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Stream/Area:
-
Subject:
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Paper ID:
IJSREM67818
Volume:
10 |
10 |
Oct 2026
Pages:
1-12
Publication Frequency:
Monthly (12 issues per year)
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.
Department of Applied Science & Engineering Technology
Narsee Monjee Institute of Management Studies
Department of Applied Science & Engineering Technology
Narsee Monjee Institute of Management Studies
Department of Applied Science & Engineering Technology
Narsee Monjee Institute of Management Studies
Department of Applied Science & Engineering Technology
Narsee Monjee Institute of Management Studies
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
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.
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Publication Date
10/12/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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