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In mathematics and economics, transportation theory or transport theory is a name given to the study of optimal transportation and allocation of resources. The problem was formalized by the French mathematician Gaspard Monge in 1781. [1] In the 1920s A.N. Tolstoi was one of the first to study the transportation problem mathematically.
Transportation costs are independent of the shipped amount; The transshipment problem is a unique Linear Programming Problem (LLP) in that it considers the assumption that all sources and sinks can both receive and distribute shipments at the same time (function in both directions) [1]
However, some problems have distinct optimal solutions; for example, the problem of finding a feasible solution to a system of linear inequalities is a linear programming problem in which the objective function is the zero function (i.e., the constant function taking the value zero everywhere).
A transportation problem from George Dantzig is used to provide a sample GAMS model. [6] This model is part of the model library which contains many more complete GAMS models. This problem finds a least cost shipping schedule that meets requirements at markets and supplies at factories. Dantzig, G B, Chapter 3.3. In Linear Programming and ...
Another related problem is the bottleneck travelling salesman problem: Find a Hamiltonian cycle in a weighted graph with the minimal weight of the weightiest edge. A real-world example is avoiding narrow streets with big buses. [15] The problem is of considerable practical importance, apart from evident transportation and logistics areas.
Cutting stock problem Linear programming Kantorovich inequality Kantorovich metric Kantorovich theorem Kantorovich–Rubinstein metric Monge–Kantorovich transportation problem Szász–Mirakjan–Kantorovich operator: Awards: Nobel Memorial Prize in Economic Sciences (1975) Stalin Prize (1949) Scientific career: Fields: Mathematics: Institutions
The discovery of linear time algorithms for linear programming and the observation that the same algorithms could in many cases be used to solve geometric optimization problems that were not linear programs goes back at least to Megiddo (1983, 1984), who gave a linear expected time algorithm for both three-variable linear programs and the ...
Such a formulation is called an optimization problem or a mathematical programming problem (a term not directly related to computer programming, but still in use for example in linear programming – see History below). Many real-world and theoretical problems may be modeled in this general framework. Since the following is valid: