Kumar Giri, P., Kumar Maiti, M., Maiti, M. (2016). Profit maximization solid transportation problem under budget constraint using fuzzy measures. Iranian Journal of Fuzzy Systems, 13(5), 35-63. doi: 10.22111/ijfs.2016.2732

Pravash Kumar Giri; Manas Kumar Maiti; Manoranjan Maiti. "Profit maximization solid transportation problem under budget constraint using fuzzy measures". Iranian Journal of Fuzzy Systems, 13, 5, 2016, 35-63. doi: 10.22111/ijfs.2016.2732

Kumar Giri, P., Kumar Maiti, M., Maiti, M. (2016). 'Profit maximization solid transportation problem under budget constraint using fuzzy measures', Iranian Journal of Fuzzy Systems, 13(5), pp. 35-63. doi: 10.22111/ijfs.2016.2732

Kumar Giri, P., Kumar Maiti, M., Maiti, M. Profit maximization solid transportation problem under budget constraint using fuzzy measures. Iranian Journal of Fuzzy Systems, 2016; 13(5): 35-63. doi: 10.22111/ijfs.2016.2732

Profit maximization solid transportation problem under budget constraint using fuzzy measures

^{1}Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Paschim-Medinipur, W.B. 721102, India

^{2}Department of Mathematics, Mahishadal Raj College, Mahishadal, Purba-Medinipur, W.B.-721628, India

Abstract

Fixed charge solid transportation problems are formulated as profit maximization problems under a budget constraint at each destination. Here item is purchased in different depots at different prices. Accordingly the item is transported to different destinations from different depots using different vehicles. Units are sold from different destinations to the customers at different selling prices. Here selling prices, purchasing costs, unit transportation costs, fixed charges, sources at origins, demands at destinations, conveyances capacities are assumed to be crisp or fuzzy. Budget constraints at destinations are imposed. It is also assumed that transported units are integer multiple of packets. So the problem is formulated as constraint optimization integer programming problem in crisp and fuzzy environments. As optimization of fuzzy objective as well as consideration of fuzzy constraint is not well defined, different measures possibility/necessity/credibility of fuzzy event are used to transform the problem into equivalent crisp problem. The reduced crisp problem is solved following generalized reduced gradient(GRG) method using lingo software. A dominance based genetic algorithm (DBGA) and a particle swarm optimization (PSO) technique using swap sequence are also developed for this purpose and are used to solve the model. The models are illustrated with numerical examples. The results obtained using DBGA and PSO are compared with those obtained from GRG. Moreover, a statistical analysis is presented to compare the algorithms.

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