Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign

Cross-selling campaigns seek to offer the right products to the set of customers with the goal of maximizing expected profit, while, at the same time, respecting the purchasing constraints set by investors. In this context, a bi-objective version of this NP-Hard problem is approached in this paper, aiming at maximizing both the promotion campaign total profit and the risk-adjusted return, which is estimated with the reward-to-variability ratio known as Sharpe ratio. Given the combinatorial nature of the problem and the large volume of data, heuristic methods are the most common used techniques. A Greedy Randomized Neighborhood Structure is also designed, including the characteristics of a neighborhood exploration strategy together with a Greedy Randomized Constructive technique, which is embedded in a multi-objective local search metaheuristic. The latter combines the power of neighborhood exploration by using a Pareto Local Search with Variable Neighborhood Search. Sets of non-dominated solutions obtained by the proposed method are described and analyzed for a number of problem instances

A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem

Published: 20 September 2020In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method haswon first and second places, respectively, thus proving its ability to find high-quality solutions consistently.Jonatas B. C. Chagas, Julian Blank, Markus Wagner, Marcone J. F. Souza, Kalyanmoy De

Potential contribution of selected metallic restorative dentistry materials to X-ray fluorescence

Recent advances have led to the use of new materials in dental restoration which is an area of rapid growth. Applications include improving oral aesthetics and essential rehabilitation, whilst procedures range from the recovery of partial elements (inlays) to fitting dental implants. Ceramics, polymers and metallic materials have all been successfully employed in dental applications and benefit from new cost efficient manufacturing techniques. The application of radiographic techniques in dentistry and other medicine is also increasing, and the combination of new materials and radiation can lead to an elevated health risk. X-rays can interact with metallic materials producing X-ray fluorescence, which can increase the radiation dose in proximity to restorative material and increase the risk of live biological tissue becoming cancerous. The issue demands consideration so that the biological risks associated with such procedures are kept as low as possible. Comparisons of doses calculated for several materials have provided evidence that the Ti cp and NiCrTi alloys present less contribution to the increase of dose in surrounding soft tissue and the potential deleterious biological effects. On the other hand, Amalgam appears to be the most deleterious alloy

A hybrid heuristic algorithm for the open-pit-mining operational planning problem

This paper deals with the Open-Pit-Mining Operational Planning problem with dynamic truck allocation. The objective is to optimize mineral extraction in the mines by minimizing the number of mining trucks used to meet production goals and quality requirements. According to the literature, this problem is NP-hard, so a heuristic strategy is justified. We present a hybrid algorithm that combines characteristics of two metaheuristics: Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search. The proposed algorithm was tested using a set of real-data problems and the results were validated by running the CPLEX optimizer with the same data. This solver used a mixed integer programming model also developed in this work. The computational experiments show that the proposed algorithm is very competitive, finding near optimal solutions (with a gap of less than 1%) in most instances, demanding short computing times.Open-pit-mining Metaheuristics GRASP Variable neighborhood search Mathematical programming