This study aims to find a set of vehicles routes with the minimum total transportation time for pharmaceutical distribution at PT. XYZ in West Jakarta. The problem is modeled as the capacitated vehicle routing problem (CVRP). The CVRP is known as an NP-Hard problem. Therefore, a simulated annealing (SA) heuristic is proposed. First, the proposed SA performance is compared with the performance of the algorithm form previous studies to solve CVRP. It is shown that the proposed SA is useful in solving CVRP benchmark instances. Then, the SA algorithm is compared to a commonly used heuristic known as the nearest neighborhood heuristics for the case study dataset. The results show that the simulated Annealing and the nearest neighbor algorithm is performing well based on the percentage differences between each algorithm with the optimal solution are 0.03% and 5.50%, respectively. Thus, the simulated annealing algorithm provides a better result compared to the nearest neighbour algorithm. Furthermore, the proposed simulated annealing algorithm can find the solution as same as the exact method quite consistently. This study has shown that the simulated annealing algorithm provides an excellent solution quality for the problem.
In the oil and gas industry, one of the key activities is the drilling process where the oil or natural gas are extracted. The process requires the supply of rolled products known as the Oil Country Tubular Goods (OCTG). As an important drilling component, OCTG has a high demand, which in some case it is not balanced with the optimal distribution route and cost that may affect the industry's profit. In order to obtain the optimal distribution routes, this research compares transport routes generated by Sweep and Savings Algorithm to solve the Capacitated Vehicle Routing Problem, by taking an oil and gas company with inefficient OCTG distribution in Sumatera as the object of the study case. Additionally, the total transportation cost is calculated to further compare the results. The research concludes that Sweep Algorithm produces the most efficient routes with the lowest total transportation cost of Rp.18,890,875,000 per year, or 24% less than total cost derived from Savings Algorithm route.
Operation Timbang (OPT) Plus is one of the Philippines’ programs that focuses on nutrition by conducting an annual assessment for 0-59 months old children in barangays to identify the malnutrition data in the area. The barangay is the smallest administrative entity in the Philippines. OPT is a plan of action that estimates the number of malnutrition individuals and identifies those who will get prioritized programs in the community. The Iloilo City Health Office conducted the program in seven districts in the Philippines. The office planned to establish a community centre and playground facility based on the priority/demand areas. Maximum Covering Location Problem (MCLP) is used for this study to determine the optimal location that covers the area. A Mathematical Programming Language (AMPL) is used to apply mathematical programming to the MCLP. The results can be used to identify the optimal facility and the maximum coverage of the demand points. The experiment showed that the facility located in Mandurriao District is the optimal facility location. For Underweight/Severely Underweight children, a maximum total of 646 are covered, and for the Overweight/Obese, 1,041 are covered for the chosen facility. In addition, the findings of the sensitivity analysis indicate that the building of the three facilities in the case study can offer 100 percent of the required coverage area.
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