Availability simulation model of complex electromechanical systems with the consideration of testability parameters

This paper proposes a stochastic MFBD (maintenance function block diagram) to describe fault diagnosis dynamic behavior of availability fluctuation evaluation for complex electromechanical system, which considers comprehensive diagnostic parameters, maintenance process and resource. The availability evaluation of complex electromechanical systems is achieved by simulation method. Firstly, the faults are divided into several types according to the quantity relationship represented by testability parameters and the logic sequence of fault-related activities is modeled. Math models describing the uncertainty between activities are established, which are embedded within MFBD. The stochastic MFBD is transformed into a simulation model designed via PI (process interaction) algorithm. Finally, a discrete-event simulation example for availability analysis of complex electromechanical system is provided and the accuracy and applicability of the proposed method are verified

A simheuristic algorithm for time-dependent waste collection management with stochastic travel times

A major operational task in city logistics is related to waste collection. Due to large problem sizes and numerous constraints, the optimization of real-life waste collection problems on a daily basis requires the use of metaheuristic solving frameworks to generate near-optimal collection routes in low computation times. This paper presents a simheuristic algorithm for the time-dependent waste collection problem with stochastic travel times. By combining Monte Carlo simulation with a biased randomized iterated local search metaheuristic, time-varying and stochastic travel speeds between different network nodes are accounted for. The algorithm is tested using real instances in a medium-sized city in Spain

Why simheuristics? : Benefits, limitations, and best practices when combining metaheuristics with simulation

Many decision-making processes in our society involve NP-hard optimization problems. The largescale, dynamism, and uncertainty of these problems constrain the potential use of stand-alone optimization methods. The same applies for isolated simulation models, which do not have the potential to find optimal solutions in a combinatorial environment. This paper discusses the utilization of modelling and solving approaches based on the integration of simulation with metaheuristics. These 'simheuristic' algorithms, which constitute a natural extension of both metaheuristics and simulation techniques, should be used as a 'first-resort' method when addressing large-scale and NP-hard optimization problems under uncertainty -which is a frequent case in real-life applications. We outline the benefits and limitations of simheuristic algorithms, provide numerical experiments that validate our arguments, review some recent publications, and outline the best practices to consider during their design and implementation stages

A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems

Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. ‘Simheuristics’ allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (grant TRA2013-48180-C3-P), FEDER, and the Ibero-American Programme for Science and Technology for Development (CYTED2014-515RT0489). Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (Grant 2014-CTP-00001) and the CAN Foundation (Navarre, Spain) (Grant 3CAN2014-3758)