Simheuristics extend metaheuristics by adding a
simulation layer that allows the optimization component to deal
efficiently with scenarios under uncertainty. This presentation
reviews both initial as well as recent applications of simheuristics,
mainly in the area of logistics and transportation. We also discuss
a novel agent-based simheuristic (ABSH) approach that combines simheuristic and multi-agent systems to efficiently solve stochastic combinatorial optimization problems. The presentation is based on papers [1], [2], and [3], which have been already accepted in the prestigious Winter Simulation Conference.Peer ReviewedPostprint (published version

Estrada-Moreno, Alejandro

Freixes, Alfons

Juan Pérez, Ángel Alejandro

Panadero, Javier

Serrat Piè, Carles

English

Simheuristics extend metaheuristics by adding a

simulation layer that allows the optimization component to deal

efficiently with scenarios under uncertainty. This presentation

reviews both initial as well as recent applications of simheuristics,

mainly in the area of logistics and transportation. We also discuss

a novel agent-based simheuristic (ABSH) approach that combines simheuristic and multi-agent systems to efficiently solve stochastic combinatorial optimization problems. The presentation is based on papers [1], [2], and [3], which have been already accepted in the prestigious Winter Simulation Conference.Peer Reviewe

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Current Trends in Simheuristics: from smart transportation to agent-based simheuristics

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XVIII Conferencia de la Asociacio´n Espan˜ola para la Inteligencia Artificial644Current Trends in Simheuristics: from smarttransportation to agent-based simheuristicsAlejandro Estrada-Moreno, Angel A. Juan, Javier Panadero, Carles Serrat, and Alfons FreixesAbstract—Simheuristics extend metaheuristics by adding asimulation layer that allows the optimization component to dealefficiently with scenarios under uncertainty. This presentationreviews both initial as well as recent applications of simheuristics,mainly in the area of logistics and transportation. We also discussa novel agent-based simheuristic (ABSH) approach that combinessimheuristic and multi-agent systems to efficiently solve stochasticcombinatorial optimization problems. The presentation is basedon papers [1], [2], and [3], which have been already accepted inthe prestigious Winter Simulation Conference.Index Terms—simheuristics, stochastic optimization, logistics& transportation, agent-based simheuristics.I. INTRODUCTIONReal-life optimization problems are often NP-hard andlarge-scale in nature, which makes traditional exact methodsinefficient to solve them.Thus, the use of heuristic and meta-heuristic algorithms to obtain high-quality solutions in lowcomputing times is required. With the increasing advances incomputing hardware and software, simulation has become a‘first-resource’ method for analyzing complex systems underuncertainty [4]. Thus, simulation is frequently employed in ar-eas such as logistics and transportation, manufacturing, supplychain management, or smart cities. These systems are modeledand then simulated to get insights on their performance underdifferent base scenarios. Simulation, however, is not an opti-mization tool. Thus, whenever a decision maker aims to findan optimal configuration for a system, she requires the useof optimization methods. Often, the associated optimizationproblems are addressed by assuming deterministic inputs andconstraints, which allows us to simplify them but at the costof not considering the real-life uncertainty that characterizesthese systems.Simheuristic algorithms integrate simulation methods in-side a metaheuristic optimization framework to deal withlarge-scale and NP-hard stochastic optimization problems.Hybridization of simulation techniques with metaheuristicsallows us to consider stochastic variables in the objectivefunction of the optimization problem, as well as probabilisticconstraints in its mathematical formulation. As discussed in[5], the simulation component deals with the uncertainty in themodel and provides feedback to the metaheuristic componentin order to guide the search in a more efficient way. Noticealso that, when dealing with stochastic optimization problems,performance statistics other than expected values must betaken into account: while in deterministic optimization one canfocus on finding a solution that minimizes cost or maximizesprofits, a stochastic version of the problem might requirethat we analyze other statistics such as its variance, differentpercentile values, or its reliability level.The main goals of this presentation are: (i) to provide acommented review of recent applications of simheuristics inthe area of transportation; and (ii) to analyze trends as well asopen research lines.II. RECENT APPLICATIONS IN TRANSPORTATION[6] discuss the need for optimizing urban waste collectionin modern smart cities and formulate the problem as an exten-sion of the vehicle-routing problem. The authors first develop acompetitive metaheuristic, based on a variable neighborhood-search framework, to solve the deterministic variant. Then,they extend their approach into a simheuristic to cope withunexpected waste levels inside the containers. Their algorithmis tested using a large-scaled benchmark set for the waste-collection problem with several realistic constraints. Theirresults include a risk analysis considering the variance of thewaste level and vehicle safety capacities.The uncapacitated facility-location problem with stochasticservice costs is analyzed in [7]. First, the authors propose anextremely fast savings-based heuristic, which generates real-time solutions for the deterministic version of the problem.This can be extremely useful in telecommunication appli-cations, where ‘good’ solutions are needed in just a fewmilliseconds for large-scale networks.[8] propose a simheuristic algorithm for solving the arc-routing problem with stochastic demands. Here, the authorsuse Monte Carlo simulation to extend the RandSHARP heuris-tic, which was originally designed to solve the deterministicversion of the problem. During the design of the routing plan,they make use of safety stocks, which allow vehicles to dealwith unexpectedly high demands during the actual distributionprocess.[9] consider a stochastic version of the capacitated facility-location problem, proposing two facility-location modelsrepresenting alternative distribution policies in e-commerce(outsourcing vs. in-house distribution). The multi-periodinventory-routing problem with stochastic customer demandsis analyzed in [10]. A variable neighborhood search is ex-tended into a simheuristic algorithm to consider variations inthe forecasted demands. With the aim of finding optimal refillpolicies for each customerperiod combination, the authors takeinto account that the quantity serviced at the beginning of oneperiod will affect the inventory levels at the end of that period.XIII Congreso Espan˜ol en Metaheur´ısticas y Algoritmos Evolutivos y Bioinspirados645III. CURRENT TRENDSSome of the following trends in the use of simheuristics canbe identified and are expected to play a relevant role in futurepublications on this topic, therefore constituting open researchlines to be yet fully explored:• A higher level of simulation-optimization integration: adeeper integration between the metaheuristic componentand the simulation component, including increasing useof the feedback provided by the simulation better to guidethe search for better solutions.• Additional objectives: a rising interest in consideringoptimization goals different from the expected value ofa solution for the stochastic optimization problem; thisincludes measuring other statistics reliability/robustnesslevels, and even considering multi-objective optimizationproblems.• Systems of increasing complexity: moving from isolatedlogistics or transportation problems to integrated prob-lems that reflect the complexity of supply networks,where interactions between different echelon stages alsoneed to be considered in order to increase global effi-ciency.• Use of more sophisticated simulation approaches: as thecomplexity of the systems increases, more advanced sim-ulation approaches are required to take into account thedynamic and possibly nonstationary time-evolution of thesystem and the interactions among its many components.• Enhanced identification of promising solutions: to speedup the computations, during a typical simheuristic processonly a reduced set of solutions are classified as ‘promis-ing’ and sent to the simulation component; enhancedstrategies to classify a new solution as a promising onecan be employed.• Statistically significant number of runs: in some of theexamples reviewed in this paper, a 2-stage approachis used; in the first stage the promising solutions aresimulated using a reduced number of runs, while inthe second stage longer simulations are executed on the‘elite’ solutions provided in the first stage to increasethe statistics’ accuracy and precision. However, statisticalconcepts could be employed to set the precise number ofruns required in each stage in order to obtain estimateswith a given level of precision.• Extending the application fields: so far, most simheuris-tics have been applied in the area of transportation,logistics,and production.However, similar stochastic op-timization problems can be found in other applicationfields such as telecommunications, finance, health-caresystems, and smart cities.• Heuristic-supported simulation: while the examples re-viewed here refer to optimization problems in whichsimulation is used to support the search carried out bythe metaheuristic, it is also possible to use heuristicsor metaheuristics to optimize certain system parametersduring a large simulation experiment.• Integration with machine learning: being a flexible andrelatively simple approach, simheuristics can be inte-grated with machine-learning approaches and, in particu-lar, with learnheuristics in order to consider optimizationproblems with dynamic inputs.• Multi-population simheuristics: all the examples reviewedhere are based on single-population metaheuristics; how-ever, integration of simulation within multi-populationmetaheuristics (e.g., genetic algorithms, etc.) might beworth exploring too since different individuals in a popu-lation might be based on different statistics obtained fromthe simulation component.• Agent-based simheuristics: similar to the way agent-basedmodeling and simulation extends the more traditionalconcept of discrete-event simulation and benefits fromdistributed and parallel computing systems, one couldconsider agent-based simheuristics as a multi-agent ex-tension of the simheuristic concept, where each agent isan autonomous and differentiated simheuristic algorithmthat interacts with the rest of the agents while searchingfor a near-optimal solution to a complex and stochasticcombinatorial optimization problem.REFERENCES[1] A. A. Juan, D. W. Kelton, C. Currie, and J. Faulin, “Simheuristicsapplications: dealing with uncertainty in logistics, transportation, andother supply chain areas,” in Proceedings of the 2018 Winter SimulationConference, M. Rabe et al., Ed. Piscataway, New Jersey: IEEE Press,2018.[2] J. Panadero, A. A. Juan, C. Corlu, J. Mozos, and S. Onggo, “Agent-based simheuristics: extending simulation-optimization algorithms viadistributed and parallel computing,” in Proceedings of the 2018 WinterSimulation Conference, M. Rabe et al., Ed. Piscataway, New Jersey:IEEE Press, 2018.[3] A. Estrada-Moreno, C. Serrat, M. Nogal, M. Cavero, and A. A. Juan,“Distribution planning in a weather-dependent scenario with stochastictraveling times: a simheuristic approach,” in Proceedings of the 2018Winter Simulation Conference, M. Rabe et al., Ed. Piscataway, NewJersey: IEEE Press, 2018.[4] T. W. Lucas, W. D. Kelton, P. J. Sanchez, S. M. Sanchez, and B. L.Anderson, “Changing the paradigm: Simulation, now a method of firstresort,” Naval Research Logistics (NRL), vol. 62, no. 4, pp. 293–303,2015.[5] A. A. Juan, J. Faulin, S. E. Grasman, M. Rabe, and G. Figueira,“A review of simheuristics: Extending metaheuristics to deal withstochastic combinatorial optimization problems,” Operations ResearchPerspectives, vol. 2, pp. 62–72, 2015.[6] A. Gruler, C. L. Quintero, L. Calvet, and A. A. Juan, “Waste collectionunder uncertainty: a simheuristic based on variable neighbourhoodsearch,” European Journal of Industrial Engineering, vol. 11, no. 2,pp. 228–255, 2017.[7] J. De Armas, A. A. Juan, J. M. Marque`s, and J. P. Pedroso, “Solvingthe deterministic and stochastic uncapacitated facility location problem:from a heuristic to a simheuristic,” Journal of the Operational ResearchSociety, vol. 68, no. 10, pp. 1161–1176, 2017. [Online]. Available:https://doi.org/10.1057/s41274-016-0155-6[8] S. Gonzalez-Martin, A. A. Juan, D. Riera, M. G. Elizondo, and J. J.Ramos, “A simheuristic algorithm for solving the arc routing problemwith stochastic demands,” Journal of Simulation, vol. 12, no. 1, pp.53–66, 2018.[9] A. Pages-Bernaus, H. Ramalhinho, A. A. Juan, and L. Calvet, “Design-ing e-commerce supply chains: a stochastic facilitylocation approach,”International Transactions in Operational Research, 2017.[10] A. Gruler, J. Panadero, J. DeArmas, J. A. Moreno-Perez, and A. A.Juan, “A variable neighborhood search simheuristic for the multi-period inventory routing problem with stochastic demands,” Interna-tional Transactions in Operational Research, 2018.

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Current Trends in Simheuristics: from smart transportation to agent-based simheuristics

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