Abstract
<p>In this paper, we proposed three heuristic algorithms to solve multiobjective transportation problems, the first heuristic algorithm used to minimize two objective functions (total flow time and total late work), the second one used to minimize two objective functions (total flow time and total tardiness) and the last one used to minimize three objective functions (total flow time, total late work and total tardiness), where these heuristic algorithms which is different from to another existing algorithms and providing the support to decision markers for handing time oriented problems.</p>
Highlights
Decision making is the process of identifying and choosing alternatives based on the values and preferences of the decision maker
We proposed three heuristic algorithms to solve multiobjective transportation problems, the first heuristic algorithm used to minimize two objective functions, the second one used to minimize two objective functions and the last one used to minimize three objective functions, where these heuristic algorithms which is different from to another existing algorithms and providing the support to decision markers for handing time oriented problems
One must acknowledge the presence of several criteria that lead to the development of multicriteria decision making
Summary
Decision making is the process of identifying and choosing alternatives based on the values and preferences of the decision maker. The purpose of any algorithm process is to find, for each instance a feasible solution called optimal, that minimize the objective function. This usual meaning of the optimum makes no sense in the multiobjective case because it doesn’t exist, in most of the cases, a solution optimizing all objectives simultaneously. Example(1): Consider the problem (P1 ) with the following data: DESTINATIONS i/j
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