Penentuan Rute Terbaik Pendistribusian Gas Industri menggunakan Algoritma Ant Colony Optimization ( Studi Kasus di PT. Samator Gas Industri, Kudus)

Abstrak – Penentuan rute terbaik pendistribusian dapat dilakukan untuk meningkatkan performance dalam proses distribusi. Utilisasi truk di PT Samator Gas Industri saat ini masih rendah yaitu 61,24% dari kapasitas maksimum truk 7000 kg. Penelitian ini bertujuan untuk menentukan rute terbaik pendistribusian gas industri di PT Samator Gas Industri untuk meminimasi jarak tempuh kendaraan dan penghematan biaya bahan bakar serta memaksimalkan utilisasi truk dengan batasan Capacitated Vehicle Routing Problem with Pickup and Delivery for Multiple Products dengan Dynamic Demand yang diselesaikan dengan metode pendekatan algoritma Ant Colony Optimization (ACO). Penelitian ini mempertimbangkan 2 jenis layanan pendistribusian yaitu pickup and delivery dengan batasan kapasitas kendaraan yang homogen. Penelitian ini menggunakan menggunakan 2 kelompok relasi meningkatkan utilisasi truk sebesar 91,86%, menurunkan persentase total jarak tempuh sebesar 15,589% menjadi 398.12 km perhari dari yang sebelumnya 324.11 km perhari, dan penghematan kebutuhan biaya bahan bakar sebesar 15,589%

Comparison of optimisation algorithms for centralised anaerobic co-digestion in a real river basin case study in Catalonia

Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.Peer ReviewedPostprint (published version

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment