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%

A Hybrid Water Flow-Like Algorithm and Variable Neighbourhood Search for Traveling Salesman Problem

Various metaheuristic methods have been proposed earlier and applied for solving the Travelling Salesman Problem (TSP). Water Flow Algorithm (WFA) is one of the recent population-based metaheuristic optimization techniques used for solving this problem. Past research has shown that improving WFA local search strategy has a significant impact on the algorithm performance. Therefore, this paper aims to solve TSP by enhancing WFA searching strategy based on a Variable Neighbourhood Search (VNS) known as hybrid WFA-VNS. It is a mixture of the exploration of WFA and the exploitation capability of VNS. This study is conducted in two stages: Pre-experiment and initial experiment. The objective of doing pre-experiment is to select four neighborhood structures to be used for the initial experiment. At the first stage, three instances are used, and there are five neighborhood structures involved. Those neighborhood structures are two opt, three opt, four opt, swapping, and insertion move. Because of pre-experiment, it discovers four best neighborhood structures, which are two opt, three opt, exchanging and insertion move. These neighborhood structures will be used in the initial experiment, which an improvement approach is employed. In an initial experiment, the performance of the proposed hybrid WFA-VNS is further studied and tested on 26 established benchmarked symmetric TSP datasets using four neighborhood structures selected in pre-experiment earlier. The TSP datasets involved are categorized into three types: small datasets, medium datasets, and large datasets. Selected neighborhood structures obtained in pre-experiment are applied and generated randomly to intensify the initial solution achieved at an earlier stage of hybrid WFA-VNS. The results of the comparison show that this hybrid approach represents an improvement and able to produce competitive results

A Modified Harmony Search Algorithm for Solving the Dynamic Vehicle Routing Problem with Time Windows

The Vehicle Routing Problem (VRP) is a classical combinatorial optimization problem. It is usually modelled in a static fashion; however, in practice, new requests by customers arrive after the initial workday plan is in progress. In this case, routes must be replanned dynamically. This paper investigates the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) in which customers’ requests either can be known at the beginning of working day or occur dynamically over time. We propose a hybrid heuristic algorithm that combines the harmony search (HS) algorithm and the Variable Neighbourhood Descent (VND) algorithm. It uses the HS to provide global exploration capabilities and uses the VND for its local search capability. In order to prevent premature convergence of the solution, we evaluate the population diversity by using entropy. Computational results on the Lackner benchmark problems show that the proposed algorithm is competitive with the best existing algorithms from the literature