An Improved Heuristic Approach for the Interval Immune Transportation Problem

Abstract We study the problem of determining the bounds of the optimal cost of a transportation problem when the capacity of the suppliers and the demand of the customers vary over an interval. We consider transportation costs such that the transportation paradox does not arise. We design a new heuristic approach based on some polyhedral properties of the problem and provide a novel integer linear programming mathematical formulation to solve it exactly. Our computational results, carried out on benchmark instances from the literature and on some new instances, show that our heuristic algorithm greatly outperforms the best solution approaches currently used.

[1]  M. A. Hoque,et al.  A heuristic solution technique to attain the minimal total cost bounds of transporting a homogeneous product with varying demands and supplies , 2014, Eur. J. Oper. Res..

[2]  Monica Gentili,et al.  Bounds on the worst optimal value in interval linear programming , 2019, Soft Comput..

[3]  S. Stefanov Characterization of the optimal solution of the convex separable continuous knapsack problem and related problems , 2019, Journal of Information and Optimization Sciences.

[4]  Gerhard J. Woeginger,et al.  Which matrices are immune against the transportation paradox? , 2003, Discret. Appl. Math..

[5]  Shiang-Tai Liu,et al.  The total cost bounds of the transportation problem with varying demand and supply , 2003 .

[6]  John W. Chinneck,et al.  Linear programming with interval coefficients , 2000, J. Oper. Res. Soc..

[7]  Wlodzimierz Szwarc,et al.  The Transportation Paradox. , 1971 .

[8]  Panos M. Pardalos,et al.  A Numerical Method for Concave Programming Problems , 2005 .

[9]  Ciriaco D’Ambrosio,et al.  Best and Worst Values of the Optimal Cost of the Interval Transportation Problem , 2017 .

[10]  M. Gentili,et al.  The Outcome Range Problem , 2019, 1910.05913.

[11]  F. L. Hitchcock The Distribution of a Product from Several Sources to Numerous Localities , 1941 .

[12]  Milan Hladík Optimal value range in interval linear programming , 2009, Fuzzy Optim. Decis. Mak..

[13]  Ciriaco D’Ambrosio,et al.  The optimal value range problem for the Interval (immune) Transportation Problem , 2020 .

[14]  Muhammad Munir Butt,et al.  An upper bound on the minimal total cost of the transportation problem with varying demands and supplies , 2017 .

[15]  Fabio Tardella,et al.  On a class of functions attaining their maximum at the vertices of a polyhedron , 1989, Discret. Appl. Math..

[16]  Miguel Delgado,et al.  Interval and fuzzy extensions of classical transportation problems , 1993 .

[17]  P. Pardalos,et al.  Checking local optimality in constrained quadratic programming is NP-hard , 1988 .

[18]  Milan Hladík,et al.  Interval linear programming under transformations: optimal solutions and optimal value range , 2019, Central Eur. J. Oper. Res..

[19]  S. Stefanov Separable Programming - Theory and Methods , 2001 .