TOOLS USED IN DECISION MAKING

Decision making is one of the important tasks of every manager. The process of taking decisions has to be based on knowledge. For optimizing this process some software solutions has been created. In this article we tried to summarize some of the features which exists in some software applications.decision making, risk solver

Horizontal collaboration in freight transport: concepts, benefits and environmental challenges

[EN] Since its appearance in the 1990s, horizontal collaboration (HC) practices have revealed themselves as catalyzers for optimizing the distribution of goods in freight transport logistics. After introducing the main concepts related to HC, this paper offers a literature review on the topic and provides a classification of best practices in HC. Then, the paper analyses the main benefits and optimization challenges associated with the use of HC at the strategic, tactical, and operational levels. Emerging trends such as the concept of ` green' or environmentally- friendly HC in freight transport logistics are also introduced. Finally, the paper discusses the need of using hybrid optimization methods, such as simheuristics and learnheuristics, in solving some of the previously identified challenges in real- life scenarios dominated by uncertainty and dynamic conditions.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness & FEDER (TRA2013-48180-C3-P, TRA2015-71883-REDT), The Erasmus+ program (2016-1-ES01-KA108-023465), the Ibero American Program of Science and Technology for Development (CYTED2014-515RT-0489), the CAN Foundation (CAN2014-3758, CAN2015-70473), and the Spanish Ministry of Education (FPU-14/00024).Serrano-Hernandez, A.; Juan, AA.; Faulin, J.; Perez-Bernabeu, E. (2017). Horizontal collaboration in freight transport: concepts, benefits and environmental challenges. SORT. Statistics and Operations Research Transactions. 41(2):393-414. https://doi.org/10.2436/20.8080.02.65S39341441

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision-makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best-known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/01100011033), as well as by the Barcelona Council and the “laCaixa” Foundation under the framework of the Barcelona Science Plan 2020-2023 (grant21S09355-01) and Generalitat Valenciana (PROMETEO/2021/065).Peer ReviewedPostprint (published version

Determining reliable solutions for the team orienteering problem with probabilistic delays

In the team orienteering problem, a fixed fleet of vehicles departs from an origin depot towards a destination, and each vehicle has to visit nodes along its route in order to collect rewards. Typically, the maximum distance that each vehicle can cover is limited. Alternatively, there is a threshold for the maximum time a vehicle can employ before reaching its destination. Due to this driving range constraint, not all potential nodes offering rewards can be visited. Hence, the typical goal is to maximize the total reward collected without exceeding the vehicle’s capacity. The TOP can be used to model operations related to fleets of unmanned aerial vehicles, road electric vehicles with limited driving range, or ride-sharing operations in which the vehicle has to reach its destination on or before a certain deadline. However, in some realistic scenarios, travel times are better modeled as random variables, which introduce additional challenges into the problem. This paper analyzes a stochastic version of the team orienteering problem in which random delays are considered. Being a stochastic environment, we are interested in generating solutions with a high expected reward that, at the same time, are highly reliable (i.e., offer a high probability of not suffering any route delay larger than a user-defined threshold). In order to tackle this stochastic optimization problem, which contains a probabilistic constraint on the random delays, we propose an extended simheuristic algorithm that also employs concepts from reliability analysis.This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033), the Barcelona City Council and Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020–2023 (grant 21S09355-001), and the Generalitat Valenciana (PROMETEO/2021/065).Peer ReviewedPostprint (published version

Combining survival analysis and simheuristics to predict the risk of delays in urban ridesharing operations with random travel times

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMore sustainable transportation and mobility concepts, such as ridesharing, are gaining momentum in modern smart cities. In many real-life scenarios, travel times among potential customers' locations should be modeled as random variables. This uncertainty makes it difficult to design efficient ridesharing schedules and routing plans, since the risk of possible delays has to be considered as well. In this paper, we model ridesharing as a stochastic team orienteering problem in which the trade-off between maximizing the expected reward and the risk of incurring time delays is analyzed. In order to do so, we propose a simulation-optimization approach that combines a simheuristic algorithm with survival analysis techniques. The aforementioned methodology allows us to generate not only the probability that a given routing plan will suffer a delay, but also gives us the probability that the routing plan experiences delays of different sizes.Peer ReviewedPostprint (author's final draft

The polygenic basis of relapse after a first episode of schizophrenia

Little is known about genetic predisposition to relapse. Previous studies have linked cognitive and psychopathological (mainly schizophrenia and bipolar disorder) polygenic risk scores (PRS) with clinical manifestations of the disease. This study aims to explore the potential role of PRS from major mental disorders and cognition on schizophrenia relapse. 114 patients recruited in the 2EPs Project were included (56 patients who had not experienced relapse after 3 years of enrollment and 58 patients who relapsed during the 3-year follow-up). PRS for schizophrenia (PRS-SZ), bipolar disorder (PRS-BD), education attainment (PRS-EA) and cognitive performance (PRS-CP) were used to assess the genetic risk of schizophrenia relapse.Patients with higher PRS-EA, showed both a lower risk (OR=0.29, 95% CI [0.11–0.73]) and a later onset of relapse (30.96± 1.74 vs. 23.12± 1.14 months, p=0.007. Our study provides evidence that the genetic burden of neurocognitive function is a potentially predictors of relapse that could be incorporated into future risk prediction models. Moreover, appropriate treatments for cognitive symptoms appear to be important for improving the long-term clinical outcome of relapse

TOOLS USED IN DECISION MAKING

Decision making is one of the important tasks of every manager. The process of taking decisions has to be based on knowledge. For optimizing this process some software solutions has been created. In this article we tried to summarize some of the features which exists in some software applications

Machining Chatter Prediction Using a Data Learning Model

Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit a self-excited vibration. In this paper, an artificial neural network (ANN)—a data learning model—is applied to model turning stability. The novel approach is to use a physics-based process model—the analytical stability limit—to generate a (synthetic) data set that trains the ANN. This enables the process physics to be combined with data learning in a hybrid approach. As anticipated, it is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training