Three new methods to find initial basic feasible solution of transportation problems

Abstract

The transportation problems have attracted many researchers in optimization because of their applications in several areas of science and real life. Therefore, solving of these problems especially finding initial basic feasible solution for them would be significant. Although there are some heuristic approaches to find initial solution, but there is no any efficient algorithm with its Matlab code to solve random large size transportation problems. In this paper, an efficient algorithm in three cases with its Matlab code is proposed which even is better than the best algorithm in references by solving some test problems and also our generated large size problems. The algorithm is forcefully efficient for problems with large size and it has sufficiently suitable results, at least in on case of the algorithm, for test problems in the references according to computational results.

Novel metaheuristic based on multiverse theory for optimization problems in emerging systems

“…Finding a suitable feasible solution of transportation problem is remarkable, so MVA has been applied to some random transportation problems [ 19 ]. The obtained results have been listed in Table 11 .…”

“…Note that the agg, qap8, SC50A, AFIRO are linear programming test problems in the "NETLIB Linear Programming test set" which is a collection of real-life linear programming examples. Finding a suitable feasible solution of transportation problem is remarkable, so MVA has been applied to some random transportation problems [19]. The obtained results have been listed in Table 11.…”

A combination of TDM and KSAM to determine initial feasible solution of transportation problems

“…Meanwhile, the solving of TP example used Algorithm 3 (TDM 1 [12]) raised the potential to produce more than on IFS. It is because the highest penalty has equal value.…”

“…Incessant Allocation Method (IAM) [10] and Allocation Table Method (ATM) [11] are iterative methods based on the allocation table. Modification of TDM (Total Different Method) 1 considers penalty only for a row of TP table [12]. Global Minimum Method (GMM) [13].…”

Volcano eruption algorithm for solving optimization problems

“…Additionally, researchers have tried to solve optimization problems by simulating several algorithms based on behavior of animals and insects, natural phenomena, or scientific theories [4][5][6][7][8][9][10][11][12][13]. Some of these proposed algorithms are: artificial bee colony algorithm [4], krill herd algorithm [5], social spider optimization [7], chicken swarm optimization (CSO) [8], big bang algorithm (BBA) [10], laying chicken algorithm (LCA) [11,18], modified genetic algorithm [12], [30], combined metaheuristic and classic algorithm [13]. Almost all previous metaheuristics have been inspired from behavior of animals or insects and only one of them has been simulated from a scientific theory [10].…”