Abstract
In today’s world, intentional disruptions in networks are expanding and the impacts are seen in many parts of the world. An effective approach for reducing the impact of such disruptions is to confuse invaders. Increasing ambiguity in the network is one of the effective ways which may confuse the invaders. To attain this goal, dummy facilities are added to the network. Dummy facilities are the facilities which are exactly the same as the real ones thus making it hard for the invader to make the distinction. In this paper, a new multi-objective mathematical model is presented to suitably design a network consisting of real and dummy warehouses. One objective is to minimize the total cost and the other is set to maximize reliability. An index for assessing network reliability is also introduced and used. The model is solved using AUGMECON and NSGA-II. Results demonstrate that establishing dummy facilities in the network will increase reliability while no significant cost is imposed.
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Notes
Non-dominated sorting chemical reaction optimization.
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Appendix A. Date set 1, small size data set
Appendix A. Date set 1, small size data set
Potential locations for warehouses | Demand | |||||||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
Demand nodes | ||||||||
1 | 7280 | 2920 | 8240 | 4420 | 3900 | 2574 | 6248 | 10000 |
2 | 2040 | 8510 | 3400 | 15360 | 7563 | 4228 | 23010 | 5000 |
3 | 6280 | 7540 | 2310 | 12300 | 10315 | 11598 | 12881 | 4000 |
4 | 5120 | 4110 | 11990 | 10630 | 14065 | 16506 | 18947 | 1000 |
5 | 3960 | 680 | 1670 | 8960 | 17815 | 6953 | 2501 | 3000 |
6 | 5500 | 1520 | 5965 | 6595 | 11066 | 5485 | 8922 | 6000 |
7 | 4600 | 821 | 7545 | 8711 | 4487 | 1410 | 7544 | 2000 |
Real fixed cost | 3.5× 1010 | 5×1010 | 6×1010 | 8×1010 | 8.5×1010 | 9.5×1010 | 9.9×1010 | |
Dummy fixed cost | 3.5×108 | 5×108. | 6×108 | 8×108 | 8.5×108 | 9.5×108 | 9.9×108 |
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Hamidi, M.R., Gholamian, M.R. Optimizing warehouse network reliability under intentional disruption by increasing network ambiguity: a multi objective optimization model. Sādhanā 45, 76 (2020). https://doi.org/10.1007/s12046-020-1295-6
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DOI: https://doi.org/10.1007/s12046-020-1295-6