Kuikka, V. and Suojanen, M. (2014). “Modelling the impact of technologies and systems on military capabilities”, Journal of Battlefield Technology, 17(2): 9-16.
• Liu, D., Gao, Q., Huang, Z.X. and Liu, H. (2012). “Reliability of the supply and demand distribution in spare parts inventory network”, In Quality, Reliability, Risk, Maintenance, and Safety Engineering International Conference (ICQR2MSE), 1415-1417.
• Liu, Y., Huang, H.Z. and Zuo, M.J. (2009). “Optimal selective maintenance for multi-state systems under imperfect maintenance”, In Reliability and Maintainability Symposium, Annual, IEEE, 321-326.
• Miman, M. (2008). Modeling and analysis of the reliability of contingency logistic networks: A multi-dimensional knapsack approach, Phd.Thesis, University of Arkansas.
• Miman, M. and Pohl, E. (2008). “Modelling and analysis of risk and reliability for a contingency logistics supply chain”, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 222(4): 477-494.
• Miman, M. and Pohl, E. (2012). “Multi-objective optimisation of a contingency logistics network through physical programming”, International Journal of Collaborative Enterprise, 3(1): 1-17.
• Naikan, V.N., Datar, A. and Sarmah, S. P. (2015). “A demand–supply interference technique for modeling spare parts inventory policy”, International Journal of Management Science and Engineering Management, 10(3): 191-198.
• Offut, E.J., Kharoufeh, J.P. and Deckro, R.F. (2006) “Distorted risk measures with application to military capability shortfalls”, Military Operation Research, 11(4): 25-39.
• Pohl, E.A., Cassady, C. R. and Kwinn, M. (2003). “A selective maintenance model for serial manufacturing systems involving multiple maintenance actions”, In Proceedings of the 17th International Conference on Production Research, Blacksburg.
• Rajagopalan, R. and Cassady, C.R. (2006). “An improved selective maintenance solution approach”, Journal of Quality in Maintenance Engineering, 12(2): 172-185.
• Thomas, M.U. (2002). “Supply chain reliability for contingency operations”, In Reliability and Maintainability Symposium, Proceedings, Annual, IEEE, 61-67.
• Thomas, M.U. (2004). “Assessing the reliability of a contingency logistics network”, Military Operations Research, 9(1): 33-41.
• Tubis, A., Nowakowski, T. and Werbińska-Wojciechowska, S. (2017). “Supply chain vulnerability and resilience–case study of footwear retail distribution network”, Logistics and Transport, 33(1): 15-24.
• Wang, Q., Yuan, X., Zuo, J., Zhang, J., Hong, J. and Lin, C. (2016). “Optimization of ecological industrial chain design based on reliability theory–a case study”, Journal of Cleaner Production, 124: 175-182.
• Xiong, B., Li, B., Fan, R., Zhou, Q. and Li, W. (2017). “Modeling and simulation for effectiveness evaluation of dynamic discrete military supply chain networks”, Complexity.
• Zhang, H., Wang, M., Tang, M. and Yang, H. (2018). “The reliability measures model of multilayer urban distribution network”, Soft Computing, 22(1): 107-118.
• Zhang, H., Zhu, L. and Xu, S. (2016). “Modeling the city distribution system reliability with bayesian networks to ıdentify ınfluence factors”, Scientific Programming, 16.
• Zhou, Q., Xiong, B., Li, B., Huang, J. and Lu, S. (2016). “Analysing the resilience of military supply network and simulation against disruptions”, International Journal of Engineering Systems Modelling and Simulation, 8(3): 195-204.