PPL adalah salah satu perusahaan third-party logistics (TPL) yang melayani kliennya dalam urusan logistik berupa manajemen gudang penyimpanan dan distribusi makanan dan minuman kemasan ke pelanggan akhir. Selama tiga bulan terakhir di tahun 2020, biaya outbound logistik perusahaan mengalami peningkatan sebesar Rp 26.383.786. Peningkatan biaya ini disebabkan penggunaan kendaraan yang berlebih sehingga perlunya perencanaan aktivitas outbound logistik. Penelitian ini mengembangkan model matematika yang bertujuan mengurangi biaya outbound logistik menggunakan metode Mixed Integer Linear Programming (MILP) dengan pengembangan model menggunakan perangakat lunak LINGO. Hasil penelitian ini menunjukan perbaikan total biaya outbound logistik dari Rp 496.496.419 menjadi Rp 380.136.216 atau sebesar Rp 116.360.203. Hasil model matematika didapat perbaikan jumlah kendaraan dari 262 unit sebelum perbaikan menjadi 179 unit setelah perbaikan atau pengurangan sebesar 83 unit atau 48,26% dan optimasi muatan produk dalam kendaraan pengirim dari 84,26% sebelum perbaikan menjadi 97,34% atau penambahan sebesar 13,08%.

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PPL is one of many third-party logistics (TPL) company that serves its client in a logistics manner. PPL runs its service as third-party logistics for food and drinks sachet products. Outbound logistics activities in a company consist of warehouse management and last-mile delivery. Last three months in 2020, the company's outbound logistics cost increased as much as IDR 26,383,786. Increasing in cost was caused by the utility of vehicles that exceeds the company's need. Therefore, the company must have an outbound logistics activities plan. The research develops a mathematical model using Mixed Integer Linear Programming (MILP) method with help of LINGO software. The result of this research is presented improvement in outbound logistics cost from IDR 496,496,419 before improvement to IDR 380.136.216 after improvement or as much as IDR 116.360.203. The improvement of the outbound logistics cost was determined by improving the company's vehicle utility from 262 units before improvement to 179 units after improvement and optimizing the vehicle's capacity for delivery to end customers from 84,26% before improvement to 97,34% after improvement or as much as 13,08%.