One of the fundamental problems in supply chain management is to design the effective inventory control policies for models with stochastic demands because efficient inventory management can both maintain a high customers’ service level and reduce unnecessary over and under-stock expenses which are significant key factors of profit or loss of an organization. In this study, a new formulation of an inventory system is analyzed under discrete Markov-modulated demand. We employ simulation-based optimization that combines simulated annealing pattern search and ranking selection (SAPS&RS) methods to approximate near-optimal solutions of this problem. After determining the values of demand, we employ novel approach to achieve minimum cost of total SCM (Supply Chain Management) network. In our proposed approach, hybrid improved cuckoo search algorithm (ICS) and genetic algorithm (GA) are presented as main platform to solve this problem. The computational results demonstrate the effectiveness and applicability of the proposed approach.
Mots clés : Improved cuckoo search algorithm, genetic algorithm, Markov chain Monte Carlo procedure, stochastic demand, inventory control
@article{RO_2018__52_2_473_0,
author = {Jamali, Gholamreza and Sana, Shib Sankar and Moghdani, Reza},
title = {Hybrid improved cuckoo search algorithm and genetic algorithm for solving {Markov-modulated} demand},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {473--497},
publisher = {EDP-Sciences},
volume = {52},
number = {2},
year = {2018},
doi = {10.1051/ro/2017076},
mrnumber = {3880539},
language = {en},
url = {http://www.numdam.org/articles/10.1051/ro/2017076/}
}
TY - JOUR AU - Jamali, Gholamreza AU - Sana, Shib Sankar AU - Moghdani, Reza TI - Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2018 SP - 473 EP - 497 VL - 52 IS - 2 PB - EDP-Sciences UR - http://www.numdam.org/articles/10.1051/ro/2017076/ DO - 10.1051/ro/2017076 LA - en ID - RO_2018__52_2_473_0 ER -
%0 Journal Article %A Jamali, Gholamreza %A Sana, Shib Sankar %A Moghdani, Reza %T Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand %J RAIRO - Operations Research - Recherche Opérationnelle %D 2018 %P 473-497 %V 52 %N 2 %I EDP-Sciences %U http://www.numdam.org/articles/10.1051/ro/2017076/ %R 10.1051/ro/2017076 %G en %F RO_2018__52_2_473_0
Jamali, Gholamreza; Sana, Shib Sankar; Moghdani, Reza. Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand. RAIRO - Operations Research - Recherche Opérationnelle, Tome 52 (2018) no. 2, pp. 473-497. doi : 10.1051/ro/2017076. http://www.numdam.org/articles/10.1051/ro/2017076/
[1] , and , Nature-inspired Cuckoo search algorithm for side lobe uppression in a symmetric linear antenna array. Radioengineering 21 (2012) 865–874.
[2] and , A hybrid algorithm using a genetic algorithm and cuckoo search algorithm to solve the traveling salesman problem and its application to multiple sequence alignment. Int. J. Adv. Sci. Technol. 61 (2013) 29–38. | DOI
[3] , Optimal control of a stochastic production-inventory model with deteriorating items. J. King Saud Univ. Sci. 25 (2013) 7–13. | DOI
[4] , A local search genetic algorithm for the job shop scheduling problem with intelligent agents. Comput. Ind. Eng. 85 (2015) 376–383. | DOI
[5] and , Supply-chain network configuration for product recovery. Prod. Plan. Control 13 (2004) 270–281. | DOI
[6] and , On a stochastic demand jump inventory model. Math. Comput. Model. 50 (2009) 1218–1228. | DOI | MR | Zbl
[7] , and , Managing stochastic demand in an Inventory Routing Problem with transportation procurement. Omega 56 (2015) 112–121. | DOI
[8] , Minimizing inventory costs for capacity-constrained production using a hybrid simulation model. Procedia Comput. Sci. 29 (2014) 759–768. | DOI
[9] , , and , Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst. Appl. 41 (2014) 3538–3560. | DOI
[10] , Adaptive genetic algorithm for lot-sizing problem with self-adjustment operation rate: a discussion. Int. J. Prod. Econ. 123 (2010) 243–245. | DOI
[11] and , Hybrid Metaheuristics Algorithms for Inventory Management Problems, in Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance (2012).
[12] , and An improved solution to replenishment lot size problem with discontinuous issuing policy and rework, and the multi-delivery policy into economic production lot size problem with partial rework. Expert Syst. Appl. 39 (2012) 13540–13546. | DOI
[13] , Inventory control in a centralized distribution network using genetic algorithms: a case study. Comput. Ind. Eng. 87 (2015) 532–539. | DOI
[14] and , Optimal policies for multiechelon inventory problems with Markov-modulated demand. Oper. Res. 49 (2001) 226–234. | DOI | MR | Zbl
[15] and , Optimality of state-dependent (s, S) policies in inventory models with Markov-modulated demand and lost sales. Prod. Oper. Manag. 8 (1999) 183–192. | DOI
[16] , Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219 (2013) 8121–8144. | MR | Zbl
[17] , and , An economic production lot size model for defective items with stochastic demand, backlogging and rework. IMA J. Manag. Math. 25 (2013) 159–183. | MR | Zbl
[18] and , Building knowledge to improve enterprise performance from inventory simulation models. Int. J. Prod. Econ. 134 (2011) 108–113. | DOI
[19] and , A simulation-based optimization approach to a lost sale stochastic inventory mode. Int. J. Oper. Res. Inf. Syst. 3 (2012) 46–63. | DOI
[20] , and , Analyzing a lost-sale stochastic inventory model with Markov-modulated demands: a simulation-based optimization study. J. Manuf. Syst. 38 (2016) 1–12. | DOI
[21] , and , The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man and Cybern. 26 (1996) 29–41. | DOI
[22] and , A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search. Comput. Ind. Eng. 50 (2006) 401–425. | DOI
[23] and , A hybrid simplex search and particle swarm optimization for unconstrained optimization. Eur. J. Oper. Res. 181 (2007) 527–548. | DOI | MR | Zbl
[24] , , , and , The impact of product recovery on logistics network design. Oper. Manag. 10 (2001) 156–173.
[25] , Central force optimization: a new metaheuristic with applications in applied electromagnetics. Progr. Electromagn. Res. 77 (2007) 425–491. | DOI
[26] and , Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17 (2012) 4831–4845. | DOI | MR | Zbl
[27] , and , A new heuristic optimization algorithm: harmony search. Simulation 76 (2001) 60–68. | DOI
[28] and , Multi-echelon vs. single-echelon inventory control policies for low-demand items. Manag. Sci. 40 (1994) 597–602. | DOI | Zbl
[29] , and , Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans. Evol. Comput. 13 (2009) 973–990. | DOI
[30] , , and , New policies for stochastic inventory control models: theoretical and computational results. Oper. Res. (2007) 1–34.
[31] , A new metaheuristic for optimization: optics inspired optimization (OIO). Comput. Oper. Res. 55 (2015) 99–125. | DOI | MR | Zbl
[32] , and , A genetic algorithm approach for solving a closed loop supply chain model: a case of battery recycling. Appl. Math. Model. 34 (2010) 655–670. | DOI | MR | Zbl
[33] and , Particle swarm optimization-neural networks, in Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, USA (1995) 1942–1948.
[34] and , A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – a case study on supply chain model. Appl. Math. Model. 35 (2011) 3905–3917. | DOI | MR | Zbl
[35] , Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys. Rev. E 49 (1994) 4677–4683. | DOI
[36] , and , A Lagrangian relaxation heuristic for solving the multi-echelon, multi-commodity, closed-loop supply chain network design problem. Int. J. Logist. Syst. Manag. 1 (2005) 382–404.
[37] and , A single unit decomposition approach to multiechelon inventory systems. Oper. Res. 56 (2008) 1089–1103. | DOI | MR | Zbl
[38] , and , An enhanced cuckoo search for optimization of bloom filter in spam filtering. Glob. J. Comput. Sci. Technol. 12 (2012) 75–81.
[39] , and , A stochastic inventory model with product recovery. CIRP J. Manuf. Sci. Technol. 6 (2013) 120–127. | DOI
[40] , and , A distribution-free newsvendor problem with nonlinear holding cost. Int. J. Syst. Sci. 46 (2015) 1269–1277. | DOI | Zbl
[41] , Coordination of a socially responsible supply chain using revenue sharing contract. Transp. Res. E: Logist. Transp. Rev. 67 (2014) 92–104. | DOI
[42] , Coordinating two-echelon supply chains under stock and price dependent demand rate. Asia-Pac. J. Oper. Res. 30 (2013) 1250051–20. | DOI | MR | Zbl
[43] , Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22 (2002) 52–67. | DOI
[44] , , and , Quantifying the risk of project delays with a genetic algorithm. Int. J. Prod. Econ. 170 (2015) 34–44. | DOI
[45] and , Inventory models with continuous and Poisson demands and discounted and average costs. Prod. Oper. Manag. 55 (2006) 279–293.
[46] and , Optimal power flow using cuckoo search optimization algorithm. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2 (2013) 4213–4218.
[47] , On two-stage selection procedures and related probability inequalities. Commun. Stat. Theory Methods A 7 (1978) 799–811. | DOI | MR | Zbl
[48] , and , Optimal replenishment order for uncertain demand in three layer supply chain. Econ. Model. 29 (2012) 2274–2282. | DOI
[49] , The stochastic EOQ model with random sales price. Appl. Math. Comput. 218 (2011) 239–248. | MR | Zbl
[50] , Price sensitive demand with random sales price–a newsboy problem. Int. J. Syst. Sci. 43 (2011) 491–498. | DOI | MR | Zbl
[51] , An EOQ model for stochastic demand for limited capacity of own warehouse. Ann. Oper. Res. 233 (2015) 383–399. | DOI | MR | Zbl
[52] , Optimal production lot size and reorder point of a two-stage supply chain while random demand is sensitive with sales teams’ initiatives. Int. J. Syst. Sci. 47 (2016) 450–465. | DOI | MR | Zbl
[53] , and , Collaborative model on the agro-industrial supply chain of cocoa. Cybern. Syst. 48 (2017) 325–347. | DOI
[54] , and , An economic production quantity model with stochastic demand in an imperfect production system. Int. J. Serv. Oper. Manag. 9 (2011) 259–283.
[55] , and , Manufacturing setup cost reduction and quality improvement for the distribution free continuous-review inventory model with a service level constraint. J. Manuf. Syst. 34 (2015) 74–82. | DOI
[56] , , , and , A distribution free newsvendor model with consignment policy and retailer’s royalty reduction. To appear in Int. J. Prod. Res. DOI: (2017). | DOI
[57] , and , A two-warehouse inventory model for non-instantaneous deteriorating items with interval-valued inventory costs and stock-dependent demand under inflationary conditions. To appear in Neural Comput. Appl. DOI: (2017). | DOI
[58] , and , Solving multi-product inventory ship routing with a heterogeneous fleet model using a hybrid cross entropy-genetic algorithm: a case study in Indonesia. Prod. Manuf. Res. 4 (2016) 90–113.
[59] , and , Closed-loop supply chains for spent batteries. Interfaces 33 (2003) 57–71. | DOI
[60] and , Optimal Control Theory: Applications to Management Science and Economic. Kluwer Academic Publisher(2000). | MR | Zbl
[61] , Biogeography-based optimization. IEEE Trans. Evol. Comput. 12 (2008) 702–713. | DOI
[62] , and , Humanitarian logistics network design under mixed uncertainty. Eur. J. Oper. Res. 250 (2016) 239–250. | DOI | MR | Zbl
[63] and , Prediction of occupational risk in the shipbuilding industry using multivariable linear regression and genetic algorithm analysis. Saf. Sci. 83 (2016) 12–22. | DOI
[64] , and , Improved cuckoo search algorithm for feed forward neural network training. Int. J. Artif. Intell. Appl. 2 (2011) 36–43.
[65] and , Cuckoo search via levy flights, in Proceedings of World Congress on Nature & Biologically Inspired Computing, December 2009, India. IEEE Publications, USA (2009) 210–214. | DOI
[66] , and , Model and heuristic algorithm of the joint replenishment problem with complete backordering and correlated demand. Int. J. Prod. Econ. 139 (2012) 33–41. | DOI
Cité par Sources :
