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"Komplek perumahan merupakan salah satu sumber pembangkit perjalanan yang dominan di Kotamadya Depok, Dengan mengkaji karakteristik bangkitan perjalanan pada komplek perumahan di 'Depok diharapkan dapat membantu memberi asukan untuk mengatasi permasalahan transportasi yang ada saat ini di wilayah tersebut dan mengantisipasi permasalahan transportasi yang akan timbul dimasa mendatang.Isi pokok tesis ini adalah menganalisis hubungan antara karakteristik sosial ekonomi dengan bangkitan perjalanan individu yang ditimbulkannya dan hubungan antara waktu keberangkatan bekerja/sekolah dengan waktu masuk kantor, maksud/ tujuan perjalanan, lokasi tempat bekerja/sekolah, jarak tempat bekerja/sekolah dari rumah dan model yang digunakan.Pembuatan model menggunakan metode regresi dengan variabel semu (dummy variabel). Sedangkan untuk pemilihan model terbaik digunakan metode "seleksi maju". Proses analisis dan uji statistik dilakukan dengan bantuan program SPSS for windows ver. 10.0.Model bangkitan perjalanan individu dibedakan berdasarkan maksud/tujuan perjalanan, yaitu: perjalanan bekerja, pekerjaan pendidikan, perjalanan belanja,perjalanan hiburan/rekreasilsosial dan perjalanan total. Masing-masing model yang 'dihasilkan merupakan persamaan .matematis yang menggambarkan hubungan dengan tingkat kepercayaan 95 persen antara bangkitan perjalanan individu dengan karakteristik social ekonomi.Model Bangkitan Perjalanan Bekerja Individu:Pkrj = 1.287 + 0.108 . KRJ2 + 1.048 . KRJ3 + 0.138 . KRJ4 - 1.295 . KRI5 - 1.308 . KRJ6 - 1.369. KRJ7 - 1.287. KRIS + 0.330. MTR(SEE =0.4173; F=46.599; R2=0.792)Pkrj = 1.269+ 0.191. KRJ2 + 1.197. KRJ3 + 0.156. KRJ4 -1.269. KRJ5 - 1.269. KRJ6 - 1.303. KRJ7 - 1.269. KRJ8+ 0.0457 . MBL(SEE = 0.4279 ; F = 43.726 ; 1? = 0.781)Pkrj = - 0.0133 + 1.497. HSLI + 1.173. HSL2 + 1.183. HSL3 + 1.355. HSL4 + 2.198. HSL5 + 0.400 . MTR(SEE = 0.4883 ; F = 40.533 ; R2 =11.711)Pkrj = - 3.03.10-11+ 1.593 . HSL1 + 1.295 . HSL2 + 1.404 . HSL3 + 1.801 . HSL4 + 2.762 . HSL5 - 0.477 . MBL(SEE =0.4867; F=40.910; R2=0.713)Madel Bangkitan Perjalanan Pendidikan Individu:Pddk = 0.634- 0.292. KRJ2 + 0.219. KRJ3 - 0.354 . KRJ4 + 1.039 . KRJ5 - 0.291 .KRJ6 - 0.291 . KRJ7-0.285 . KRJ8- 0.0747 . USIA2 - 0.207. USIA3-0.356. USIA4 - 0.499 . USIA5 - 0.345. USIA6-0.343. USIA7 - 0.346. USIA8(SEE =02825; F=55.0119; R2=0.893)Model Bangkitan Perjalanan Belanja Individu:Pblj = - 0.101 + 0.360 . KLG2 + 0.103. KLG3 + 0.06386. KLG4 + 0.04377. USIA2 + 0.112. USIA3 - 0.219 . USIA4 + 0.015 . USIA5 - 0.09545. USIA6 + 0.101 . USIA7 - 0.111 . USIA8(SEE =0.2801; F=4.091; R2-0.299)Model Bangkitan Perjalanan Hiburan/Rekreasi/Sosial Individu:Phib = 0.140 + 0.593. MBL + 0.06801 . HSL1- 0.09289. HSL2 + 0.321 . HSL3 - 0.303 . HSL4 - 0.288. HSL5 - 0.205. SIM(SEE =0.3536; F=5.526; R2=0.283)Model Bangkitan Perjalanan Total Individu:Ptot = 1.984 - 0.587 . KRJ2 + 0.845. KRJ3 - 0.09656 . KRJ4 - 0.262 . KRJ5 - 1.657 . KRJ6 - 2.043. KRJ7 - 1.872. KRJ8 + 0.01888. USIA2 + 0.03835 . USIA3- 0.06661 . USIA4 - 1.108 . USIA5 - 0.309. USIA6-0.570. USIA7 - 0.607. USIAS + 0.670 . MBL + 0.395. MTR + 0.387. KWN- (SEE = 0.7710; F = 6.255 ; R2 = 0.544)Model Waktu Keberangkatan Individu:BRKT = 0.209 + 0.881 . MSK - 0.577. LOK -- 0.383 . TJH + 0.0055. JRK - 0.0581 . MD(SEE = 0.4802 ; F =112.222 ; R2 = 0.899)"

"Pemerintah Indonesia telah menetapkan mandatori B30 dengan penetapan Peraturan Menteri Energi dan Sumber Daya Mineral Republik Indonesia No.12 Tahun 2015. Hal ini mendorong pada peningkatan volume distribusi suplai biodiesel diseluruh Indonesia demi memenuhi mandatori tersebut. Fatty Acid Methyl Ester (FAME) sebagai salah satu bahan biodiesel dicampurkan pada solar untuk menciptakan biofuel, yang didistribukan ke Badan Usaha Bahan Bakar Minyak untuk dicampurkan. Skema distribusi FAME saat ini belum optimal dan menghasilkan total biaya ongkos angkut(OA) yang tidak ekonomis. Pada penelitian ini, penulis menggunakan transportation problem model untuk mengoptimalkan penyaluran distribusi FAME dan menekan total biaya distribusi. Hasil dari optimasi mampu menghemat 22.6% dari total biaya distribusi jika dilakukan BAU (business as usual). Rute yang dipilihpun hanya sebanyak 92 rute, yang mana lebih sedikit daripada sebelumnya yaitu 104 rute. Proyeksi permintaan pada 2027 juga dilakukan dengan metode simple moving average, yaitu naik 11.22% dari 2022, lalu di masukkan ke dalam model optimasi bersama kapasitas BUBBN maksimal, yang menghasilkan 23.26% penghematan total biaya OA dan 86 rute terpilih. Disarankan untuk mengerjakan kajian lebih lanjut yang mengintegrasi jarak pada model untuk menemukan rekomendasi rute yang lebih sesuai dengan keadaan lapangan yang mementingkan jarak dan waktu pula dalam penentuan rute.......The Indonesian government has set a mandatory B30 with the stipulation of Regulation of the Minister of Energy and Mineral Resources of the Republic of Indonesia No. 12 of 2015. This encourages an increase in the volume of distribution of biodiesel supply throughout Indonesia in order to fulfill the mandate. Fatty Acid Methyl Ester (FAME) as one of the biodiesel ingredients is mixed in diesel to create biofuel, which is distributed to Oil Fuel Business Entities to be mixed. The current FAME distribution scheme is not optimal and results in an uneconomic total cost of transportation (OA). In this study, the authors use the transportation problem model to optimize the distribution of FAME distributions and reduce the total distribution costs. The results of the optimization are able to save 22,6% of the total distribution costs if BAU (business as usual) is carried out. Only 92 routes were selected, which is less than the previous 104 routes. Projection on demand in 2027 also searched with simple moving average method, which is increased 11.22% from 2022, then optimized into the model with maximal BUBBN capacity, resulting 23.26% saving OA total cost and 86 routes. It is recommended to carry out further studies that integrate the distance in the model to find route recommendations that are more suitable for field conditions which also place the importance on distance and time in determining the distribution routes."

"ABSTRAKThe Fuzzy Fixed Charge Transportation Problem in which both fixed and transportation cost are fuzzy numbers is considered in this paper. Due to NP-hardness of the problem, we utilize three types of Electromagnetism-like Algorithms (EM), Genetic Algorithm (GA), and Simulated Annealing (SA) which are firstly being proposed and comprised in this research area. Besides, our other novelty approach is the use of new encoding mechanism, namely string representation, for the first time which is employed for the problem and can be used in any extended transportation problems. Also, the last version of EM is being firstly developed and proposed in this paper. The employed operators and parameters are calibrated to ensure the best performance of the algorithms. Besides, different problem sizes are considered at random to study the impacts of the rise in the problem size on the performance of the algorithms."

"ABSTRACTThe fixed charge transportation problem (FCTP) is one of important and classical transportation problems with many real world applications in the area of logistics and supply chain management. Due to nature complexity of this problem, the literature has seen a large number of heuristics and meta heuristics to solve the FCTP. This paper proposes a new heuristic along with well known meta heuristics to solve the FCTP with discount supposition on both fixed and variable charges. In addition, two models with all units discount and incremental discount are firstly introduced in this study to apply the discount mechanism. As such, since the previous researchers mainly used spanning tree based and priority based representations, this study utilizes both of these methods and compared the results. Finally, a comprehensive discussion based on the computational results of heuristic and meta heuristics with different encoding approaches has been investigated through different problem sizes."