Production-distribution planning of construction supply chain management under fuzzy random
environment for large-scale construction projects

Jiuping Xu; Pei Wei

doi:10.3934/jimo.2013.9.31

Previous Article
Stability analysis for set-valued vector mixed variational inequalities in real reflexive Banach spaces
JIMO Home
This Issue
Next Article
Inventory policies for a partially observed supply capacity model

January 2013, 9(1): 31-56. doi: 10.3934/jimo.2013.9.31

Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects

Jiuping Xu ^1, and Pei Wei ^1,

1.	State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China, China

Received July 2011 Revised May 2012 Published December 2012

Abstract
Related Papers

In this paper, production-distribution planning in construction supply chain management is investigated. A bi-level model for a production-distribution planning problem under a fuzzy random environment is presented. Construction projects, the leader in the hierarchy, not only control the allocation of items to each depot but are also responsible for ordering items from the manufacturing company. The manufacturing company, the follower in the hierarchy, reacts to these orders by deciding which manufacturing plants and production lines are going to be used. The model presented in this paper is able to handle such practical issues and is solved using an improved Artificial Bee Colony algorithm based on a fuzzy random simulation. A case study is presented to illustrate the effectiveness of the proposed approach. Conclusions and future research directions are discussed.

Keywords: Construction supply chain management, bi-level programming, ABC program., fuzzy random variable, production-distribution planning.

Mathematics Subject Classification: Primary: 90C90, 90B9.

Citation: Jiuping Xu, Pei Wei. Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects. Journal of Industrial & Management Optimization, 2013, 9 (1) : 31-56. doi: 10.3934/jimo.2013.9.31

References:

[1]	R. Arbulu, I. Tommelein, K. Walsh and J. Hershauer, Value stream analysis of re-engineered construction supply chain,, Building Research & Information, 31 (2003), 161. doi: 10.1080/09613210301993. Google Scholar
[2]	G. Avninder, Determining loading dock requirements in production-distribution facilities under uncertainty,, Computers & Industrial Engineering, 57 (2009), 161. doi: 10.1016/j.cie.2008.11.002. Google Scholar
[3]	B. Akay and D. Karaboga, "A modified Artificial Bee Colony Algorithm for Real-Parameter Optimization,", Information Sciences, (2010). Google Scholar
[4]	I. Averbakh, On-line integrated production-distribution scheduling problems with capacitated deliveries,, European Journal of Operational Research, 200 (2010), 377. doi: 10.1016/j.ejor.2008.12.030. Google Scholar
[5]	O. Ben-Ayed, D. E. Boyce and C. E. Blair, A general bilevel linear programming formulation of the network design problem,, Transportation Research Part B: Methodological, 22 (1988), 311. doi: 10.1016/0191-2615(88)90006-9. Google Scholar
[6]	B. Bilgen and I. Ozkarahan, Strategic, tactical and operational production-distribution models: A review,, International Journal of Technology Management, 28 (2004), 151. doi: 10.1504/IJTM.2004.005059. Google Scholar
[7]	D. Barnes-Schuster, Y. Bassok and R. Anupindi, Optimizing delivery lead time/inventory placement in a two-stage production/distribution system,, European Journal of Operational Research, 174 (2006), 1664. doi: 10.1016/j.ejor.2002.08.002. Google Scholar
[8]	B. Bilgen, Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem,, Expert Systems with Applications, 37 (2010), 4488. doi: 10.1016/j.eswa.2009.12.062. Google Scholar
[9]	A. Cox and P. Ireland, Managing construction supply chains: The common sense approach,, Engineering, 9 (2002), 409. Google Scholar
[10]	M. Cheong, R. Bhatnagar and S. Graves, Logistics network design with supplier consolidation hubs and multiple shipment options,, Journal of Industrial and Management Optimization, 3 (2007), 51. doi: 10.3934/jimo.2007.3.51. Google Scholar
[11]	F. Cheng, S. Yang, R. Akella and X. Tang, An integrated approach for selection of service vendors in service supply chain,, Journal of Industrial and Management Optimization, 7 (2011), 907. doi: 10.3934/jimo.2011.7.907. Google Scholar
[12]	H. I. Calvete, C. Galé and M. Oliveros, Bilevel model for production-distribution planning solved by using ant colony optimization,, Computers & Operations Research, 38 (2011), 320. doi: 10.1016/j.cor.2010.05.007. Google Scholar
[13]	K. Das and S. Sengupta, A hierarchical process industry production-distribution planning model,, International Journal of Production Economics, 117 (2009), 402. doi: 10.1016/j.ijpe.2008.12.003. Google Scholar
[14]	O. Dey and D. Chakraborty, A fuzzy random continuous review inventory system,, International Journal of Production Economics, 132 (2011), 101. doi: 10.1016/j.ijpe.2011.03.015. Google Scholar
[15]	S. S. Erenguc, N. C. Simpson and A. J. Vakharia, Integrated production/distribution planning in supply chains: An invited review,, European Journal of Operational Research, 115 (1999), 219. doi: 10.1016/S0377-2217(98)90299-5. Google Scholar
[16]	H. Farvaresh and M. M. Sepehri, A single-level mixed integer linear formulation for a bi-level discrete network design problem,, Transportation Research Part E: Logistics and Transportation Review, 47 (2011), 623. doi: 10.1016/j.tre.2011.02.001. Google Scholar
[17]	S. Heilpern, The expected value of a fuzzy number,, Fuzzy Sets and Systems, 47 (1992), 81. doi: 10.1016/0165-0114(92)90062-9. Google Scholar
[18]	G. Hua, S. Wang and C. K. Chan, A Fractional programming model for internation facility location,, Journal of Industrial and Management Optimization, 5 (2009), 629. doi: 10.3934/jimo.2009.5.629. Google Scholar
[19]	J. S. Hu, H. Zheng, R. Q. Xu, Y. P. Ji and C. Y. Guo, Supply chain coordination for fuzzy random newsboy problem with imperfect quality,, International Journal of Approximate Reasoning, 51 (2010), 771. doi: 10.1016/j.ijar.2010.04.002. Google Scholar
[20]	H. Kwakernaak, Fuzzy random variables-I. definitions and theorems,, Information Science, 15 (1978), 1. doi: 10.1016/0020-0255(78)90019-1. Google Scholar
[21]	H. Kwakernaak, Fuzzy random variables-II. Algorithms and examples for the discrete case,, Information Science, 17 (1979), 253. doi: 10.1016/0020-0255(79)90020-3. Google Scholar
[22]	R. Kruse and K. D. Meyer, "Statistics with Vague Data,", Reidel Publishing Company, (1987). doi: 10.1007/978-94-009-3943-1. Google Scholar
[23]	D. Karaboga, "An Idea Based on Honey Bee Swarm for Numerical Optimization,", Technical Report TR06, (2005). Google Scholar
[24]	D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,, Journal of Global Optimization, 39 (2007), 459. doi: 10.1007/s10898-007-9149-x. Google Scholar
[25]	D. Karaboga and B. Basturk, On the performance of artificial bee colony (ABC) algorithm,, Applied Soft Computing, 8 (2008), 687. doi: 10.1016/j.asoc.2007.05.007. Google Scholar
[26]	Ö. Kabak and F. Ülengin, Possibilistic linear-programming approach for supply chain networking decisions,, European Journal of Operational Research, 209 (2011), 253. doi: 10.1016/j.ejor.2010.09.025. Google Scholar
[27]	K. A. London and R. Kenley, An industrial organization economic supply chain approach for the construction industry: A review,, Construction Management and Economics, 19 (2001), 777. doi: 10.1080/01446190110081699. Google Scholar
[28]	Y. H. Lee and S. H. Kim, Production-distribution planning in supply chain considering capacity constraints,, Computers & Industrial Engineering, 43 (2002), 169. doi: 10.1016/S0360-8352(02)00063-3. Google Scholar
[29]	T. F. Liang,, Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain,, Computers & Industrial Engineering, 55 (2008), 676. doi: 10.1016/j.cie.2008.02.008. Google Scholar
[30]	Y. Lan, R. Zhao and W. Tang, Minimum risk criterion for uncertain production planning problems,, Computers & Industrial Engineering, (2011). Google Scholar
[31]	X. Li, Z. Qin, L. Yang and K. Li, Entropy maximization model for the trip distribution problem with fuzzy and random parameters,, Journal of Computational and Applied Mathematics, 235 (2011), 1906. doi: 10.1016/j.cam.2010.09.004. Google Scholar
[32]	D. F. Pyke and M. A. Cohen, Multiproduct integrated production-distribution systems,, European Journal of Operational Research, 74 (1994), 18. doi: 10.1016/0377-2217(94)90201-1. Google Scholar
[33]	A. M. Sarmiento and R. Nagi, A review of integrated analysis of production-distribution systems,, IIE Transactions, 31 (1999), 1061. doi: 10.1080/07408179908969907. Google Scholar
[34]	H. Selim, C. Araz and I. Ozkarahan, Collaborative production-distribution planning in supply chain: A fuzzy goal programming approach,, Transportation Research Part E: Logistics and Transportation Review, 44 (2008), 396. doi: 10.1016/j.tre.2006.11.001. Google Scholar
[35]	C. J. Vidal and M. Goetschalckx, Strategic production-distribution models: A critical review with emphasis on global supply chain models,, European Journal of Operational Research, 98 (1997), 1. doi: 10.1016/S0377-2217(97)80080-X. Google Scholar
[36]	D. Vila, A. Martel and R. Beauregard, Taking market forces into account in the design of production-distribution networks: A positioning by anticipation approach,, Journal of Industrial and Management Optimization, 2 (2006), 255. Google Scholar
[37]	J. Xu and Z. Zeng, A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries,, Applied Mathematical Modelling., (). doi: 10.1016/j.apm.2011.10.031. Google Scholar
[38]	Q. Liu and J. Xu, A study on facility location-allocation problem in mixed random and fuzzy environment,, Journal of Intelligent Manufacturing, 22 (2011), 389. doi: 10.1007/s10845-009-0297-3. Google Scholar
[39]	J. Xu, X. Zhou and S. Li, A class of chance constrained multi-objective portfolio selection model under fuzzy random environment,, Journal of Optimization Theory and Applications, 150 (2011), 530. Google Scholar
[40]	J. Xu, L. Yao and X. Zhao, A multi-objective chance-constrained network optimal model with random fuzzy coefficients and its application to logistics distribution center location problem,, Fuzzy Optimization and Decision Making, 10 (2011), 255. Google Scholar
[41]	J. Xu, F. Yan ans S. Li, Vehicle routing optimization with soft time windows in a fuzzy random environment,, Transportation Research Part E: Logistics and Transportation Review, 47 (2011), 1075. doi: 10.1016/j.tre.2011.04.002. Google Scholar
[42]	J. Xu and X. Zhou, "Fuzzy-Like Multiple Objective Decision Making,", Springer, (2011). Google Scholar
[43]	M. Yao, J. Lin and C. Yang, An integrated approach for the operations of distribution and lateral transshipment for seasonal products - A case study in household product industry,, Journal of Intdustrial and Management Optimization, 7 (2011), 401. doi: 10.3934/jimo.2011.7.401. Google Scholar
[44]	G. Zhang, J. Shang and W. Li, Collaborative production planning of supply chain under price and demand uncertainty,, European Journal of Operational Research, 215 (2011), 590. doi: 10.1016/j.ejor.2011.07.007. Google Scholar
[45]	J. Zhang and J. Chen, Externality of contracts on supply chains with two suppliers and a common retailer,, Journal of Industrial and Management Optimization, 6 (2010), 795. doi: 10.3934/jimo.2010.6.795. Google Scholar

show all references

References:

[1]	R. Arbulu, I. Tommelein, K. Walsh and J. Hershauer, Value stream analysis of re-engineered construction supply chain,, Building Research & Information, 31 (2003), 161. doi: 10.1080/09613210301993. Google Scholar
[2]	G. Avninder, Determining loading dock requirements in production-distribution facilities under uncertainty,, Computers & Industrial Engineering, 57 (2009), 161. doi: 10.1016/j.cie.2008.11.002. Google Scholar
[3]	B. Akay and D. Karaboga, "A modified Artificial Bee Colony Algorithm for Real-Parameter Optimization,", Information Sciences, (2010). Google Scholar
[4]	I. Averbakh, On-line integrated production-distribution scheduling problems with capacitated deliveries,, European Journal of Operational Research, 200 (2010), 377. doi: 10.1016/j.ejor.2008.12.030. Google Scholar
[5]	O. Ben-Ayed, D. E. Boyce and C. E. Blair, A general bilevel linear programming formulation of the network design problem,, Transportation Research Part B: Methodological, 22 (1988), 311. doi: 10.1016/0191-2615(88)90006-9. Google Scholar
[6]	B. Bilgen and I. Ozkarahan, Strategic, tactical and operational production-distribution models: A review,, International Journal of Technology Management, 28 (2004), 151. doi: 10.1504/IJTM.2004.005059. Google Scholar
[7]	D. Barnes-Schuster, Y. Bassok and R. Anupindi, Optimizing delivery lead time/inventory placement in a two-stage production/distribution system,, European Journal of Operational Research, 174 (2006), 1664. doi: 10.1016/j.ejor.2002.08.002. Google Scholar
[8]	B. Bilgen, Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem,, Expert Systems with Applications, 37 (2010), 4488. doi: 10.1016/j.eswa.2009.12.062. Google Scholar
[9]	A. Cox and P. Ireland, Managing construction supply chains: The common sense approach,, Engineering, 9 (2002), 409. Google Scholar
[10]	M. Cheong, R. Bhatnagar and S. Graves, Logistics network design with supplier consolidation hubs and multiple shipment options,, Journal of Industrial and Management Optimization, 3 (2007), 51. doi: 10.3934/jimo.2007.3.51. Google Scholar
[11]	F. Cheng, S. Yang, R. Akella and X. Tang, An integrated approach for selection of service vendors in service supply chain,, Journal of Industrial and Management Optimization, 7 (2011), 907. doi: 10.3934/jimo.2011.7.907. Google Scholar
[12]	H. I. Calvete, C. Galé and M. Oliveros, Bilevel model for production-distribution planning solved by using ant colony optimization,, Computers & Operations Research, 38 (2011), 320. doi: 10.1016/j.cor.2010.05.007. Google Scholar
[13]	K. Das and S. Sengupta, A hierarchical process industry production-distribution planning model,, International Journal of Production Economics, 117 (2009), 402. doi: 10.1016/j.ijpe.2008.12.003. Google Scholar
[14]	O. Dey and D. Chakraborty, A fuzzy random continuous review inventory system,, International Journal of Production Economics, 132 (2011), 101. doi: 10.1016/j.ijpe.2011.03.015. Google Scholar
[15]	S. S. Erenguc, N. C. Simpson and A. J. Vakharia, Integrated production/distribution planning in supply chains: An invited review,, European Journal of Operational Research, 115 (1999), 219. doi: 10.1016/S0377-2217(98)90299-5. Google Scholar
[16]	H. Farvaresh and M. M. Sepehri, A single-level mixed integer linear formulation for a bi-level discrete network design problem,, Transportation Research Part E: Logistics and Transportation Review, 47 (2011), 623. doi: 10.1016/j.tre.2011.02.001. Google Scholar
[17]	S. Heilpern, The expected value of a fuzzy number,, Fuzzy Sets and Systems, 47 (1992), 81. doi: 10.1016/0165-0114(92)90062-9. Google Scholar
[18]	G. Hua, S. Wang and C. K. Chan, A Fractional programming model for internation facility location,, Journal of Industrial and Management Optimization, 5 (2009), 629. doi: 10.3934/jimo.2009.5.629. Google Scholar
[19]	J. S. Hu, H. Zheng, R. Q. Xu, Y. P. Ji and C. Y. Guo, Supply chain coordination for fuzzy random newsboy problem with imperfect quality,, International Journal of Approximate Reasoning, 51 (2010), 771. doi: 10.1016/j.ijar.2010.04.002. Google Scholar
[20]	H. Kwakernaak, Fuzzy random variables-I. definitions and theorems,, Information Science, 15 (1978), 1. doi: 10.1016/0020-0255(78)90019-1. Google Scholar
[21]	H. Kwakernaak, Fuzzy random variables-II. Algorithms and examples for the discrete case,, Information Science, 17 (1979), 253. doi: 10.1016/0020-0255(79)90020-3. Google Scholar
[22]	R. Kruse and K. D. Meyer, "Statistics with Vague Data,", Reidel Publishing Company, (1987). doi: 10.1007/978-94-009-3943-1. Google Scholar
[23]	D. Karaboga, "An Idea Based on Honey Bee Swarm for Numerical Optimization,", Technical Report TR06, (2005). Google Scholar
[24]	D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,, Journal of Global Optimization, 39 (2007), 459. doi: 10.1007/s10898-007-9149-x. Google Scholar
[25]	D. Karaboga and B. Basturk, On the performance of artificial bee colony (ABC) algorithm,, Applied Soft Computing, 8 (2008), 687. doi: 10.1016/j.asoc.2007.05.007. Google Scholar
[26]	Ö. Kabak and F. Ülengin, Possibilistic linear-programming approach for supply chain networking decisions,, European Journal of Operational Research, 209 (2011), 253. doi: 10.1016/j.ejor.2010.09.025. Google Scholar
[27]	K. A. London and R. Kenley, An industrial organization economic supply chain approach for the construction industry: A review,, Construction Management and Economics, 19 (2001), 777. doi: 10.1080/01446190110081699. Google Scholar
[28]	Y. H. Lee and S. H. Kim, Production-distribution planning in supply chain considering capacity constraints,, Computers & Industrial Engineering, 43 (2002), 169. doi: 10.1016/S0360-8352(02)00063-3. Google Scholar
[29]	T. F. Liang,, Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain,, Computers & Industrial Engineering, 55 (2008), 676. doi: 10.1016/j.cie.2008.02.008. Google Scholar
[30]	Y. Lan, R. Zhao and W. Tang, Minimum risk criterion for uncertain production planning problems,, Computers & Industrial Engineering, (2011). Google Scholar
[31]	X. Li, Z. Qin, L. Yang and K. Li, Entropy maximization model for the trip distribution problem with fuzzy and random parameters,, Journal of Computational and Applied Mathematics, 235 (2011), 1906. doi: 10.1016/j.cam.2010.09.004. Google Scholar
[32]	D. F. Pyke and M. A. Cohen, Multiproduct integrated production-distribution systems,, European Journal of Operational Research, 74 (1994), 18. doi: 10.1016/0377-2217(94)90201-1. Google Scholar
[33]	A. M. Sarmiento and R. Nagi, A review of integrated analysis of production-distribution systems,, IIE Transactions, 31 (1999), 1061. doi: 10.1080/07408179908969907. Google Scholar
[34]	H. Selim, C. Araz and I. Ozkarahan, Collaborative production-distribution planning in supply chain: A fuzzy goal programming approach,, Transportation Research Part E: Logistics and Transportation Review, 44 (2008), 396. doi: 10.1016/j.tre.2006.11.001. Google Scholar
[35]	C. J. Vidal and M. Goetschalckx, Strategic production-distribution models: A critical review with emphasis on global supply chain models,, European Journal of Operational Research, 98 (1997), 1. doi: 10.1016/S0377-2217(97)80080-X. Google Scholar
[36]	D. Vila, A. Martel and R. Beauregard, Taking market forces into account in the design of production-distribution networks: A positioning by anticipation approach,, Journal of Industrial and Management Optimization, 2 (2006), 255. Google Scholar
[37]	J. Xu and Z. Zeng, A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries,, Applied Mathematical Modelling., (). doi: 10.1016/j.apm.2011.10.031. Google Scholar
[38]	Q. Liu and J. Xu, A study on facility location-allocation problem in mixed random and fuzzy environment,, Journal of Intelligent Manufacturing, 22 (2011), 389. doi: 10.1007/s10845-009-0297-3. Google Scholar
[39]	J. Xu, X. Zhou and S. Li, A class of chance constrained multi-objective portfolio selection model under fuzzy random environment,, Journal of Optimization Theory and Applications, 150 (2011), 530. Google Scholar
[40]	J. Xu, L. Yao and X. Zhao, A multi-objective chance-constrained network optimal model with random fuzzy coefficients and its application to logistics distribution center location problem,, Fuzzy Optimization and Decision Making, 10 (2011), 255. Google Scholar
[41]	J. Xu, F. Yan ans S. Li, Vehicle routing optimization with soft time windows in a fuzzy random environment,, Transportation Research Part E: Logistics and Transportation Review, 47 (2011), 1075. doi: 10.1016/j.tre.2011.04.002. Google Scholar
[42]	J. Xu and X. Zhou, "Fuzzy-Like Multiple Objective Decision Making,", Springer, (2011). Google Scholar
[43]	M. Yao, J. Lin and C. Yang, An integrated approach for the operations of distribution and lateral transshipment for seasonal products - A case study in household product industry,, Journal of Intdustrial and Management Optimization, 7 (2011), 401. doi: 10.3934/jimo.2011.7.401. Google Scholar
[44]	G. Zhang, J. Shang and W. Li, Collaborative production planning of supply chain under price and demand uncertainty,, European Journal of Operational Research, 215 (2011), 590. doi: 10.1016/j.ejor.2011.07.007. Google Scholar
[45]	J. Zhang and J. Chen, Externality of contracts on supply chains with two suppliers and a common retailer,, Journal of Industrial and Management Optimization, 6 (2010), 795. doi: 10.3934/jimo.2010.6.795. Google Scholar

[1]	Yi Jing, Wenchuan Li. Integrated recycling-integrated production - distribution planning for decentralized closed-loop supply chain. Journal of Industrial & Management Optimization, 2018, 14 (2) : 511-539. doi: 10.3934/jimo.2017058
[2]	Lan Luo, Zhe Zhang, Yong Yin. Simulated annealing and genetic algorithm based method for a bi-level seru loading problem with worker assignment in seru production systems. Journal of Industrial & Management Optimization, 2019 doi: 10.3934/jimo.2019134
[3]	Tien-Fu Liang, Hung-Wen Cheng. Multi-objective aggregate production planning decisions using two-phase fuzzy goal programming method. Journal of Industrial & Management Optimization, 2011, 7 (2) : 365-383. doi: 10.3934/jimo.2011.7.365
[4]	Zhe Zhang, Jiuping Xu. Bi-level multiple mode resource-constrained project scheduling problems under hybrid uncertainty. Journal of Industrial & Management Optimization, 2016, 12 (2) : 565-593. doi: 10.3934/jimo.2016.12.565
[5]	Yeong-Cheng Liou, Siegfried Schaible, Jen-Chih Yao. Supply chain inventory management via a Stackelberg equilibrium. Journal of Industrial & Management Optimization, 2006, 2 (1) : 81-94. doi: 10.3934/jimo.2006.2.81
[6]	Didier Vila, Alain Martel, Robert Beauregard. Taking market forces into account in the design of production-distribution networks: A positioning by anticipation approach. Journal of Industrial & Management Optimization, 2007, 3 (1) : 29-50. doi: 10.3934/jimo.2007.3.29
[7]	Jia Shu, Jie Sun. Designing the distribution network for an integrated supply chain. Journal of Industrial & Management Optimization, 2006, 2 (3) : 339-349. doi: 10.3934/jimo.2006.2.339
[8]	Jun Pei, Panos M. Pardalos, Xinbao Liu, Wenjuan Fan, Shanlin Yang, Ling Wang. Coordination of production and transportation in supply chain scheduling. Journal of Industrial & Management Optimization, 2015, 11 (2) : 399-419. doi: 10.3934/jimo.2015.11.399
[9]	Suresh P. Sethi, Houmin Yan, Hanqin Zhang, Jing Zhou. Information Updated Supply Chain with Service-Level Constraints. Journal of Industrial & Management Optimization, 2005, 1 (4) : 513-531. doi: 10.3934/jimo.2005.1.513
[10]	Lisha Wang, Huaming Song, Ding Zhang, Hui Yang. Pricing decisions for complementary products in a fuzzy dual-channel supply chain. Journal of Industrial & Management Optimization, 2019, 15 (1) : 343-364. doi: 10.3934/jimo.2018046
[11]	K.H. Wong, Chi Kin Chan, H. W.J. Lee. Optimal feedback production for a single-echelon supply chain. Discrete & Continuous Dynamical Systems - B, 2006, 6 (6) : 1431-1444. doi: 10.3934/dcdsb.2006.6.1431
[12]	Sanjoy Kumar Paul, Ruhul Sarker, Daryl Essam. Managing risk and disruption in production-inventory and supply chain systems: A review. Journal of Industrial & Management Optimization, 2016, 12 (3) : 1009-1029. doi: 10.3934/jimo.2016.12.1009
[13]	Mahmoud Ameri, Armin Jarrahi. An executive model for network-level pavement maintenance and rehabilitation planning based on linear integer programming. Journal of Industrial & Management Optimization, 2020, 16 (2) : 795-811. doi: 10.3934/jimo.2018179
[14]	Xia Zhao, Jianping Dou. Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1263-1288. doi: 10.3934/jimo.2018095
[15]	Zongmin Li, Jiuping Xu, Wenjing Shen, Benjamin Lev, Xiao Lei. Bilevel multi-objective construction site security planning with twofold random phenomenon. Journal of Industrial & Management Optimization, 2015, 11 (2) : 595-617. doi: 10.3934/jimo.2015.11.595
[16]	Amin Aalaei, Hamid Davoudpour. Two bounds for integrating the virtual dynamic cellular manufacturing problem into supply chain management. Journal of Industrial & Management Optimization, 2016, 12 (3) : 907-930. doi: 10.3934/jimo.2016.12.907
[17]	Jun Li, Hairong Feng, Kun-Jen Chung. Using the algebraic approach to determine the replenishment optimal policy with defective products, backlog and delay of payments in the supply chain management. Journal of Industrial & Management Optimization, 2012, 8 (1) : 263-269. doi: 10.3934/jimo.2012.8.263
[18]	Kar Hung Wong, Yu Chung Eugene Lee, Heung Wing Joseph Lee, Chi Kin Chan. Optimal production schedule in a single-supplier multi-manufacturer supply chain involving time delays in both levels. Journal of Industrial & Management Optimization, 2018, 14 (3) : 877-894. doi: 10.3934/jimo.2017080
[19]	Bibhas C. Giri, Bhaba R. Sarker. Coordinating a multi-echelon supply chain under production disruption and price-sensitive stochastic demand. Journal of Industrial & Management Optimization, 2019, 15 (4) : 1631-1651. doi: 10.3934/jimo.2018115
[20]	Xiaohong Chen, Kui Li, Fuqiang Wang, Xihua Li. Optimal production, pricing and government subsidy policies for a closed loop supply chain with uncertain returns. Journal of Industrial & Management Optimization, 2020, 16 (3) : 1389-1414. doi: 10.3934/jimo.2019008

2019 Impact Factor: 1.366

American Institute of Mathematical Sciences

Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects

References:

References:

Tools

Metrics

Other articles
by authors

Tools

Metrics

Other articles
by authors

American Institute of Mathematical Sciences

Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects

References:

References:

Tools

Metrics

Other articlesby authors

Tools

Metrics

Other articlesby authors

Other articles
by authors

Other articles
by authors