A Compromise Decision-making Model for Multi-objective Large-scale Programming Problems with a Block Angular Structure under Uncertainty

This paper proposes a compromise model, based on the technique for order preference through similarity ideal solution (TOPSIS) methodology, to solve the multi-objective large-scale linear programming (MOLSLP) problems with block angular structure involving fuzzy parameters. The problem involves fuzzy parameters in the objective functions and constraints. This compromise programming method is based on the assumption that the optimal alternative is closer to fuzzy positive ideal solution (FPIS) and at the same time, farther from fuzzy negative ideal solution (FNIS).An aggregating function that is developed from LP- metric is based on the particular measure of ‘‘closeness” to the ‘‘ideal” solution.An efficient distance measurement is utilized to calculate positive and negative ideal solutions. The solution process is as follows: first, the decomposition algorithm is used to divide the large-dimensional objective space into a two-dimensional space. A multi-objective identical crisp linear programming is derived from the fuzzy linear model for solving the problem. Then, a single-objective large-scale linear programming problem is solved to find the optimal solution. Finally, to illustrate the proposed method, an illustrative example is provided

Multi-mode resource-constrained project scheduling and soft and hard time windows for ending activities

This study developed a mathematical model to optimize multi-mode resource-constrained project scheduling problem and soft and hard time windows for ending activities. The developed model optimized project scheduling problem in the near real world, taking into account multi-mode time of activities, as well as hard and soft time windows simultaneously. In order to optimize the model, meta-heuristic genetic algorithms and simulated annealing algorithm were used. Input parameters of these algorithms were set by response level method; then, performance of the algorithms was measured in small sized problems using exact solution software. Statistical tests were applied; efficiency of the algorithms was evaluated in solving large-scale real-world problems. Computational results showed that the genetic algorithm had a higher efficiency in optimizing the suggested model and was able to achieve higher quality solutions in lower computing time