A differential evolution algorithm to solve multi-skilled project portfolio scheduling problems

Solving a new mixed integer non-linear programming model of the multi-skilled project scheduling problem considering learning and forgetting effect on the employee efficiency

“…Regarding this point, the problem could not be solved by presented methods for MRCPSP even for the medium size problems, which are mainly based on complete numeration [7]. Generally, the main objective in this problem is to minimize the project makespan [8].…”

“…The reason is that if an employee is not allocated to the assumed skill k 1 and activity j 1 , according to (11) the variables will take zero for all possible values of time. If the allocation is performed, the related variables will be equal to 1 in the time interval of starting to finishing time of the activity, because of (7) and (8), and they will take zero value in times out of the mentioned interval, due to (9) and (10). This should be done because the number of allocated staff members to each skill of an activity must exactly equal to the required value according to (12).…”

“…Knowing the starting and finishing time of activities, we are able to take into account the precedence relations among them by (6). When an activity starts, in order to have all the required skills started simultaneously without preemption, we use (7) and (8). When the employee i is assigned to the activity j, constraints (9) and (10) make him devote his time as much as d j merely to that activity from beginning to the end, and consequently he is not able to do another job in that time.…”

Heuristic optimization for software project management with impacts of team efficiency

“…Therefore heuristic methods are among popular choices for practical problem solving, such as genetic algorithms (GAs) [1], GA with a pre-decision algorithm [5], differential evolution algorithms [6], ant colony optimization algorithms [7] [8], artificial bee colony [9], meta heuristic algorithms of simulated annealinggenetic algorithm-Tabu search [10], frog-leaping algorithm [11], branch-and-bound methods [12], hybrid mixed-integer linear programming and constraint programming algorithms [13] [14]. Pareto optimality is used as the multi-objective optimization method for reducing the probability of overtime during software projects [15].…”

“…Equation (6) indicates that the team efficiency defined in this paper may affect selecting the desirable solutions through the objective of projectDuration.…”