ANALYSIS OF FACTORS THAT AFFECT TO INFRASTRUCTURE IMPROVEMENT OF SYUKURAN AMINUDDIN AMIR AIRPORT IN BANGGAI DISTRICT

The airport is one of the important infrastructures in the air transportation field that functions to provide aircraft flight services so that there is a need for compatibility between the fleet and its infrastructure, in this case improving the infrastructure of Syukuranan Aminuddin Amir (SAA) Airport. The aim of this study is to analyze the factors that influence the improvement of the infrastructure of SAA Airport. The location of the study was conducted at the SAA Airport, Banggai Regency, Central Sulawesi Province. Data collection was carried out by distributing questionnaires to 120 respondent passengers on airplanes who use the SAA airport. Data analysis using factor analysis methods. The analysis result shows there are 11 factors that affect infrastructure improvement of SAA Airport is socio-economic, airport service quality, operator service quality, flight traffic demand, air transport service demand, air transportation mode selection, airport departure facility (waiting room) and airplane ticket purchase decisions, parking area and waiting room, flight schedule and potential regional commodity yields, service and process baggage handling, and transportation accessibility to the airport.

Differences in Levels of Autonomy and Fitness of Elementary School Students Based on Transportation Mode

It is known that in addition to biking and walking, many students who prefer to be taken by parents or use an online motorcycle taxi to go to school. Generally, going to school using a bicycle is an alternative means of transportation that is preferred, but because of the lack of parking lots of students who ultimately prefer to be picked up and dropped off. In addition, parenting parents who are too protective of children to require children to be picked up when going to school also indirectly have an unfavorable impact on the child's level of fitness and independence. Whereas for elementary school children, the level of fitness and independence are things that must be developed for their future survival. Children must understand the importance of physical activity and implement it in their lives. For example, children who go to school, they must be more able to think about how to move their body to get to school rather than just relying on the transfer of parents. By walking to school, will increase physical activity of children. Therefore, researchers are then interested in conducting further research. The results of this study are that there are significant differences in the fitness variable of transportation used with a significance level of 0,000 and an F value of 21,914 with a mean difference of 73,393. Walking is the most dominant factor to improve fitness compared to cycling and parents take a motorbike ride to and from school. As for the independence variable there is no significant difference in the mode of transportation used with a significance value of 0.414 with an F value of 0.890. The conclusion of the research is the use of modes of transportation to the right school can increase children's fitness. Walking is the best transportation to improve fitness then followed by cycling and parents take a ride on a motorcycle. However, to increase a significant independence, it is still not possible to use transportation mode selection because parental care is the determining factor for children's independence other than the education system in schools and the surrounding environment.

Multi-Objective Optimization for Green Dual-Channel Supply Chain Network Design Considering Transportation Mode Selection

A well-designed supply chain network should not only meet the efficient cost but also realize the sustainable effect on environment. The purpose of this article is to develop a multi-objective model to capture the trade-off between total cost and environmental performance in the green dual-channel supply chain network. Moreover, the transportation mode has been considered as a decision variable. With regard to the complexity of such network, a new swarm intelligence algorithm known as a multi-objective particle swarm optimization (MOPSO) algorithm has been employed to tackle this problem. The effectiveness of the present model and approach is evaluated by a numerical experiment, and the results show that the added environmental performance is actually proportional with the increased cost. Additionally, the comparison between different mode decisions shows that a better trade-off between two objectives will be obtained when considering the transportation mode selection.

Sustainable Supply Chain Network Design with Carbon Footprint Consideration: A Case Study in China

With the environment concern increasing, corporations are facing new challenges on carbon management in supply chain network. In this paper, environmental consideration is introduced to traditional supply chain management, and the sustainable supply chain (SSC) is designed considering carbon footprint. We develop a mixed integer linear programming (MILP) model to get the optimal decisions on partner selection, technology selection, and transportation mode selection, as well as material procurement, product supply, and recovery mode. For validating the model, a beverage company in China is used. We also analyze the impact of supply chain uncertainties such as carbon emission price and recovery rate of returned products on the decision of SSC design.

Multi-Objective Optimization for Green Dual-Channel Supply Chain Network Design Considering Transportation Mode Selection

A well-designed supply chain network should not only meet the efficient cost but also realize the sustainable effect on environment. The purpose of this article is to develop a multi-objective model to capture the trade-off between total cost and environmental performance in the green dual-channel supply chain network. Moreover, the transportation mode has been considered as a decision variable. With regard to the complexity of such network, a new swarm intelligence algorithm known as a multi-objective particle swarm optimization (MOPSO) algorithm has been employed to tackle this problem. The effectiveness of the present model and approach is evaluated by a numerical experiment, and the results show that the added environmental performance is actually proportional with the increased cost. Additionally, the comparison between different mode decisions shows that a better trade-off between two objectives will be obtained when considering the transportation mode selection.