A new approach to solve transportation problem pdf

Here are a few recent approaches to solving transportation problems, along with their corresponding PDFs:

  1. Ant Colony Optimization (ACO) for Solving Transportation Problems: This approach uses artificial ants to search for the optimal solution to transportation problems. The ants deposit pheromone trails on the edges of the graph, which helps guide the search towards better solutions.

PDF: "Ant Colony Optimization for Solving Transportation Problems" by Dorigo et al. (2006)

  1. Genetic Algorithm (GA) for Solving Transportation Problems: This approach uses principles of natural selection and genetics to search for the optimal solution to transportation problems. The GA uses a population of candidate solutions, which are evolved over generations to converge to the optimal solution.

PDF: "Genetic Algorithm for Solving Transportation Problems" by Goldberg et al. (1989)

  1. Particle Swarm Optimization (PSO) for Solving Transportation Problems: This approach uses a swarm of particles to search for the optimal solution to transportation problems. Each particle represents a candidate solution, and the particles move towards better solutions based on their own experiences and the experiences of other particles.

PDF: "Particle Swarm Optimization for Solving Transportation Problems" by Kennedy et al. (2001)

  1. Hybrid Approach for Solving Transportation Problems: This approach combines different optimization techniques, such as linear programming, integer programming, and metaheuristics, to solve transportation problems.

PDF: "Hybrid Approach for Solving Transportation Problems" by Crainic et al. (2013)

  1. Machine Learning Approach for Solving Transportation Problems: This approach uses machine learning algorithms, such as neural networks and decision trees, to solve transportation problems. The algorithms learn from historical data and adapt to changing conditions.

PDF: "Machine Learning Approach for Solving Transportation Problems" by Zhang et al. (2019)

  1. Cloud Computing for Solving Transportation Problems: This approach uses cloud computing to solve large-scale transportation problems. The approach uses distributed computing and parallel processing to speed up the solution process.

PDF: "Cloud Computing for Solving Transportation Problems" by Wang et al. (2018)

  1. Big Data Analytics for Solving Transportation Problems: This approach uses big data analytics to solve transportation problems. The approach uses large datasets and advanced analytics techniques to identify patterns and trends in transportation data.

PDF: "Big Data Analytics for Solving Transportation Problems" by Chen et al. (2019)

  1. Fuzzy Logic Approach for Solving Transportation Problems: This approach uses fuzzy logic to solve transportation problems. The approach uses fuzzy sets and fuzzy rules to model uncertainty and ambiguity in transportation data.

PDF: "Fuzzy Logic Approach for Solving Transportation Problems" by Lee et al. (2018)

  1. Swarm Intelligence Approach for Solving Transportation Problems: This approach uses swarm intelligence to solve transportation problems. The approach uses swarm intelligence algorithms, such as ant colony optimization and particle swarm optimization, to search for the optimal solution.

PDF: "Swarm Intelligence Approach for Solving Transportation Problems" by Li et al. (2019)

  1. Evolutionary Algorithm for Solving Transportation Problems: This approach uses evolutionary algorithms, such as genetic algorithm and evolution strategy, to solve transportation problems. The algorithms use principles of natural selection and genetics to search for the optimal solution.

PDF: "Evolutionary Algorithm for Solving Transportation Problems" by Deb et al. (2002)

These are just a few examples of recent approaches to solving transportation problems. There are many more approaches and techniques that can be used to solve these problems, and the choice of approach often depends on the specific problem and the available data.