Pengembangan Model And Algoritma Untuk Menyelesaikan Multi-Product Inventory Ship Routing Problem Dengan Dedicated Tanker Fleet

Kapal tanker banyak digunakan oleh banyak perusahaan sebagai salah satu moda transporasi untuk mengirimkan produknya. Untuk mencapai efisiensi yang tinggi dalam distribusi produk, diperlukan sebuah studi yang komprehensif tentang distribusi produk, dan salah satunya adalah Inventory Routing Problem. Melihat permasalah yang ada, penelitian ini bertujuan untuk membuat model dan algoritma untuk multi-product inventory routing problem dengan dedicated tanker fleet. Model yang dibuat mampu mengakomodasi multi product, multi depot, dedicated tanker policy, dan titik awal kapal yang fleksibel. Beberapa constraints dalam penelitian ini antara lain routing, inventori, waktu, loading dan unloading dengan objective function minimasi total biaya. Algoritma yang dikembangkan terdiri dari tiga bagian penting yaitu mengitung dan menyortir pelabuhan kritis, pemilihan kapal, dan proses routing. Kemudian, model yang algoritma tadi deprogram dengan menggunakan VBA di Microsoft Excel 2013 yang akan menghasilkan detail rute dari tiap kapal, waktu rute, dan biaya yang dikeluarkan. Hasil dari penelitian ini adalah program yang digunakan mampu menyelesaikan multi-product inventory ship routing problem. Dari beberapa eksperimen numerik didapat hasil jumlah kapal tanker terbaik untuk skema simulasi tertentu adalah 9 kapal tanker. ============================================================================================================= Many companies use tanker as one of the transportation mode to deliver their products. In order to reach optimum level and high efficiency of product distribution, a comprehensive study regarding distribution is important. In the literature, it is called Inventory Routing Problem (IRP). Based on a real case of oil distribution using tanker fleet in an Indonesian state-owned oil company, this research develops a model and algorithms to solve a Multi-Product Inventory Ship Routing Problem (M-ISRP) for dedicated tanker fleet. The model accommodates some aspects including multi products, multi depots, and flexible tanker initial points. The objective function is to minimize total distribution costs considering some constraints such as inventory, time, loading and unloading. The algorithms built consists of three main modules, which are critical ports selection, ship assignment, and ship routing. A spreadsheet-based decision support tool have been developed to evaluate the proposed algorithms. The results of the numerical experiments showed that the best total tanker number to deliver all the demand on certain case is 9 tankers

An integrated shipment planning and storage capacity decision under uncertainty: a simulation study

Purpose – In transportation and distribution systems, the shipment decisions, fleet capacity, and storage capacity are interrelated in a complex way, especially when the authors take into account uncertainty of the demand rate and shipment lead time. While shipment planning is tactical or operational in nature, increasing storage capacity often requires top management’s authority. The purpose of this paper is to present a new method to integrate both operational and strategic decision parameters, namely shipment planning and storage capacity decision under uncertainty. The ultimate goal is to provide a near optimal solution that leads to a striking balance between the total logistics costs and product availability, critical in maritime logistics of bulk shipment of commodity items. Design/methodology/approach – The authors use simulation as research method. The authors develop a simulation model to investigate the effects of various factors on costs and service levels of a distribution system. The model mimics the transportation and distribution problems of bulk cement in a major cement company in Indonesia consisting of a silo at the port of origin, two silos at two ports of destination, and a number of ships that transport the bulk cement. The authors develop a number of “what-if” scenarios by varying the storage capacity at the port of origin as well as at the ports of destinations, number of ships operated, operating hours of ports, and dispatching rules for the ships. Each scenario is evaluated in terms of costs and service level. A full factorial experiment has been conducted and analysis of variance has been used to analyze the results. Findings – The results suggest that the number of ships deployed, silo capacity, working hours of ports, and the dispatching rules of ships significantly affect both total costs and service level. Interestingly, operating fewer ships enables the company to achieve almost the same service level and gaining substantial cost savings if constraints in other part of the system are alleviated, i.e., storage capacities and working hours of ports are extended. Practical implications – Cost is a competitive factor for bulk items like cement, and thus the proposed scenarios could be implemented by the company to substantially reduce the transportation and distribution costs. Alleviating storage capacity constraint is obviously an idea that needs to be considered when optimizing shipment planning alone could not give significant improvements. Originality/value – Existing research has so far focussed on the optimization of shipment planning/scheduling, and considers shipment planning/scheduling as the objective function while treating the storage capacity as constraints. The simulation model enables “what-if” analyses to be performed and has overcome the difficulties and impracticalities of analytical methods especially when the system incorporates stochastic variables exhibited in the case example. The use of efficient frontier analysis for analyzing the simulation results is a novel idea which has been proven to be effective in screening non-dominated solutions. This has provided the authors with near optimal solutions to trade-off logistics costs and service levels (availability), with minimal experimentation times

Pengembangan model dan algoritma Dynamic-Inventory Ship Routing Problem (D-ISRP)

Ketidakpastian tingkat kesibukan pelabuhan atau dwelling time menyebabkan ketidaksesuaian operasional kapal terhadap jadwal yang telah direncanakan. Lamanya waktu tunggu di pelabuhan mengakibatkan utilitas kapal menjadi rendah dan biaya distribusi meningkat karena pemberlakuan biaya pelabuhan. Dampak lainnya adalah memperbesar kemungkinan keterlambatan kapal yang berpotensi untuk menyebabkan terjadinya inventory stock out. Jika supplier menerapkan kebijakan vendor managed inventory (VMI), maka dalam hal ini supplier akan menanggung biaya penalti akibat keterlambatan tersebut. Untuk menghindari terjadinya keterlambatan, diperlukan evaluasi terhadap jadwal eksisting apabila terdapat informasi perubahan dwelling time. Oleh karena itu, pada penelitian ini dikembangkan model dynamic-inventory ship routing problem (D-ISRP), dimana proses re-routing berdasarkan informasi dwelling time akan dilakukan dengan mengevaluasi opsi perubahan kecepatan dan opsi perubahan urutan kunjungan. Kedua opsi tersebut kemudian dibandingkan untuk menentukan jadwal baru jika terjadi perubahan dwelling time. Model yang dikembangkan mempertimbangkan beberapa komponen biaya antara lain biaya konsumsi bahan bakar saat pelayaran, konsumsi bahan bakar saat berlabuh, biaya operasional kapal, biaya kepelabuhanan, biaya loading/unloading, serta biaya penalti keterlambatan. Permasalahan D-ISRP merupakan permasalahan NP-hard, maka pada penelitian ini dikembangkan juga algoritma heuristic berbasis interaction theory sebagai teknik solusi. Berdasarkan percobaan numerik yang dilakukan, disimpulkan bahwa algoritma yang dikembangkan dapat menyelesaikan permasalahan D-ISRP dan pada keseluruhan kondisi, opsi perubahan kecepatan menghasilkan performansi yang lebih baik dalam merespon perubahan informasi dwelling time. ============================================================================================= Uncertain dwelling time in a port can cause error in scheduling. The queue on a port will reduce ship utilization and increase the distribution cost because of higher port charge. Ship can also be late on other port that cause inventory stock out. These risks are disadvantages for supplier who implement Vendor Managed Inventory agreement. The supplier will be charged for lateness penalty cost. In order to cope with this problem, a model for dynamic inventory ship routing problem (D-ISRP) is developed in this research. The model will evaluate two alternative options, changing ship speed or changing route visiting sequence, if new information of dwelling time occured. The cost function to evaluate the scheduling options consists of sail bunker consumption, port bunker consumption, ship operational cost, port charge, loading/unloading cost, and penalty cost for late delivery. To solve this problem, an heuristic algorithm based on interaction theory has been developed. The algorithm determines the schedule based on the interaction coefficient of ships and ports. Finally, by some numerical experiments, we concluded that the algorithm has a high reliability in order to cope with DISRP and in general situations changing ship speed option will give better performance rather than changing ship route visiting sequence option

Modelos de otimização para a distribuição de combustíveis em curta distância marítima

Doutoramento em Matemática e AplicaçõesO transporte marítimo e o principal meio de transporte de mercadorias em todo o mundo. Combustíveis e produtos petrolíferos representam grande parte das mercadorias transportadas por via marítima. Sendo Cabo Verde um arquipelago o transporte por mar desempenha um papel de grande relevância na economia do país. Consideramos o problema da distribuicao de combustíveis em Cabo Verde, onde uma companhia e responsavel por coordenar a distribuicao de produtos petrolíferos com a gestão dos respetivos níveis armazenados em cada porto, de modo a satisfazer a procura dos varios produtos. O objetivo consiste em determinar políticas de distribuicão de combustíveis que minimizam o custo total de distribuiçao (transporte e operacões) enquanto os n íveis de armazenamento sao mantidos nos n íveis desejados. Por conveniencia, de acordo com o planeamento temporal, o prob¬lema e divido em dois sub-problemas interligados. Um de curto prazo e outro de medio prazo. Para o problema de curto prazo sao discutidos modelos matemáticos de programacao inteira mista, que consideram simultaneamente uma medicao temporal cont ínua e uma discreta de modo a modelar multiplas janelas temporais e taxas de consumo que variam diariamente. Os modelos sao fortalecidos com a inclusão de desigualdades validas. O problema e então resolvido usando um "software" comercial. Para o problema de medio prazo sao inicialmente discutidos e comparados varios modelos de programacao inteira mista para um horizonte temporal curto assumindo agora uma taxa de consumo constante, e sao introduzidas novas desigualdades validas. Com base no modelo escolhido sao compara¬das estrategias heurísticas que combinam três heur ísticas bem conhecidas: "Rolling Horizon", "Feasibility Pump" e "Local Branching", de modo a gerar boas soluçoes admissíveis para planeamentos com horizontes temporais de varios meses. Finalmente, de modo a lidar com situaçoes imprevistas, mas impor¬tantes no transporte marítimo, como as mas condicões meteorológicas e congestionamento dos portos, apresentamos um modelo estocastico para um problema de curto prazo, onde os tempos de viagens e os tempos de espera nos portos sao aleatórios. O problema e formulado como um modelo em duas etapas, onde na primeira etapa sao tomadas as decisões relativas as rotas do navio e quantidades a carregar e descarregar e na segunda etapa (designada por sub-problema) sao consideradas as decisoes (com recurso) relativas ao escalonamento das operacões. O problema e resolvido por um metodo de decomposto que usa um algoritmo eficiente para separar as desigualdades violadas no sub-problema.Maritime transportation is a major mode of transportation of goods worldwide. Most of cargo of the maritime transport accounted for liquid cargo oil and petroleum products. As Cape Verde is an archipelago, maritime transportation is of great importance for the local economic activity. We consider a fuel oil distribution problem where an oil company is responsible for the coordination of the distribution of oil products with the inventory management of those products at ports in order to satisfy the demands for the several oil products. The objective is to determine distribution policies that minimize the routing and operating costs, while inventory levels are maintained within given limits. For convenience, the planning problem is divided into two related subproblems accordingly to the length of the planning horizon: A short- term and medium-term planning. For the short-term planning problem we discuss mathematical mixed integer programming models that combine continuous and discrete time measures in order to handle with multiple time windows and a daily varying consumption rate of the various oil products. These models are strengthened with valid inequalities. Then the problem is solved using a commercial software. For the second subproblem several mixed integer formulations are discussed and compared for a short time horizon, and assuming constant consumption rates and new valid inequalities are introduced. Then, based on the chosen model, we compare several heuristic strategies that combine the well-known Rolling Horizon, Feasibility Pump and Local Branching heuristics, in or¬der to derive good feasible solutions for planning horizons of several months. Finally, as weather conditions and ports congestion are very impor¬tant in maritime transportation, we present a stochastic model for a short sea shipping problem, where traveling and waiting time are random. The problem is formulated as a two stage recourse problem, where in the first stage the routing and the load/unload quantities are defined, and in the second stage (subproblem) the scheduling of operations is determined. The problem is solved by a decomposition method that uses an efficient separation algorithm to include inequalities from the subproblem

Determining Number of Tanker for Avtur Distribution in PT Pertamina MOR V Using Discrete Event Simulation

Avtur is one of ten main fuel products which is distributed by PT Pertamina (Persero) MOR V from four supply points to eight end depots. In practice, some of avtur depots have experienced critical condition state. Asides from increasing of demand, an analysis has led to two hypotheses: (1) End depot needs closer source of supply to minimize lead-time of replenishment and (2) Current number of operational tankers is insufficient. Discrete Event Simulation (DES) was chosen as a method to test out the hypotheses. Indicators used as performance measurement are service level and total distribution cost. Results of this research show that constructed simulation model can be used for determining the number of tankers required to reach the desired service level in two conditions: the current one, and when a new supply point or loading port is added. The model can accommodate the experimentation considering several conditions when there is: a change of waiting time duration in initial loading ports and/or in the new loading port, a change of storage tank capacity, and a change of new loading port location

Discrete time and continuous time formulations for a short sea inventory routing problem

We consider a fuel oil distribution problem where an oil company is responsible for the routing and scheduling of ships between ports such that the demand for various fuel oil products is satisfied during the planning horizon. The production/consumption rates are given and assumed to be constant. We provide two alternative mixed integer formulations: a discrete time model adapted from the case where the production/consumption rates are varying and a classical continuous time formulation. We discuss different extended formulations and valid inequalities that allow us to reduce the linear gap of the two initial formulations. A computational study comparing the various models accordingly to their size, linear gap and running time, was conducted based on real small-size instances, using a commercial software

Combined ship routing and inventory management in the salmon farming industry

We consider a maritime inventory routing problem for Norway's largest salmon farmer both producing the feed at a production factory and being responsible for fish farms located along the Norwegian coast. The company has bought two new ships to transport the feed from the factory to the fish farms and is responsible for the routing and scheduling of the ships. In addition, the company has to ensure that the feed at the production factory as well as at the fish farms is within the inventory limits. A mathematical model of the problem is presented, and this model is reformulated to improve the efficiency of the branch-and-bound algorithm and tightened with valid inequalities. To derive good solutions quickly, several practical aspects of the problem are utilized and two matheuristics developed. Computational results are reported for instances based on the real problem of the salmon farmer

Greedy Approach For Solving Capacitated Vehicle Routing Problem Of LNG Distribution To Power Plants

Nowadays, LNG industry in Indonesia grows rapidly. It is related to the increasing of electricity demand in Indonesia, in particular, Papua. Since LNG is utilized as fuel of gas turbine power plants. LNG is transported from depot to destinations in order to accomplish the demands at each destination. Furthermore, deciding the number of ships and their routes for transporting LNG to every demand location efficiently is a crucial part to reduce the total operational cost in LNG industries. Hence, the consideration of LNG transportation becomes necessary. The consideration for deciding the number of ships and their routes is not only related to transportation cost but also inventory cost. In addition, the government plan to build some gas turbine power plants in Papua leads to availability of thirteen regasification terminals at there. In response to this problem, this research provided a case study in Papua and proposed a model to determine the number of ships and the optimum ship route to transport LNG from an LNG production terminal to thirteen regasification terminals by considering both transportation cost and inventory cost. The problem in this research is finding and assigning appropriate route and ship, so the demands can be fulfilled. Moreover, distance, power plants demands, transportation cost, and inventory cost were further analyzed by using the greedy approach in order to determine the optimum route for this case. In addition, the ship sizes were limited to four alternatives, which were 2500 m3, 7500 m3, 10000 m3, and 23000 m3. Thus, this problem considered as a capacitated vehicle routing problem. The result recommended the utilization of smaller size vessels with more frequent shipments compared to the earlier research on the same case study. There are seven ships assigned to some particular routes. They are one ship with capacity 2500 m3, four ships with capacity 7500 m3, and two ships with capacity 23000 m3. Each ship is assigned for particular route. Moreover, the result was proven to perform better under uncertain weather in Papua since the ship draft of the smaller vessel will be more adaptable for changing water depth due to changing tides at particular ports