MATHEMATICAL MODEL AND DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SELECTING MULTIPLE DEPOT LOCATIONS AND VEHICLE ROUTING PROBLEM WITH MULTIPLE TRIPS, AND SIMULTANEOUS PICKUP AND DELIVERY
Vehicle routing problem is a complex way to determine a vehicle’s route to serve some customers with optimal operational cost. One of the problem’s complexities in determining a vehicle’s route is when there is a process of pickup and delivery simultaneously like in galoon beverages. In such cases,...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/43413 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Vehicle routing problem is a complex way to determine a vehicle’s route to serve some customers with optimal operational cost. One of the problem’s complexities in determining a vehicle’s route is when there is a process of pickup and delivery simultaneously like in galoon beverages. In such cases, other than the simultaneous pickup and delivery characteristics, the company usually has multiple depots that are spread across different locations to fulfill the customers’ demand. On this account, the process of serving the customers should be done on the most optimal depot based on its location for each customer. Other than that, to minimize the vehicles’ fixed cost, each vehicles should be allowed to serve on multiple trips.
The objective function of the problem’s mathematical model is to minimize the total transportation cost by minimizing the total vehicles being used, choosing the most optimal depots, and calculating the most optimal total duration. The model solution is solved with an analytical method. In building the algorithm to solve the problem, initial solution to determine whether to open or close an operating depot uses random method and then the routing is built by using Sequential Insertion (SI). The initial solution is then improved to get a better feasible solution using Discrete Particle Swarm Optimization (DPSO) algorithm.
The algorithm that is regenerated to acquire faster computation time on average compared to optimal solution using analytical method, specifically the gap is -99.9891%. Compared to the objective function, the calculation using algorithm is close to the optimal solution that’s resulted from the analytical method with a gap of 0.4%.
Keywords: simultaneous pickup and delivery, multiple depots, multiple trips, location routing problem, analytic, Discrete Particle Swarm Optimization, Sequential Insertion |
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