Personality Mining System for Automated Applicant Ranking

Abstract. In the last decades the explosion of ICT has opened up new avenues regarding peoples' accessibility to new job opportunities. Current technological advances in conjunction with people's online presence provide a great opportunity to automate the recruitment process and make it more effective. In this paper, we propose a novel approach for improving the efficiency of erecruitment systems. Our approach relies on the linguistic analysis of data available for job applicants, in order to infer the applicants' personality traits and rank them accordingly. To showcase the functionality of our method, we employed it in a web based e-recruitment system that we implemented

Mixed integer least squares optimization for flight and maintenance planning of mission aircraft

We address the problem of generating a joint flight and maintenance plan for a unit of mission aircraft. The objective is to establish a balanced allocation of the flight load and the maintenance capacity to the individual aircraft of the unit, so that its long-term availability is kept at a high and steady level. We propose a mixed integer nonlinear model to formulate the problem, the objective function of which minimizes a least squares index expressing the total deviation of the individual aircraft flight and maintenance times from their corresponding target values. Using the model's special structure and properties, we develop an exact search algorithm for its solution. We analyze the computational complexity of this algorithm, and we present computational results comparing its performance against that of a commercial optimization package. Besides demonstrating the superiority of the proposed algorithm, these results reveal that the total computational effort required for the solution of the problem depends mainly on two crucial parameters: the size of the unit (i.e., the number of aircraft that comprise it) and the space capacity of the maintenance station. (c) 2012 Wiley Periodicals, Inc. Naval Research Logistics, 201

A Multiobjective Model for Maximizing Fleet Availability under the Presence of Flight and Maintenance Requirements

Every aircraft, military or civilian, must be grounded for maintenance after it has completed a certain number of flight hours since its last maintenance check. In this paper, we address the problem of deciding which available aircraft should fly and for how long, and which grounded aircraft should perform maintenance operations, in a group of aircraft that comprise a combat unit. The objective is to achieve maximum availability of the unit over the planning horizon. We develop a multiobjective optimization model for this problem, and we illustrate its application and solution on a real life instance drawn from the Hellenic Air Force. We also propose two heuristic approaches for solving large scale instances of the problem. We conclude with a discussion that gives insight into the behavior of the model and of the heuristics, based on the analysis of the results obtained

Branch and price for covering shipments in a logistic distribution network with a fleet of aircraft

We consider the problem of covering a set of shipments in a logistic distribution network with a fleet of aircraft. The aim is to cover as many shipments as possible, while also minimizing the number of aircraft utilized for that purpose. We develop an integer programming model and a branch and price solution algorithm for this problem. The proposed methodology utilizes a master problem that covers the maximum possible number of shipments using a given set of aircraft-routes, and a column generation subproblem that generates cost-effective aircraft-routes which are fed into the master problem. We describe the proposed methodology, illustrating how it can be modified in order to accommodate several problem extensions. We also investigate how its efficiency is affected by various key design parameters. We conclude with extensive experimental results demonstrating its computational performance. © 2017 Informa UK Limited, trading as Taylor & Francis Group

Column generation for scheduling shipments within a supply chain network with the minimum number of vehicles

We consider the problem of scheduling a set of shipments between different nodes of a supply chain network. Each shipment has a fixed departure time, as well as an origin and a destination node, which, combined, determine the duration of the associated trip. The aim is to schedule as many shipments as possible, while also minimizing the number of vehicles utilized for this purpose. We develop an integer programming model and an associated branch and price solution algorithm for this problem. The optimal solution to the LP relaxation of the problem is obtained through column generation, a methodology for solving linear programs with a huge number of variables, without explicitly considering all of them. In the context of our application, the proposed methodology utilizes a master problem that schedules the maximum possible number of shipments using only a small set of vehicle-routes, and a column generation (colgen) sub-problem that generates cost-effective vehicle-routes which are fed into the master problem. The optimal solution to the colgen sub-problem is obtained with an efficient network optimization solution algorithm, which outperforms existing commercial optimization software packages that can be used alternatively for that purpose. After finding the optimal solution to the LP relaxation of the problem, the algorithm branches on the fractional decision variables (vehicle-routes), in order to reach the optimal integer solution. Special branching rules that expedite the algorithm's performance are utilized for this purpose. We describe in detail the steps of the proposed solution algorithm, focusing on several problem aspects that have a strong influence on its performance. We conclude with limited computational results that demonstrate the algorithm's performance on a particular case study, and a discussion of how various computational difficulties that can possibly arise can be handled

Column generation for optimal shipment delivery in a logistic distribution network

We consider a logistic distribution decision-making problem, in which a vehicle fleet must carry out a set of deliveries between pairs of nodes of the underlying transportation network. The goal is to maximize the number of deliveries that will be carried out, while also minimizing the number of vehicles utilized to this end. The optimization is lexicographic in the sense that the former objective exhibits higher priority than the latter one. For this problem, we develop an integer programming model formulation and an associated column generation-based solution methodology. The proposed methodology utilizes a master problem which tries to fulfill the maximum possible number of deliveries given a specific set of vehicle routes and a column generation subproblem which is used to generate cost-effective vehicle routes1, for improving the master problem solution. We describe the steps of the proposed methodology, illustrating how it can be modified to accommodate interesting problem variations that often arise in practice. We also present extensive computational results demonstrating the computational performance of the proposed solution algorithm and illustrating how its behavior is influenced by key design parameters. © 2017, Springer International Publishing AG

Optimal assignment of aircrew trainees to simulator and classroom training sessions subject to seniority and preference restrictions

We develop an integer programming model and an exact solution methodology for assigning aircrew members to training sessions which they need to attend in order to fulfill the requirements for renewal of their flight capability. The aim is to find the optimal assignment that minimizes the number of unassigned trainees, while also satisfying a variety of seniority, preference, capacity and language compatibility restrictions. The proposed methodology partitions the aircrew members into distinct groups based on their seniority, making the optimal assignment decisions pertaining to each of these groups sequentially. We present extensive computational results demonstrating that the proposed methodology enables the solution of realistic size problems in moderate computational times. © 2016 Elsevier Lt

An exact solution algorithm for maximizing the fleet availability of an aircraft unit subject to flight and maintenance requirements

Flight and Maintenance Planning (FMP) of mission aircraft addresses the question of which available aircraft to fly and for how long, and which grounded aircraft to perform maintenance operations on, in a group of aircraft that comprise a unit. The objective is to achieve maximum fleet availability of the unit over a given planning horizon, while also satisfying certain flight and maintenance requirements. Heuristic approaches that are used in practice to solve the FMP problem often perform poorly, generating solutions that are far from optimum. On the other hand, the more sophisticated mathematical optimization models that have been developed to tackle this problem handle small problems effectively, but tend to be computationally inefficient for larger problems that often arise in practice. In this work, we develop an exact solution algorithm for the FMP Problem, which is capable of identifying the global optimal solution of realistic size problems in very reasonable computational times. Initially, this algorithm obtains a valid upper bound on the optimal objective function value by solving a simplified relaxation of the original problem; then, this value is gradually reduced, until a feasible solution that attains it is identified. The algorithm employs special valid inequalities (cuts), which exclude solutions that do not qualify for optimality from further consideration. The experimental results that we present demonstrate that the proposed solution algorithm is significantly more efficient than a commercial optimization software package that can be used alternatively for the solution of the problem under consideration

Mixed integer biobjective quadratic programming for maximum-value minimum-variability fleet availability of a unit of mission aircraft

We consider the FMP problem encountered in the Hellenic Air Force (HAF), that is, the problem of issuing individual flight and maintenance plans for a group of aircraft comprising a unit, so as to maximize the fleet availability of the unit over a multi-period planning horizon while also satisfying various flight and maintenance related restrictions. The optimization models that have been developed to tackle this problem often perform unsatisfactorily, providing solutions for which the fleet availability exhibits significant variability. In order to handle this difficulty, in this work we develop a mixed integer programming model, which, besides the typical objective maximizing the fleet availability, also includes an additional objective that minimizes its variability. Motivated by the substantial computational difficulties the typical ε-constraint reduced feasible region approach is faced with, as a result of the solution complexity of the optimization models involved, we also develop two specialized solution methodologies for this problem. Both methodologies identify the entire frontier of non-dominated solutions, utilizing suitable relaxations of the original model and exploiting the fact that the domain comprising possible fleet availability values is a discrete set. The first one disaggregates the original FMP model into smaller subproblems whose solution is attained much more efficiently. The second one is a variant of the ε-constraint method, applied to a suitable relaxation of the original FMP model. We present extensive computational results assessing the efficiency of the proposed solution methodologies and demonstrating that their performance is significantly superior to that of the typical ε-constraint method applied directly to the original biobjective model. © 2017 Elsevier Lt