Efficient Solution Strategies for Multi Objective Transportation Models with Practical Relevance
Keywords:
Multi-Objective Transportation Problem, Pareto Optimality, Efficient Solution Strategies, Evolutionary Algorithms, Fuzzy Programming, Sustainable LogisticsAbstract
The increasingly intricate real-world transportation and logistics systems have created a need to generate multi-objective transportation models which can serve and fulfil incompatible goals like minimization of cost, time efficiency, service quality, and environmental sustainability. The practical complexities can hardly be addressed using the traditional single-objective transportation models, hence the desire to seek efficient and powerful solution strategies. This paper had given a detailed analysis of multi-objective transportation models including their mathematical representation, Pareto optimality, and trade-off. Different classical solution methods such as weighted sum method, goal programming method, and the sophisticated methods such as fuzzy programming method and evolutionary algorithms method were contrasted based on their computational capability and their application to real-life problems. Moreover, practical applications in the area of supply chain management, sustainable transportation planning, and decision-support systems were also emphasized in the study with managerial and policy implications. Combining theoretical backgrounds with the application of relevance, the research was valuable in addition to the selection of proper solution strategies of complex transportation issues, and it formed a basis of research in the future of multi-objective transportation optimization.
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