The DEA model developed by Charnes et al. [8] is a mathematical programming model that considers several inputs and outputs.
This paper presents a comprehensive multi-objective mixed integer mathematical programming model which considers cell formation and production planning problems simultaneously.
A recursive mathematical programming model that considers regular maintenance, emergent maintenance, and maintenance improvement factors is used to obtain the optimal equipment PM schedule.
The problem of interest is formulated as a bi-objective mathematical programming model that considers the travel cost of road users in each origin-destination pair and the investment return of the whole network.
In the context of fuzzy mathematical programming, the paper jointly considers fuzzy/flexible constraints for fuzziness, fuzzy coefficients for lack of knowledge, and fuzzy goal of decision maker(s).
Here, we describe a more flexible framework for solving this problem based on an efficient mathematical programming procedure (IQP) that considers information about the geometry of the point configurations, as well as the consistency of the density distribution in the neighborhood of feature points.
In most of the real-life decision-making problems in mathematical programming, the parameters are considered as random variables.
There are so many papers which considered mathematical programming for modeling the problems in various areas (Mousavi et al. 2014; AriaNezhad et al. 2013; Alimardani et al. 2013; Seifbarghy et al. 2015).
By using a natural generalization of the concept of a set, the fuzzy set, which was introduced initially by Zadeh [1], considering mathematical programming problems which are expressed as optimizing some goal function given certain constraints, this be relaxed by means of a subjective gradation.
A distinct advantage of our applied exact solution technique versus other possible heuristic approaches is that it guarantees the global optimality of the obtained solution for the considered mathematical programming formulation, rather than just an adequate solution.
