Path: Top -> Journal -> Jurnal Internasional -> Fuzzy Information and Engineering -> 2021 -> Volume 13, Issue 1
A Profit Maximisation Solid Transportation Problem Using Genetic Algorithm in Fuzzy Environment
Oleh : S. Samanta, A. Ojha, B. Das & S. K. Mondal, Fuzzy Information and Engineering
Dibuat : 2021-09-02, dengan 0 file
Keyword : All unit discount, genetic algorithm, profit maximisation solid transportation problem, fuzzy resource and demand
Url : http://www.tandfonline.com/doi/full/10.1080/16168658.2021.1915454
Sumber pengambilan dokumen : Web
In this paper, a solid transportation problem has been considered in which the transportation is accomplished in two stages firstly, from the origin(s) to the near by station(s) of the destination(s) and secondly, from the near by station(s) to the exact destination(s). Here, a fuzzy AUD (all unit discount) policy has been introduced based upon the amount of transportation along with a fuzzy fixed charge. In addition, a budget constraint has been incorporated taking fuzzy unit transportation cost. Then the proposed model has been converted into a single objective optimisation problem using interval arithmetic method. To solve the model, Genetic Algorithm (GA) has been used depending on roulette wheel selection, crossover and mutation. Finally, a numerical example has been illustrated to study the feasibility of the model.
Deskripsi Alternatif :In this paper, a solid transportation problem has been considered in which the transportation is accomplished in two stages firstly, from the origin(s) to the near by station(s) of the destination(s) and secondly, from the near by station(s) to the exact destination(s). Here, a fuzzy AUD (all unit discount) policy has been introduced based upon the amount of transportation along with a fuzzy fixed charge. In addition, a budget constraint has been incorporated taking fuzzy unit transportation cost. Then the proposed model has been converted into a single objective optimisation problem using interval arithmetic method. To solve the model, Genetic Algorithm (GA) has been used depending on roulette wheel selection, crossover and mutation. Finally, a numerical example has been illustrated to study the feasibility of the model.
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