No abstract

Distribution systems play a significant role in logistics operations. For the companies with consumer goods products this even more important as consumer goods production has fairly cheap price compared to the distribution cost that must be spent by the company. In addition, increased fuel costs have urged the company to be more efficient in planning and schedule the transportation routes. This paper presents the application of the Particle Swarm Optimization (PSO) algorithm to minimize the travel distance and total cost of a Heterogeneous Fleet Vehicle Routing Problem (HFVRP). Experimental results from its application to a real-world case study are presented. The model in this research is the HFVRP where vehicles have different capacities, variable costs, and fixed costs. PSO algorithm was applied because of the high number of customers served, and therefore the exact methods may not be sufficient. PSO parameter setting which produced the optimum result was with the number of swarms 50, C1 1,5, and C2 2 determined through the design of the experiment. The results of computation show that using PSO can minimize the total traveled distance with an average savings of 51.55% and minimize total cost with an average savings of 44.92% from the existing vehicle routes operated by the company.

This paper presents the application of a sweep algorithm to optimize distribution route of a Capacitated Vehicle Routing Problem (CVRP). The objective of this research is to find a set of optimal routes that minimizes the number of vehicles required and total distance travelled for all vehicles to serve customers. The algorithm consists of two phases, a clustering phase and a route generation phase. Experimental results from its application to a real-world case of PT Eastern Pearl Flour Mills, Indonesia are presented. The obtained vehicle routes which are compared against the existing daily distribution routes showing that significant savings can be gained on the daily cost of transportation related expenses. The number of vehicle routes and vehicle used for delivery are reduced and therefore, the distance travelled and the utilization of the vehicles are also improved. The algorithm applied in this research would be suitable for solving practical vehicle routing optimization problems in other logistics companies.

The seafood industry has experienced a gradual increase in exports each year which has an impact on the high value of competitiveness in companies. PT. Nirvana Niaga Sejahtera is a seafood industry engaged in the tuna commodity export trade. The concept of competitiveness analysis and the best supplier selection used is the Five Forces Analysis concept based on five strength indicators which are incoming company threats, buyers, consumers, substitute product manufacturers, analysis of supplier strengths and AHP methods use expert choice 11 software to produce the company's best supplier. The results of the Five Forces Analysis model show that investor threats to new companies and the number of similar companies can affect the bargaining power of buyers and the quality of suppliers. The selection model using AHP produces alternative for supplier that are Supplier A (Toli-Toli) 0.385, Supplier B (Kendari) 0.322, Supplier C (Ternate) 0.279.

Forecasting for passenger demand in a new railway service is a challenging activity. There are many variables that could affect the number of passengers that will use railway services and many of those variables may be unique to each specific situation. In this study, we present a study that attempts to forecast railway passengers for a newly constructed railway line of Makassar -Parepare in South Sulawesi, Indonesia. We propose a combination of the System Dynamics and Artificial Neural Networks (ANN). The system dynamics is a simulation method that is used to generate a set of training data for the ANN. The initial data were collected through a survey of willingness to use