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Paper ID:IJSREM67818

Applied Science & Engineering Technology

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67818

Volume:

10 |

10 |

Oct 2026

Pages:

1-12

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PD

Pranav Damaraju

Department of Applied Science & Engineering Technology

Narsee Monjee Institute of Management Studies

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YC

Yash Choube

Department of Applied Science & Engineering Technology

Narsee Monjee Institute of Management Studies

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SM

Shrey Mittal

Department of Applied Science & Engineering Technology

Narsee Monjee Institute of Management Studies

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SJ

Sarthak Jain

Department of Applied Science & Engineering Technology

Narsee Monjee Institute of Management Studies

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Abstract

This paper develops and tests a method for minimising last-mile delivery costs in Mumbai by combining a transportation model with a mixed-integer linear programme (MILP). The problem studied is how a delivery firm with several depots and a capacitated van fleet should allocate delivery zones to depots, group and sequence the zones on routes, and choose the number of vans so that all daily demand is met at minimum cost. Allocation and routing interact, compact exact models scale poorly, and open, verifiable city-level examples are scarce; an illustrative case with clearly labelled synthetic demand is therefore used. A transportation linear programme is formulated as an allocation benchmark, and a multi-depot capacitated routing MILP with lifted Miller–Tucker–Zemlin constraints is solved to proven optimality with the SCIP solver through Google OR-Tools for a two-depot, twelve-zone Mumbai network serving 1,000 parcels per day. Compared with a nearest-depot, nearest-neighbour dispatch rule, the optimal plan reduces daily cost by 20.4% (from ₹6,443.68 to ₹5,131.94), distance by 14.5% and the fleet from four vans to three; 68.6% of the saving comes from eliminating one under-filled route. Across seven re-solved sensitivity scenarios the saving ranges from 13.6% to 23.1%. The plan is robust to changes in cost rates but sensitive to vehicle capacity and demand through the minimum fleet size. The paper also documents a structured EBSCO bibliometric search, profiles the core literature and derives managerial implications for urban parcel operators.

KEYWORDS

Last-mile delivery transportation problem mixed-integer linear programming multi-depot vehicle routing operations research Mumbai.

Cite This Article

Pranav Damaraju, Yash Choube, Shrey Mittal, Sarthak Jain, "Applied Science & Engineering Technology," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-12, Oct 2026.

Full Article/Conclusion

This paper combined a transportation allocation benchmark with a multi-depot capacitated routing MILP to plan last-mile deliveries in an illustrative Mumbai case. Solved to proven optimality, the model cut daily cost by 20.4% and distance by 14.5% relative to a nearest-depot, nearest-neighbour baseline, with most of the gain coming from running one van fewer. The transportation model reproduced the optimal depot allocation almost exactly but could not see the fleet saving, which shows why allocation and routing should be optimised together. Scenario testing showed that the plan is robust to cost-rate changes but responds sharply to capacity and demand through the minimum fleet size. Because the data are synthetic, the contribution is a transparent, reproducible method and a set of managerial insights to be tested on real data, not an empirical finding about Mumbai's delivery sector.

References

[1]     J. Olsson, D. Hellström, and H. Pålsson, "Framework of last mile logistics research: A systematic review of the literature," Sustainability, vol. 11, no. 24, Art. no. 7131, 2019, doi: 10.3390/su11247131. [2]     N. Boysen, S. Fedtke, and S. Schwerdfeger, "Last-mile delivery concepts: A survey from an operational research perspective," OR Spectrum, vol. 43, no. 1, pp. 1–58, 2021, doi: 10.1007/s00291-020-00607-8. [3]     M. Savelsbergh and T. Van Woensel, "50th anniversary invited article—City logistics: Challenges and opportunities," Transportation Science, vol. 50, no. 2, pp. 579–590, 2016, doi: 10.1287/trsc.2016.0675. [4]     L. Ranieri, S. Digiesi, B. Silvestri, and M. Roccotelli, "A review of last mile logistics innovations in an externalities cost reduction vision," Sustainability, vol. 10, no. 3, Art. no. 782, 2018, doi: 10.3390/su10030782. [5]     R. Mangiaracina, A. Perego, A. Seghezzi, and A. Tumino, "Innovative solutions to increase last-mile delivery efficiency in B2C e-commerce: A literature review," International Journal of Physical Distribution & Logistics Management, vol. 49, no. 9, pp. 901–920, 2019, doi: 10.1108/IJPDLM-02-2019-0048. [6]     G. Laporte, "Fifty years of vehicle routing," Transportation Science, vol. 43, no. 4, pp. 408–416, 2009, doi: 10.1287/trsc.1090.0301. [7]     P. Toth and D. Vigo, Eds., Vehicle Routing: Problems, Methods, and Applications, 2nd ed. Philadelphia, PA, USA: SIAM, 2014, doi: 10.1137/1.9781611973594. [8]     F. L. Hitchcock, "The distribution of a product from several sources to numerous localities," Journal of Mathematics and Physics, vol. 20, no. 1–4, pp. 224–230, 1941, doi: 10.1002/sapm1941201224. [9]     F. S. Hillier and G. J. Lieberman, Introduction to Operations Research, 11th ed. New York, NY, USA: McGraw-Hill Education, 2021. [10]    G. B. Dantzig and J. H. Ramser, "The truck dispatching problem," Management Science, vol. 6, no. 1, pp. 80–91, 1959, doi: 10.1287/mnsc.6.1.80.

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Publication Date

10/12/2026

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Review Rounds: 2

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