A hybrid algorithm combining heuristics with Monte Carlo simulation for solving the Stochastic Flow Shop Problem

In this paper a hybrid simulation-based algorithm is proposed for the Stochastic Flow Shop Problem. The main idea of the methodology is to transform the stochastic problem into a deterministic problem and then apply simulation. To achieve this goal we use Monte Carlo simulation and a modified version of the well-known NEH heuristic. This approach aims to provide flexibility and simplicity due to the fact that it is not constrained by any previous assumption and relies in well-tested heuristics.Postprint (published version

Using specification and description language to represent users’ profiles in OMNET++ simulations

Omnet++ is a powerful and open-source simulation tool which is basically intended to model discrete-event systems. In particular, Omnet++ is extensively used to model and simulate computer networks. Typically, when a Wide Area Network needs to be modeled, different assumptions are made in order to simplify the complexity associated with human behavior. Nevertheless, human behavior can also be modeled, at least to some extent, by using Multi Agent Systems (MAS). This paper presents a methodology that allows connecting a MAS model –which accounts for human behavior–, with a standard Omnet++ model –which represents the behavior of a computer network. The approach presented here can be useful to obtain a better representation of the human behavior through a MAS model when using Omnet++. Furthermore, our approach simplifies the modeling process by splitting the complexity of a real system into two different parts. Therefore, on the one hand computer scientists can focus on the Omnet++ model while, on the other hand, specialists in human behavior can focus on the MAS model. Finally, our approach also facilitates the distribution of the models among different computers.Postprint (published version

A Parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of casesPeer ReviewedPreprin

A simulation-based approach for solving the flowshop problem

A simulation-based algorithm for the Permutation Flowshop Sequencing Problem (PFSP) is presented. The algorithm uses Monte Carlo Simulation and a discrete version of the triangular distribution to incorporate a randomness criterion in the classical Nawaz, Enscore, and Ham (NEH) heuristic and starts an iterative process in order to obtain a set of alternative solutions to the PFSP. Thus, a random but biased lo We can then consider several properties per solution other than the makespan, such as balanced idle times among machines, number of completed jobs at a given target time, etc. This allows the decision-maker to consider multiple solution characteristics apart from those defined by the aprioristic objective function. Therefore, our methodology provides flexibility during the sequence selection process, which may help to improve the scheduling process. Several tests have been performed to discuss the effectiveness of this approach. The results obtained so far are promising enough to encourage further developments and improvements on the algorithm and its applications in real-life scenarios. In particular, Multi-Agent Simulation is proposed as a promising technique to be explored in future works.Postprint (published version

Applications of discrete-event simulation to reliability and availability assesment in civil engineering structures

This paper discusses the convenience of predicting, quantitatively, time-dependent reliability and availability levels asso-ciated with most building or civil engineering structures. Then, the paper reviews different approaches to these problems and proposes the use of discrete-event simulation as the most realistic way to deal with them, specially during the design stage. The paper also reviews previous work on the use of both Monte Carlo simulation and discrete-event simulation in this area and shows how discrete-event simulation, in particular, could be employed to solve uncertainty in time-dependent structural reliability problems. Finally, a case study is developed to illustrate some of the concepts previously covered in the paper.Postprint (published version

Using simulation to provide alternative solutions to the flowshop sequencing problem

In this paper we present SS-GNEH, a simulation-based algorithm for the Permutation Flowshop Sequencing Problem (PFSP). Given a PFSP instance, the SSGNEH algorithm incorporates a randomness criterion to the classical NEH heuristic and starts an iterative process in order to obtain a set of alternative solutions, each of which outperforms the NEH algorithm. Thus, a random but oriented local search of the space of solutions is performed, and a list of "good alternative solutions" is obtained. We can then consider several desired properties per solution other than maximum time employed, such as balanced idle times among machines, number of completed jobs at a given target time, etc. This allows the decision-maker to consider multiple solution characteristics other than just those defined by the aprioristic objective function. Therefore, our methodology provides flexibility during the sequence selection process, which may help to improve the scheduling process. Several tests have been performed to discuss the effectiveness of this approach. The results obtained so far are promising enough to encourage further developments on the algorithm and its applications in real-life scenariosPostprint (published version

A hybrid algorithm combining path scanning and biased random sampling for the Arc Routing Problem

The Arc Routing Problem is a kind of NP-hard routing problems where the demand is located in some of the arcs connecting nodes and should be completely served fulfilling certain constraints. This paper presents a hybrid algorithm which combines a classical heuristic with biased random sampling, to solve the Capacitated Arc Routing Problem (CARP). This new algorithm is compared with the classical Path scanning heuristic, reaching results which outperform it. As discussed in the paper, the methodology presented is flexible, can be easily parallelised and it does not require any complex fine-tuning process. Some preliminary tests show the potential of the proposed approach as well as its limitationsPostprint (published version

A simulation-based algorithm for solving the Vehicle Routing Problem with Stochastic Demands

This paper proposes a flexible solution methodology for solving the Vehicle Routing Problem with Stochastic Demands (VRPSD). The logic behind this methodology is to transform the issue of solving a given VRPSD instance into an issue of solving a small set of Capacitated Vehicle Routing Problem (CVRP) instances. Thus, our approach takes advantage of the fact that extremely efficient metaheuristics for the CVRP already exists. The CVRP instances are obtained from the original VRPSD instance by assigning different values to the level of safety stocks that routed vehicles must employ to deal with unexpected demands. The methodology also makes use of Monte Carlo Simulation (MCS) to obtain estimates of the expected costs associated with corrective routing actions (recourse actions) after a vehicle runs out of load before completing its route.Postprint (published version

Combinando randomización sesgada y búsqueda local iterativa para resolver problemas de flow-shop

A la hora de poder aplicar algoritmos te oricos a casos reales, no solo resulta conveniente que el algoritmo sea e ciente sino tambi en que sea lo m as comprensible posible y que no requiera de comple- jos procesos de parametrizaci on. Siguiendo esta l ogi- ca, proponemos aqu un algoritmo h brido que reune las caracter sticas anteriores para resolver el proble- ma del Flow-Shop (FSP). El algoritmo, que no re- quire de parametrizaci on alguna, combina estrategias de randomizaci on con una B usqueda Local Iterativa (ILS), logrando ser competitivo con otros conocidos algoritmos que se encuentran entre los m as simples y e cientes para el FSP. Nuestro enfoque de ne (1) un nuevo operador para el proceso de perturbaci on ILS, (2) un nuevo criterio de aceptaci on basado en reglas simples y transparentes, y (3) un proceso de random- izaci on sesgada de la soluci on inicial. Los resultados preliminares obtenidos con las instancias de Taillard permiten concluir que la soluci on propuesta puede ser una excelente alternativa en aplicaciones realesPeer ReviewedPostprint (published version

A two-gene epigenetic signature for the prediction of response to neoadjuvant chemotherapy in triple-negative breast cancer patients

Background: pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) varies between 30 and 40% approximately. To provide further insight into the prediction of pCR, we evaluated the role of an epigenetic methylation-based signature. Methods: epigenetic assessment of DNA extracted from biopsy archived samples previous to NAC from TNBC patients was performed. Patients included were categorized according to previous response to NAC in responder (pCR or residual cancer burden, RCB = 0) or non-responder (non-pCR or RCB > 0) patients. A methyloma study was performed in a discovery cohort by the Infinium HumanMethylation450 BeadChip (450K array) from Illumina. The epigenetic silencing of those methylated genes in the discovery cohort were validated by bisulfite pyrosequencing (PyroMark Q96 System version 2.0.6, Qiagen) and qRT-PCR in an independent cohort of TN patients and in TN cell lines. Results: twenty-four and 30 patients were included in the discovery and validation cohorts, respectively. In the discovery cohort, nine genes were differentially methylated: six presented higher methylation in non-responder patients (LOC641519, LEF1, HOXA5, EVC2, TLX3, CDKL2) and three greater methylation in responder patients (FERD3L, CHL1, and TRIP10). After validation, a two-gene (FER3L and TRIP10) epigenetic score predicted RCB = 0 with an area under the ROC curve (AUC) = 0.905 (95% CI = 0.805-1.000). Patients with a positive epigenetic two-gene score showed 78.6% RCB = 0 versus only 10.7% RCB = 0 if signature were negative. Conclusions: these results suggest that pCR in TNBC could be accurately predicted with an epigenetic signature of FERD3L and TRIP10 genes. Further prospective validation of these findings is warranted