American Institute of Mathematical Sciences

Advanced
September  2021, 17(5): 2783-2804. doi: 10.3934/jimo.2020094

Two-stage mean-risk stochastic mixed integer optimization model for location-allocation problems under uncertain environment

Zhimin Liu 1,2, , Shaojian Qu 2,3,4,, , Hassan Raza 2, , Zhong Wu 2, , Deqiang Qu 2, and  Jianhui Du 2,

1. 

School of Mathematics Science, Liaocheng University, Liaocheng, China
2. 

Business School, University of Shanghai for Science and Technology, Shanghai, China
3. 

Nanjing University of Information Science and Technology, Nanjing, China
4. 

National University of Singapore, Singapore

* Corresponding author: Shaojian Qu

Received  August 2019 Revised  February 2020 Published  September 2021 Early access  May 2020

Fund Project: The first author is supported by National Social Science Foundation of China (No. 17BGL083)

The problem of the optimal location-allocation of processing factory and distribution center for supply chain networks under uncertain transportation cost and customer demand are studied. We establish a two-stage mean-risk stochastic 0-1 mixed integer optimization model, by considering the uncertainty and the risk measure of the supply chain. Given the complexity of the model this paper proposes a modified hybrid binary particle swarm optimization algorithm (MHB-PSO) to solve the resulting model, yielding the optimal location and maximal expected return of the supply chain simultaneously. A case study of a bread supply chain in Shanghai is then presented to investigate the specific influence of uncertainties on the food factory and distribution center location. Moreover, we compare the MHB-PSO with hybrid particle swarm optimization algorithm and hybrid genetic algorithm, to validate the proposed algorithm based on the computational time and the convergence rate.

Keywords: Two-stage mean-risk stochastic 0-1 mixed integer optimization, conditional-value-at-risk, location allocation, uncertainty, MHB-PSO.
Mathematics Subject Classification: Primary: 90C15; Secondary: 90C90.
Citation: Zhimin Liu, Shaojian Qu, Hassan Raza, Zhong Wu, Deqiang Qu, Jianhui Du. Two-stage mean-risk stochastic mixed integer optimization model for location-allocation problems under uncertain environment. Journal of Industrial & Management Optimization, 2021, 17 (5) : 2783-2804. doi: 10.3934/jimo.2020094
References:
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A. Chen, Shelter location-allocation model for flood evacuation planning, Journal of the Eastern Asia Society for Transportation Studies, 6 (2005), 4237-4252.   Google Scholar

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X. Chen, A. Shapiro and H. Sun, Convergence analysis of sample average approximation of two-stage stochastic generalized equations, SIAM Journal on Optimization, 29 (2019), 135-161.  doi: 10.1137/17M1162822.  Google Scholar

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J. Kennedy and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, IEEE International Conference on Systems, Man, and Cybernetics, 5 (1997), 4104-4108.   Google Scholar

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B. Li, J. Sun, H. L. Xu and M. Zhang, A class of two-stage distributionally robust games, Journal of Industrial and Management Optimization, 15 (2019), 387–400. doi: 10.3934/jimo.2018048.  Google Scholar

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B. Li, Q. Xun, J. Sun, K. L. Teo, and C. J. Yu, A model of distributionally robust two-stage stochastic convex programming with linear recourse, Applied Mathematical Modelling, 58 (2018), 86–97. doi: 10.1016/j.apm.2017.11.039.  Google Scholar

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I. S. Litvinchev, M. Mata and L. Ozuna, Lagrangian heuristic for the two-stage capacitated facility location problem, Applied and Computational Mathematics, 11 (2012), 137-146.   Google Scholar

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N. Loree and F. Aros-Vera, Points of distribution location and inventory management model for Post-Disaster Humanitarian Logistics, Transportation Research Part E: Logistics and Transportation Review, 116 (2018), 1-24.  doi: 10.1016/j.tre.2018.05.003.  Google Scholar

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A. Moreno, D. Alem, D. Ferreira and A. Clark, An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains, European Journal of Operational Research, 269 (2018), 1050-1071.  doi: 10.1016/j.ejor.2018.02.022.  Google Scholar

[28]

S.M. Mousavi, R. Tavakkoli-Moghaddam and F. Jolai, A possibilistic programming approach for the location problem of multiple cross-docks and vehicle routing scheduling under uncertainty, Engineering Optimization, 45 (2013), 1223-1249.  doi: 10.1080/0305215X.2012.729053.  Google Scholar

[29]

S. Mudchanatongsuk, F. Ordoez and J. Liu, Robust solutions for network design under transportation cost and demand uncertainty, Journal of the Operational Research Society, 59 (2008), 652–662. doi: 10.1057/palgrave.jors.2602362.  Google Scholar

[30]

A. M. Nezhad, H. Manzour and S. Salhi, Lagrangian relaxation heuristics for the uncapacitated single-source multi-product facility location problem, International Journal of Production Economics, 145 (2013), 713-723.  doi: 10.1016/j.ijpe.2013.06.001.  Google Scholar

[31]

N. Noyan, Risk-averse stochastic modeling and optimization, in Recent Advances in Optimization and Modeling of Contemporary Problems, INFORMS: PubsOnLine, 2018,221–254. doi: 10.1287/educ.2018.0183.  Google Scholar

[32]

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[33]

N. Noyan and G. Rudolf, Optimization with multivariate conditional value-at-risk-constraints, Operations Research, 61 (2013), 990-1013.  doi: 10.1287/opre.2013.1186.  Google Scholar

[34]

L. K. Nozick and M. A. Turnquist, A two-echelon inventory allocation and distribution center location analysis, Transportation Research Part E: Logistics and Transportation Review, 37 (2001), 425-441.  doi: 10.1016/S1366-5545(01)00007-2.  Google Scholar

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N. Ricciardi, R. Tadei and A. Grosso, Optimal facility location with random throughput costs, Computers and Operations Research, 29 (2002), 593-607.  doi: 10.1016/S0305-0548(99)00090-8.  Google Scholar

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V. Rico-Ramirez, G. A. Iglesias-Silva, F. Gomez-De la Cruz and S. Hernandez-Castro, Two-stage stochastic approach to the optimal location of booster disinfection stations, Industrial and Engineering Chemistry Research, 46 (2007), 6284-6292.  doi: 10.1021/ie070141a.  Google Scholar

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[45]

K. M. Sim, Y. Guo and B. Shi, BLGAN: Bayesian learning and genetic algorithm for supporting negotiation with incomplete information, IEEE Transactions on Systems Man and Cybernetics Part B, 39 (2009), 198-211.  doi: 10.1109/TSMCB.2008.2004501.  Google Scholar

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H. Soleimani, M. Seyyed-Esfahani and G. Kannan, Incorporating risk measures in closed-loop supply chain network design, International Journal of Production Research, 52 (2014), 1843-1867.  doi: 10.1080/00207543.2013.849823.  Google Scholar

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show all references

References:
[1]

M. Abbasa, Cutting plane method for multiple objective stochastic integer linear programming, European Journal of Operational Research, 168 (2006), 967-984.  doi: 10.1016/j.ejor.2002.11.006.  Google Scholar

[2]

P. Artzner, F. Delbaen, J. M. Eber and D. Heath, Coherent measures of risk, Mathematical Finance, 9 (1999), 203-228.  doi: 10.1111/1467-9965.00068.  Google Scholar

[3]

Z. Bai 2007]Bai2007The, G. Z. Bai, The transportation problem with uncertain transportation cost Google Scholar

[4]

V. Balachandran and S. Jain, Optimal facility location under random demand with general cost structure, Naval Research Logistics Quarterly, 23 (1976), 421-436.  doi: 10.1002/nav.3800230305.  Google Scholar

[5]

S. Baptista, M. I. Gomes and A. P. Barbosa-Povoa, A two-stage stochastic model for the design and planning of a multi-product closed loop supply chain, Computer Aided Chemical Engineering, 30, (2012), 412–416. doi: 10.1016/B978-0-444-59519-5.50083-6.  Google Scholar

[6]

K. S. H. Basta, Computationally efficient solution of a multiproduct, two-stage distribution-location problem, The Journal of the Operational Research Society, 45 (1994), 1316-1323.   Google Scholar

[7]

J. R. Birge and F. Louveaux, Introduction to Stochastic Programming, 2nd edition, Springer, New York, 2011. doi: 10.1007/978-1-4614-0237-4.  Google Scholar

[8]

K. W. Chau, A two-stage dynamic model on allocation of construction facilities with genetic algorithm, Automation in Construction, 13 (2004), 481-490.  doi: 10.1016/j.autcon.2004.02.001.  Google Scholar

[9]

A. Chen, Shelter location-allocation model for flood evacuation planning, Journal of the Eastern Asia Society for Transportation Studies, 6 (2005), 4237-4252.   Google Scholar

[10]

X. Chen, A. Shapiro and H. Sun, Convergence analysis of sample average approximation of two-stage stochastic generalized equations, SIAM Journal on Optimization, 29 (2019), 135-161.  doi: 10.1137/17M1162822.  Google Scholar

[11]

X. Chen, H. L. Sun and H. Xu, Discrete approximation of two-stage stochastic and distributionally robust linear complementarity problems, Mathematical Programming, 177 (2019), 255-289.  doi: 10.1007/s10107-018-1266-4.  Google Scholar

[12]

M. Clerc and J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6 (2002), 58-73.  doi: 10.1109/4235.985692.  Google Scholar

[13]

L. Cooper, Location-Allocation Problems, Operations Research, 11 (1963), 331-343.  doi: 10.1287/opre.11.3.331.  Google Scholar

[14]

L. Gelders, L. Pintelon and L. N. Van Wassenhove, A location-allocation problem in a large Belgian brewery, European Journal of Operational Research, 28 (1987), 196-206.  doi: 10.1016/0377-2217(87)90218-9.  Google Scholar

[15]

D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Co. Inc., Reading, MA, 1989. Google Scholar

[16]

C. A. Irawan and D. Jones, Formulation and solution of a two-stage capacitated facility location problem with multilevel capacities, Annals of Operations Research, 272 (2019), 41-67.  doi: 10.1007/s10479-017-2741-7.  Google Scholar

[17]

R. Ishfaq and C. R. Sox, Hub location-allocation in intermodal logistic networks, European Journal of Operational Research, 210 (2011), 213-230.  doi: 10.1016/j.ejor.2010.09.017.  Google Scholar

[18]

Y. Ji, S. Qu, Z. Wu and Z. Liu, A fuzzy-robust weighted approach for multicriteria bilevel games, IEEE Transactions on Industrial Informatics, (2020). doi: 10.1109/TII.2020.2969456.  Google Scholar

[19]

Q. Jin, X. Hui and Y. Yong, A simulated annealing methodology to multiproduct capacitated facility location with stochastic demand, The Scientific World Journal, (2015), 1–9. Google Scholar

[20]

J. Kennedy and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, IEEE International Conference on Systems, Man, and Cybernetics, 5 (1997), 4104-4108.   Google Scholar

[21]

A. Klose, An lp-based heuristic for two-stage capacitated facility location problems, The Journal of the Operational Research Society, 50 (1999), 157-166.  doi: 10.1016/S0377-2217(99)00300-8.  Google Scholar

[22]

B. Li, J. Sun, H. L. Xu and M. Zhang, A class of two-stage distributionally robust games, Journal of Industrial and Management Optimization, 15 (2019), 387–400. doi: 10.3934/jimo.2018048.  Google Scholar

[23]

B. Li, Q. Xun, J. Sun, K. L. Teo, and C. J. Yu, A model of distributionally robust two-stage stochastic convex programming with linear recourse, Applied Mathematical Modelling, 58 (2018), 86–97. doi: 10.1016/j.apm.2017.11.039.  Google Scholar

[24]

I. S. Litvinchev, M. Mata and L. Ozuna, Lagrangian heuristic for the two-stage capacitated facility location problem, Applied and Computational Mathematics, 11 (2012), 137-146.   Google Scholar

[25]

N. Loree and F. Aros-Vera, Points of distribution location and inventory management model for Post-Disaster Humanitarian Logistics, Transportation Research Part E: Logistics and Transportation Review, 116 (2018), 1-24.  doi: 10.1016/j.tre.2018.05.003.  Google Scholar

[26]

L. R. Medsker, Hybrid Intelligent Systems, Kluwer Academic Publishers, Boston, 1995. Google Scholar

[27]

A. Moreno, D. Alem, D. Ferreira and A. Clark, An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains, European Journal of Operational Research, 269 (2018), 1050-1071.  doi: 10.1016/j.ejor.2018.02.022.  Google Scholar

[28]

S.M. Mousavi, R. Tavakkoli-Moghaddam and F. Jolai, A possibilistic programming approach for the location problem of multiple cross-docks and vehicle routing scheduling under uncertainty, Engineering Optimization, 45 (2013), 1223-1249.  doi: 10.1080/0305215X.2012.729053.  Google Scholar

[29]

S. Mudchanatongsuk, F. Ordoez and J. Liu, Robust solutions for network design under transportation cost and demand uncertainty, Journal of the Operational Research Society, 59 (2008), 652–662. doi: 10.1057/palgrave.jors.2602362.  Google Scholar

[30]

A. M. Nezhad, H. Manzour and S. Salhi, Lagrangian relaxation heuristics for the uncapacitated single-source multi-product facility location problem, International Journal of Production Economics, 145 (2013), 713-723.  doi: 10.1016/j.ijpe.2013.06.001.  Google Scholar

[31]

N. Noyan, Risk-averse stochastic modeling and optimization, in Recent Advances in Optimization and Modeling of Contemporary Problems, INFORMS: PubsOnLine, 2018,221–254. doi: 10.1287/educ.2018.0183.  Google Scholar

[32]

N. Noyan, Risk-averse two-stage stochastic programming with an application to disaster management, Computers and Operations Research, 39 (2012), 541-559.  doi: 10.1016/j.cor.2011.03.017.  Google Scholar

[33]

N. Noyan and G. Rudolf, Optimization with multivariate conditional value-at-risk-constraints, Operations Research, 61 (2013), 990-1013.  doi: 10.1287/opre.2013.1186.  Google Scholar

[34]

L. K. Nozick and M. A. Turnquist, A two-echelon inventory allocation and distribution center location analysis, Transportation Research Part E: Logistics and Transportation Review, 37 (2001), 425-441.  doi: 10.1016/S1366-5545(01)00007-2.  Google Scholar

[35]

W. Ogryczak and A. Ruszczyński, Dual stochastic dominance and related mean-risk models, SIAM Journal on Optimization, 13 (2002), 60-78.  doi: 10.1137/S1052623400375075.  Google Scholar

[36]

M. Padberg, Classical Cuts for Mixed-Integer Programming and Branch-and-Cut, Mathematical Methods of Operations Research, 53 (2001), 173-203.  doi: 10.1007/s001860100120.  Google Scholar

[37]

N. Ricciardi, R. Tadei and A. Grosso, Optimal facility location with random throughput costs, Computers and Operations Research, 29 (2002), 593-607.  doi: 10.1016/S0305-0548(99)00090-8.  Google Scholar

[38]

V. Rico-Ramirez, G. A. Iglesias-Silva, F. Gomez-De la Cruz and S. Hernandez-Castro, Two-stage stochastic approach to the optimal location of booster disinfection stations, Industrial and Engineering Chemistry Research, 46 (2007), 6284-6292.  doi: 10.1021/ie070141a.  Google Scholar

[39]

R. T. Rockafellar, Coherent approaches to risk in optimization under uncertainty, in Tutorials in Operations Research, INFORMS: PubsOnline, 2007, 38–61. doi: 10.1287/educ.1073.0032.  Google Scholar

[40]

R. T. Rockafellar and S. Uryasev, Optimization of conditional value-at-risk, Journal of Risk, 2 (2000), 21-41.  doi: 10.1007/978-1-4757-6594-6_17.  Google Scholar

[41]

A. Ruszczyński, Decomposition methods, in Handbooks in Operations Research and Management Science, Vol. 10, Elsevier Sci. B. V., Amsterdam, 2003,141–211. doi: 10.1016/S0927-0507(03)10003-5.  Google Scholar

[42]

A. Shapiro, D. Dentcheva and A. Ruszczyński, Lectures on Stochastic Programming: Modeling and Theory,, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2009. doi: 10.1137/1.9780898718751.  Google Scholar

[43]

H. D. Sherali and B. M. P. Fraticelli, A modification of Benders' decomposition algorithm for discrete subproblems: An approach for stochastic programs with integer recourse, Journal of Global Optimization, 22 (2002), 319-342.  doi: 10.1023/A:1013827731218.  Google Scholar

[44]

J. Shu and J. Sun, Designing the distribution network for an integrated supply chain, Journal of Industrial and Management Optimization, 2 (2006), 339-349.  doi: 10.3934/jimo.2006.2.339.  Google Scholar

[45]

K. M. Sim, Y. Guo and B. Shi, BLGAN: Bayesian learning and genetic algorithm for supporting negotiation with incomplete information, IEEE Transactions on Systems Man and Cybernetics Part B, 39 (2009), 198-211.  doi: 10.1109/TSMCB.2008.2004501.  Google Scholar

[46]

H. Soleimani, M. Seyyed-Esfahani and G. Kannan, Incorporating risk measures in closed-loop supply chain network design, International Journal of Production Research, 52 (2014), 1843-1867.  doi: 10.1080/00207543.2013.849823.  Google Scholar

[47]

T. R. Stidsen, K. A. Andersen and B. Dammann, A branch and bound algorithm for a class of biobjective mixed integer programs, Management Science, 60 (2014), 1009-1032.  doi: 10.1287/mnsc.2013.1802.  Google Scholar

[48]

H. L. Sun, H. Xu and Y. Wang, Asymptotic analysis of sample average approximation for stochastic optimization problems with joint chance constraints via conditional value at risk and difference of convex functions, Journal of Optimization Theory and Applications, 161 (2014), 257-284.  doi: 10.1007/s10957-012-0127-1.  Google Scholar

[49]

J. Sun, L. Z. Liao and B. Rodrigues, Quadratic two-stage stochastic optimization with coherent measures of risk, Mathematical Programming, 168 (2018), 599-613.  doi: 10.1007/s10107-017-1131-x.  Google Scholar

[50]

S. A. Trusevych, R. H. Kwon and A. K. S. Jardine, Optimizing critical spare parts and location based on the conditional value-at-risk criterion, The Engineering Economist, 59 (2014), 116-135.  doi: 10.1080/0013791X.2013.876795.  Google Scholar

[51]

W. Shih, A branch and bound method for the multiconstraint zero-one knapsack problem, Journal of the Operational Research Society, 30 (1979), 369-378.  doi: 10.2307/3009639.  Google Scholar

[52]

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Figure 1.  Network structure of location-allocation supply chain
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Figure 2.  Two-stage process of location-allocation problem
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Figure 3.  Location of flour factory, food factory, Rt-mart and bread demand area in Shanghai
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Figure 4.  Location-allocation supply chain network
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Figure 5.  Comparisons of different algorithms
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Figure 6.  Values of fitness functions and supply chain profit with different confidence
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Table 1.  Gap analysis of various research focus of supply chain
Reference Location type Random parameters Stochastic approach Risk approach Solving algorithm
[27] Relief centers Demand, supply Two-stage Risk-neutral Heuristic
[Irawan and Jones2019Formulation] Distribution center // Two-stage Risk-neutral Matheuristic
[32] Emergency facility Demand Two-stage CVaR Bender decomposition
[6] Warehouse // Two-stage Risk-neutral Branch-and-bound
[21] Factory // Two-stage Risk-neutral Heuristic
[24] Factory, warehouse // Two-stage Risk-neutral Heuristic
[37] Facility Throughput costs One-stage Risk-neutral Heuristic
[4] Factory Demand One-stage Risk-neutral Branch-and-bound
[5] Facility Demand Two-stage Risk-neutral L-shaped
[30] Distribution center // One-stage Risk-neutral Heuristic
[50] Facility Lead time Two-stage CVaR Decomposition
[19] Facility Demand One-stage Risk-neutral Combined simulated annealing
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Reference Location type Random parameters Stochastic approach Risk approach Solving algorithm
[27] Relief centers Demand, supply Two-stage Risk-neutral Heuristic
[Irawan and Jones2019Formulation] Distribution center // Two-stage Risk-neutral Matheuristic
[32] Emergency facility Demand Two-stage CVaR Bender decomposition
[6] Warehouse // Two-stage Risk-neutral Branch-and-bound
[21] Factory // Two-stage Risk-neutral Heuristic
[24] Factory, warehouse // Two-stage Risk-neutral Heuristic
[37] Facility Throughput costs One-stage Risk-neutral Heuristic
[4] Factory Demand One-stage Risk-neutral Branch-and-bound
[5] Facility Demand Two-stage Risk-neutral L-shaped
[30] Distribution center // One-stage Risk-neutral Heuristic
[50] Facility Lead time Two-stage CVaR Decomposition
[19] Facility Demand One-stage Risk-neutral Combined simulated annealing
Table 2.  Numerical optimal solution and value of the example
$\mathbf{e}_{best}=(1, 1, 1, 0)$ $\mathbf{c}_{best}=(1, 0, 0, $ $0, 1, 0, 1, 0, 1, 0)$
$x_{111}^*=495.0740$ $x_{112}^*=935.0000$ $x^*_{113}=69.9260$ $x_{123}^*=382.7580$ $y_{111}^*=495.0740$
$y_{125}^*=424.7554$ $y_{127}^*=466.7951$ $y_{129}^*=43.4494$ $y_{135}^*=52.9868$ $y_{139}^*=399.6973$
$z_{111}^*=495.0740$ $z_{152}^*=477.7422$ $z_{171}^*=2.6723$ $z_{172}^*=7.1685$ $Pro=1.2952e+04$
$z_{174}^*=456.9543$ $z_{193}^*=443.1467$ Others=0 $Val=-1.2799e+04$
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$\mathbf{e}_{best}=(1, 1, 1, 0)$ $\mathbf{c}_{best}=(1, 0, 0, $ $0, 1, 0, 1, 0, 1, 0)$
$x_{111}^*=495.0740$ $x_{112}^*=935.0000$ $x^*_{113}=69.9260$ $x_{123}^*=382.7580$ $y_{111}^*=495.0740$
$y_{125}^*=424.7554$ $y_{127}^*=466.7951$ $y_{129}^*=43.4494$ $y_{135}^*=52.9868$ $y_{139}^*=399.6973$
$z_{111}^*=495.0740$ $z_{152}^*=477.7422$ $z_{171}^*=2.6723$ $z_{172}^*=7.1685$ $Pro=1.2952e+04$
$z_{174}^*=456.9543$ $z_{193}^*=443.1467$ Others=0 $Val=-1.2799e+04$
Table 3.  Comparisons of different algorithms
Algorithm $\mathbf{e}_{best}$ $\mathbf{c}_{best}$ Pro Val TI
MHB-PSO $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.2952e+04$ $-1.2799e+04$ $9849.4900$
Hybrid PSO $(0, 1, 1, 1)$ $(0, 0, 1, 0, 0, 1, 1, 0, 1, 0)$ $1.2861e+04$ $-1.2683e+04$ $11169.2300$
Hybrid GA $(0, 1, 0, 1)$ $(0, 0, 1, 0, 0, 1, 0, 0, 1, 1)$ $1.2855e+04$ $-1.2660e+04$ $12035.1647$
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Algorithm $\mathbf{e}_{best}$ $\mathbf{c}_{best}$ Pro Val TI
MHB-PSO $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.2952e+04$ $-1.2799e+04$ $9849.4900$
Hybrid PSO $(0, 1, 1, 1)$ $(0, 0, 1, 0, 0, 1, 1, 0, 1, 0)$ $1.2861e+04$ $-1.2683e+04$ $11169.2300$
Hybrid GA $(0, 1, 0, 1)$ $(0, 0, 1, 0, 0, 1, 0, 0, 1, 1)$ $1.2855e+04$ $-1.2660e+04$ $12035.1647$
Table 4.  Results of MHB-PSO with different parameters
System Parameters Results
T N $c_{min}$ $c_{max}$ $\mathbf{e}_{best}$ $\mathbf{c}_{best}$ Val Error(%)
50 10 2.0 2.1 $(1, 1, 1, 0)$ $(1, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2690e+04$ 0.99
100 10 2.0 2.1 $(0, 1, 1, 1)$ $(0, 0, 1, 0, 0, 1, 1, 0, 1, 0)$ $-1.2712e+04$ 0.76
1000 10 2.0 2.1 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2780e+04$ 0.23
2000 10 2.0 2.1 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2780e+04$ 0.23
1000 100 2.0 2.1 $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $-1.2799e+04$ 0.08
1000 1000 2.0 2.1 $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $-1.2799e+04$ 0.08
1000 10 2.0 2.5 $(0, 1, 0, 1)$ $(0, 0, 1, 0, 1, 0, 0, 0, 1, 1)$ $-1.2801e+04$ 0.06
1000 10 2.0 3.0 $(1, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2809e+04$ 0.00
1000 10 2.0 4.0 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2780e+04$ 0.23
1000 10 2.5 3.0 $(1, 1, 0, 1)$ $(0, 0, 0, 1, 1, 0, 1, 0, 1, 0)$ $-1.2721e+04$ 0.69
1000 10 2.5 4.0 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2760e+04$ 0.38
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System Parameters Results
T N $c_{min}$ $c_{max}$ $\mathbf{e}_{best}$ $\mathbf{c}_{best}$ Val Error(%)
50 10 2.0 2.1 $(1, 1, 1, 0)$ $(1, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2690e+04$ 0.99
100 10 2.0 2.1 $(0, 1, 1, 1)$ $(0, 0, 1, 0, 0, 1, 1, 0, 1, 0)$ $-1.2712e+04$ 0.76
1000 10 2.0 2.1 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2780e+04$ 0.23
2000 10 2.0 2.1 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2780e+04$ 0.23
1000 100 2.0 2.1 $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $-1.2799e+04$ 0.08
1000 1000 2.0 2.1 $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $-1.2799e+04$ 0.08
1000 10 2.0 2.5 $(0, 1, 0, 1)$ $(0, 0, 1, 0, 1, 0, 0, 0, 1, 1)$ $-1.2801e+04$ 0.06
1000 10 2.0 3.0 $(1, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2809e+04$ 0.00
1000 10 2.0 4.0 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2780e+04$ 0.23
1000 10 2.5 3.0 $(1, 1, 0, 1)$ $(0, 0, 0, 1, 1, 0, 1, 0, 1, 0)$ $-1.2721e+04$ 0.69
1000 10 2.5 4.0 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $-1.2760e+04$ 0.38
Table 5.  Supply chain profit with random and expected transportation cost and demand
$\xi(\omega)$ $\mathbf{e}_{best}$ $\mathbf{c}_{best}$ Pro
Random $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.2952e+04$
Expected $(0, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 0, 0, 1, 1)$ $1.2819e+04$
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$\xi(\omega)$ $\mathbf{e}_{best}$ $\mathbf{c}_{best}$ Pro
Random $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.2952e+04$
Expected $(0, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 0, 0, 1, 1)$ $1.2819e+04$
Table 6.  Comparison of supply chain profit of model with $ \lambda = 0.1 $ and $ \lambda = 0 $
$\lambda$ $\mathbf{e}_{best}$ $\mathbf{c}_{best}$ Pro
$\lambda=0$ $(1, 1, 1, 0)$ $(0, 0, 1, 1, 0, 1, 1, 0, 1, 0)$ $1.3007e+04$
$\lambda=0.1$ $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.2952e+04$
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$\lambda$ $\mathbf{e}_{best}$ $\mathbf{c}_{best}$ Pro
$\lambda=0$ $(1, 1, 1, 0)$ $(0, 0, 1, 1, 0, 1, 1, 0, 1, 0)$ $1.3007e+04$
$\lambda=0.1$ $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.2952e+04$
Table 7.  Effect of raw material cost and retail price on supply chain profit
$(r_{11}, r_{12})$ $h_1$ ${\mathbf e}_{best}$ ${\mathbf c}_{best}$ Pro
(4.6, 4.8) 14.0 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $1.1989e+04$
(4.6, 4.8) 14.5 $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.2952e+04$
(4.6, 4.8) 15.0 $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.3809e+04$
(4.3, 4.8) 14.5 $(0, 1, 1, 1)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $1.3233e+04$
(4.9, 4.8) 14.5 $(0, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 0, 0, 1, 1)$ $1.2500e+04$
(4.6, 4.5) 14.5 $(0, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 0, 0, 1, 1)$ $1.3063e+04$
(4.6, 5.0) 14.5 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $1.2914e+04$
(4.3, 4.5) 14.5 $(0, 1, 1, 1)$ $(0, 0, 0, 1, 0, 0, 1, 1, 1, 0)$ $1.3424e+04$
(4.9, 5.0) 14.5 $(0, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 0, 0, 1, 1)$ $1.2361e+04$
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$(r_{11}, r_{12})$ $h_1$ ${\mathbf e}_{best}$ ${\mathbf c}_{best}$ Pro
(4.6, 4.8) 14.0 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $1.1989e+04$
(4.6, 4.8) 14.5 $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.2952e+04$
(4.6, 4.8) 15.0 $(1, 1, 1, 0)$ $(1, 0, 0, 0, 1, 0, 1, 0, 1, 0)$ $1.3809e+04$
(4.3, 4.8) 14.5 $(0, 1, 1, 1)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $1.3233e+04$
(4.9, 4.8) 14.5 $(0, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 0, 0, 1, 1)$ $1.2500e+04$
(4.6, 4.5) 14.5 $(0, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 0, 0, 1, 1)$ $1.3063e+04$
(4.6, 5.0) 14.5 $(1, 1, 1, 0)$ $(0, 0, 0, 1, 0, 1, 1, 0, 1, 0)$ $1.2914e+04$
(4.3, 4.5) 14.5 $(0, 1, 1, 1)$ $(0, 0, 0, 1, 0, 0, 1, 1, 1, 0)$ $1.3424e+04$
(4.9, 5.0) 14.5 $(0, 1, 0, 1)$ $(0, 0, 0, 1, 0, 1, 0, 0, 1, 1)$ $1.2361e+04$
Table 8.  Parameters for suppliers, processing factories, and distribution centers
Index Suppliers Processing plants Distribution centers
$s, i, j$ $a_{1s}$ $r_{1s}$ $b_{i1}$ $q_{i1}$ $f_i$ $\tau_{1j}$ $w_{1j}$ $g_j$
1 1500 4.6 850 0.80 185 500 0.25 135
2 1200 4.8 935 0.75 180 480 0.25 130
3 $/$ $/$ 845 0.81 200 450 0.25 120
4 $/$ $/$ 950 0.76 160 470 0.25 125
5 $/$ $/$ $/$ $/$ $/$ 510 0.25 140
6 $/$ $/$ $/$ $/$ $/$ 490 0.25 130
7 $/$ $/$ $/$ $/$ $/$ 520 0.25 145
8 $/$ $/$ $/$ $/$ $/$ 505 0.25 143
9 $/$ $/$ $/$ $/$ $/$ 460 0.25 123
10 $/$ $/$ $/$ $/$ $/$ 485 0.25 133
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Index Suppliers Processing plants Distribution centers
$s, i, j$ $a_{1s}$ $r_{1s}$ $b_{i1}$ $q_{i1}$ $f_i$ $\tau_{1j}$ $w_{1j}$ $g_j$
1 1500 4.6 850 0.80 185 500 0.25 135
2 1200 4.8 935 0.75 180 480 0.25 130
3 $/$ $/$ 845 0.81 200 450 0.25 120
4 $/$ $/$ 950 0.76 160 470 0.25 125
5 $/$ $/$ $/$ $/$ $/$ 510 0.25 140
6 $/$ $/$ $/$ $/$ $/$ 490 0.25 130
7 $/$ $/$ $/$ $/$ $/$ 520 0.25 145
8 $/$ $/$ $/$ $/$ $/$ 505 0.25 143
9 $/$ $/$ $/$ $/$ $/$ 460 0.25 123
10 $/$ $/$ $/$ $/$ $/$ 485 0.25 133
Table 9.  Random transportation cost from flour factory to food factory
Flour factory Yingyuan food factory Changli food factory Ziyan food factory Sunhong food factory
Fuxin third $\mathscr{U}(0.18, 0.21)$ $\mathscr{U}(0.12, 0.16)$ $\mathscr{U}(0.25, 0.28)$ $\mathscr{U}(0.40, 0.43)$
Fuxin $\mathscr{U}(0.45, 0.49)$ $\mathscr{U}(0.25, 0.28)$ $\mathscr{U}(0.11, 0.15)$ $\mathscr{U}(0.27, 0.30)$
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Flour factory Yingyuan food factory Changli food factory Ziyan food factory Sunhong food factory
Fuxin third $\mathscr{U}(0.18, 0.21)$ $\mathscr{U}(0.12, 0.16)$ $\mathscr{U}(0.25, 0.28)$ $\mathscr{U}(0.40, 0.43)$
Fuxin $\mathscr{U}(0.45, 0.49)$ $\mathscr{U}(0.25, 0.28)$ $\mathscr{U}(0.11, 0.15)$ $\mathscr{U}(0.27, 0.30)$
Table 10.  Random transportation cost of food factory transporting product to Rt-mart supermarket
Rt-mart Yingyuan food factory Changli food factory Ziyan food factory Sunhong food factory
Meilanhu $\mathscr{U}(0.26, 0.30)$ $\mathscr{U}(0.37, 0.40)$ $\mathscr{U}(0.46, 0.49)$ $\mathscr{U}(0.65, 0.68)$
Anting $\mathscr{U}(0.46, 0.49)$ $\mathscr{U}(0.41, 0.45)$ $\mathscr{U}(0.40, 0.44)$ $\mathscr{U}(0.68, 0.72)$
Nanxiang $\mathscr{U}(0.31, 0.34)$ $\mathscr{U}(0.27, 0.31)$ $\mathscr{U}(0.33, 0.36)$ $\mathscr{U}(0.56, 0.60)$
Yangpu $\mathscr{U}(0.08, 0.11)$ $\mathscr{U}(0.17, 0.20)$ $\mathscr{U}(0.34, 0.37)$ $\mathscr{U}(0.41, 0.44)$
Sijing $\mathscr{U}(0.45, 0.49)$ $\mathscr{U}(0.27, 0.31)$ $\mathscr{U}(0.18, 0.21)$ $\mathscr{U}(0.48, 0.52)$
Chunshen $\mathscr{U}(0.36, 0.39)$ $\mathscr{U}(0.12, 0.16)$ $\mathscr{U}(0.06, 0.09)$ $\mathscr{U}(0.33, 0.37)$
Kangqiao $\mathscr{U}(0.26, 0.29)$ $\mathscr{U}(0.11, 0.15)$ $\mathscr{U}(0.24, 0.28)$ $\mathscr{U}(0.19, 0.23)$
Songjiang $\mathscr{U}(0.58, 0.62)$ $\mathscr{U}(0.37, 0.41)$ $\mathscr{U}(0.21, 0.25)$ $\mathscr{U}(0.51, 0.55)$
Fengxian $\mathscr{U}(0.58, 0.62)$ $\mathscr{U}(0.36, 0.40)$ $\mathscr{U}(0.24, 0.27)$ $\mathscr{U}(0.30, 0.34)$
Nicheng $\mathscr{U}(0.63, 0.67)$ $\mathscr{U}(0.52, 0.55)$ $\mathscr{U}(0.54, 0.57)$ $\mathscr{U}(0.22, 0.25)$
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Rt-mart Yingyuan food factory Changli food factory Ziyan food factory Sunhong food factory
Meilanhu $\mathscr{U}(0.26, 0.30)$ $\mathscr{U}(0.37, 0.40)$ $\mathscr{U}(0.46, 0.49)$ $\mathscr{U}(0.65, 0.68)$
Anting $\mathscr{U}(0.46, 0.49)$ $\mathscr{U}(0.41, 0.45)$ $\mathscr{U}(0.40, 0.44)$ $\mathscr{U}(0.68, 0.72)$
Nanxiang $\mathscr{U}(0.31, 0.34)$ $\mathscr{U}(0.27, 0.31)$ $\mathscr{U}(0.33, 0.36)$ $\mathscr{U}(0.56, 0.60)$
Yangpu $\mathscr{U}(0.08, 0.11)$ $\mathscr{U}(0.17, 0.20)$ $\mathscr{U}(0.34, 0.37)$ $\mathscr{U}(0.41, 0.44)$
Sijing $\mathscr{U}(0.45, 0.49)$ $\mathscr{U}(0.27, 0.31)$ $\mathscr{U}(0.18, 0.21)$ $\mathscr{U}(0.48, 0.52)$
Chunshen $\mathscr{U}(0.36, 0.39)$ $\mathscr{U}(0.12, 0.16)$ $\mathscr{U}(0.06, 0.09)$ $\mathscr{U}(0.33, 0.37)$
Kangqiao $\mathscr{U}(0.26, 0.29)$ $\mathscr{U}(0.11, 0.15)$ $\mathscr{U}(0.24, 0.28)$ $\mathscr{U}(0.19, 0.23)$
Songjiang $\mathscr{U}(0.58, 0.62)$ $\mathscr{U}(0.37, 0.41)$ $\mathscr{U}(0.21, 0.25)$ $\mathscr{U}(0.51, 0.55)$
Fengxian $\mathscr{U}(0.58, 0.62)$ $\mathscr{U}(0.36, 0.40)$ $\mathscr{U}(0.24, 0.27)$ $\mathscr{U}(0.30, 0.34)$
Nicheng $\mathscr{U}(0.63, 0.67)$ $\mathscr{U}(0.52, 0.55)$ $\mathscr{U}(0.54, 0.57)$ $\mathscr{U}(0.22, 0.25)$
Table 11.  Random distribution cost of distribution center transporting product to consumer
Rt-mart Demand area 1 Demand area 2 Demand area 3 Demand area 4
Meilanhu $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(2.00, 2.50)$ $\mathscr{U}(1.70, 2.20)$
Anting $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.90, 2.40)$ $\mathscr{U}(1.80, 2.30)$
Nanxiang $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.90, 2.40)$ $\mathscr{U}(1.70, 2.20)$
Yangpu $\mathscr{U}(1.10, 1.60)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(2.00, 2.50)$ $\mathscr{U}(1.20, 1.70)$
Sijing $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.50, 2.00)$
Chunshen $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.10, 1.60)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.40, 1.90)$
Kangqiao $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.60, 2.10)$ $\mathscr{U}(1.10, 1.60)$
Songjiang $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.60, 2.10)$
Fengxian $\mathscr{U}(1.70, 2.20)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.60, 2.10)$
Nicheng $\mathscr{U}(1.70, 2.20)$ $\mathscr{U}(1.70, 2.20)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.00, 1.50)$
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Rt-mart Demand area 1 Demand area 2 Demand area 3 Demand area 4
Meilanhu $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(2.00, 2.50)$ $\mathscr{U}(1.70, 2.20)$
Anting $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.90, 2.40)$ $\mathscr{U}(1.80, 2.30)$
Nanxiang $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.90, 2.40)$ $\mathscr{U}(1.70, 2.20)$
Yangpu $\mathscr{U}(1.10, 1.60)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(2.00, 2.50)$ $\mathscr{U}(1.20, 1.70)$
Sijing $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.50, 2.00)$
Chunshen $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.10, 1.60)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.40, 1.90)$
Kangqiao $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.40, 1.90)$ $\mathscr{U}(1.60, 2.10)$ $\mathscr{U}(1.10, 1.60)$
Songjiang $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.60, 2.10)$
Fengxian $\mathscr{U}(1.70, 2.20)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.00, 1.50)$ $\mathscr{U}(1.60, 2.10)$
Nicheng $\mathscr{U}(1.70, 2.20)$ $\mathscr{U}(1.70, 2.20)$ $\mathscr{U}(1.50, 2.00)$ $\mathscr{U}(1.00, 1.50)$
Table 12.  Random demand of consumer
Demand area 1 Demand area 2 Demand area 3 Demand area 4
$\mathscr{U}(500, 525)-h_1$ $\mathscr{U}(490, 515)-h_1$ $\mathscr{U}(445, 470)-h_1$ $\mathscr{U}(455, 480)-h_1$
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Demand area 1 Demand area 2 Demand area 3 Demand area 4
$\mathscr{U}(500, 525)-h_1$ $\mathscr{U}(490, 515)-h_1$ $\mathscr{U}(445, 470)-h_1$ $\mathscr{U}(455, 480)-h_1$
[1]

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