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de la Cruz, R., Padilla, O., Valle, M. A., & Ruz, G. A. (2021). Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks. Mathematics, 9(6), 639.
Abstract: This study aims to analyze and explore criminal recidivism with different modeling strategies: one based on an explanation of the phenomenon and another based on a prediction task. We compared three common statistical approaches for modeling recidivism: the logistic regression model, the Cox regression model, and the cure rate model. The parameters of these models were estimated from a Bayesian point of view. Additionally, for prediction purposes, we compared the Cox proportional model, a random survival forest, and a deep neural network. To conduct this study, we used a real dataset that corresponds to a cohort of individuals which consisted of men convicted of sexual crimes against women in 1973 in England and Wales. The results show that the logistic regression model tends to give more precise estimations of the probabilities of recidivism both globally and with the subgroups considered, but at the expense of running a model for each moment of the time that is of interest. The cure rate model with a relatively simple distribution, such as Weibull, provides acceptable estimations, and these tend to be better with longer follow-up periods. The Cox regression model can provide the most biased estimations with certain subgroups. The prediction results show the deep neural network's superiority compared to the Cox proportional model and the random survival forest.
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Lespay, H., & Suchan, K. (2021). A case study of consistent vehicle routing problem with time windows. Int. Trans. Oper. Res., 28, 1135–1163.
Abstract: We develop a heuristic for the consistent vehicle routing problem with time windows (ConVRPTW), which is motivated by a real-world application at a food company's distribution center. Besides standard VRPTW restrictions, ConVRPTW assigns each customer just one driver to fulfill his or her orders during the whole multiperiod planning horizon. For each driver and period, a route is sought to serve all their customers with positive demand. For each customer, the number of periods between consecutive orders and the ordered quantities is highly irregular. This causes difficulties in the daily routing, negatively impacting the service level of the company. Similar problems have been studied as ConVRP, where the number of drivers is fixeda priori, and only the total travel time is minimized. Moreover, the clients present no time window constraints, but the visits should be scheduled with a small arrival time variation. In our model, the objective is to minimize the number of drivers. We impose hard time windows but do not consider time consistency in more detail. We compare solutions given by the heuristic with solutions of a mixed-integer linear programming model on a set of small artificial instances and solutions used by the food company on real-world instances. The results show the effectiveness of the heuristic. For the company, we obtain significant improvements in the routing plans, with a lower number of vehicles and a higher rate of orders delivered within the prescribed time window.
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Lespay, H., & Suchan, K. (2022). Territory Design for the Multi-Period Vehicle Routing Problem with Time Windows. Comput. Oper. Res., 145, 105866.
Abstract: This study introduces the Territory Design for the Multi-Period Vehicle Routing Problem with Time Windows (TD-MPVRPTW) problem, motivated by a real-world application at a food company's distribution center. This problem deals with the design of contiguous and compact territories for delivery of orders from a depot to a set of customers, with time windows, over a multi-period planning horizon. Customers and their demands vary over time. The problem is modeled as a mixed-integer linear program (MILP) and solved by a proposed heuristic. The heuristic solutions are compared with the proposed MILP solutions on a set of small artificial instances and the food company's solutions on a set of real-world instances. Computational results show that the proposed algorithm can yield high-quality solutions within moderate running times. A methodology is proposed in which the territories computed by the proposed heuristic on the past demand of one month are used for the operational routing during the following month, in which the demand is known only one day in advance. An evaluation shows that the territories obtained with our methodology would have led to levels of service significantly better than the ones that were experienced by the company, using a significantly lower number of vehicles to execute the deliveries.
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Munoz-Herrera, S., & Suchan, K. (2022). Constrained Fitness Landscape Analysis of Capacitated Vehicle Routing Problems. Entropy, 24(1), 53.
Abstract: Vehicle Routing Problems (VRP) comprise many variants obtained by adding to the original problem constraints representing diverse system characteristics. Different variants are widely studied in the literature; however, the impact that these constraints have on the structure of the search space associated with the problem is unknown, and so is their influence on the performance of search algorithms used to solve it. This article explores how assignation constraints (such as a limited vehicle capacity) impact VRP by disturbing the network structure defined by the solution space and the local operators in use. This research focuses on Fitness Landscape Analysis for the multiple Traveling Salesman Problem (m-TSP) and Capacitated VRP (CVRP). We propose a new Fitness Landscape Analysis measure that provides valuable information to characterize the fitness landscape's structure under specific scenarios and obtain several relationships between the fitness landscape's structure and the algorithmic performance.
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Nafees, N., Ahmed, S., Kakkar, V., Bahar, A. N., Wahid, K. A., & Otsuki, A. (2022). QCA-Based PIPO and SIPO Shift Registers Using Cost-Optimized and Energy-Efficient D Flip Flop. Electronics, 11(19), 3237.
Abstract: With the growing use of quantum-dot cellular automata (QCA) nanotechnology, digital circuits designed at the Nanoscale have a number of advantages over CMOS devices, including the lower utilization of power, increased processing speed of the circuit, and higher density. There are several flip flop designs proposed in the literature with their realization in the QCA technology. However, the majority of these designs suffer from large cell counts, large area utilization, and latency, which leads to the high cost of the circuits. To address this, this work performed a literature survey of the D flip flop (DFF) designs and complex sequential circuits that can be designed from it. A new design of D flip flop was proposed in this work and to assess the performance of the proposed QCA design, an in-depth comparison with existing designs was performed. Further, sequential circuits such as parallel-in-parallel-out (PIPO) and serial-in-parallel-out (SIPO) shift registers were designed using the flip flop design that was put forward. A comprehensive evaluation of the energy dissipation of all presented fundamental flip-flop circuits and other sequential circuits was also performed using the QCAPro tool, and their energy dissipation maps were also obtained. The suggested designs showed lower power dissipation and were cost-efficient, making them suitable for designing higher-power circuits.
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Tiwari, A. K., Suozzi, E., Silva, C., De Maio, M., & Zanetti, M. (2021). Role of Integrated Approaches in Water Resources Management: Antofagasta Region, Chile. Sustainability, 13(3), 1297.
Abstract: Water is essential for the survival of all living beings and plays a significant role in the growth of any country ' s economy. At present, water depletion and pollution are a serious challenge due to anthropogenic, geogenic and climate change activities worldwide, including in Chile. The Antofagasta region is located in northern Chile and is the heart of its mining industry, playing a significant role in the country ' s economy. The Antofagasta region ' s main challenge is water shortage and contamination. Due to it, the region ' s local population is facing major difficulties in obtaining the necessary water for domestic, industrial, irrigation, and other uses. Therefore, a water resources management plan is essential for the region to maintain a sustainable environment. Considering the above points, significant parameters, such as slope, aspect, elevation, hillshade, drainage, drainage density and river basin-maps of the Antofagasta region prepared using the digital elevation model (DEM) data in geographic information system (GIS) environment. Besides, a pollution risk level assessment of the study area ' s cities/villages done using GIS application. The important created maps and the identification of pollution risk of cities/villages of the present study could provide significant information to policymakers and help them make a suitable water management plan for the area.
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