Some Algorithms to Solve a Bi-Objectives Problem for Team Selection

Abstract

In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among ( k h ) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the “deep” and “wide” aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness.

Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem

“…For this purpose, the weighted 𝐿 𝑝 metrics measure the distance of any solution from the reference point. The ideal objective vector is often used as the reference point: Many studies have used CP to approach the MOP problem, such as for university timetabling [40][41][42], team selection [43], in a knowledge-based recommender [44], and project task assignment [45]. However, this t may require pre-defined minimal and maximal values of the objective functions.…”

“…However, this t may require pre-defined minimal and maximal values of the objective functions. Although some of these values are predictable [43], most other cases require the problem to be solved as a single objective function multiple times, which may be costly. Other studies [44,46] show that the referential point may be selected from business estimations, which can provide better performance for the agents in the searching process.…”

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

“…For this purpose, the weighted 𝐿 𝑝 metrics measure the distance of any solution from the reference point. The ideal objective vector is often used as the reference point: There are many studies that have used CP to approach the MOP problem such as for university timetabling [40], [41], [42], or in team selection [43], in knowledge based recommender [44], project task assignment [45]. However, it may require pre-defined minimal and maximal values of the objective functions.…”

“…However, it may require pre-defined minimal and maximal values of the objective functions. Some of these values are predictable [43], most of other cases require to solve the problem as single objective function multiple times, which may be costly. Other studies [44] [46] show that the referential point may be selected from business estimations could provide better performance for the agents in searching process.…”

Meta-Heuristic Algorithms for Learning Path Recommender at MOOC

“…The mission of the model now is to find the solutions closest to this expected point. We have been success used compromise programming for our MOP in the team selection problem [21] and task assignment problem [22]. For the considered problem compromise programming is described as follows.…”