TY - JOUR
ID - 2732
TI - Profit maximization solid transportation problem under budget constraint using fuzzy measures
JO - Iranian Journal of Fuzzy Systems
JA - IJFS
LA - en
SN - 1735-0654
AU - Kumar Giri, Pravash
AU - Kumar Maiti, Manas
AU - Maiti, Manoranjan
AD - Department of Applied Mathematics with Oceanology and
Computer Programming, Vidyasagar University, Paschim-Medinipur, W.B. 721102, India
AD - Department of Mathematics, Mahishadal Raj College, Mahishadal,
Purba-Medinipur, W.B.-721628, India
AD - Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Paschim-Medinipur, W.B. 721102, India
Y1 - 2016
PY - 2016
VL - 13
IS - 5
SP - 35
EP - 63
KW - Solid transportation problem
KW - Budget constraints
KW - Possibility /Necessity/Credibility measure
KW - Dominance based genetic algorithm
KW - Particle Swarm Optimization
DO - 10.22111/ijfs.2016.2732
N2 - 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. Unitsare 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. Itis 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. Asoptimization 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.
UR - https://ijfs.usb.ac.ir/article_2732.html
L1 - https://ijfs.usb.ac.ir/article_2732_cf202305823d950fc1d3a3c0438eefd2.pdf
ER -