A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times

González-Neira, Eliana María; Ferone, Daniele; Hatami, Sara; Juan, Ángel A.

doi:10.1016/j.simpat.2017.09.001

Cited by 99 publications

(42 citation statements)

References 81 publications

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“…() discuss the permutation flow‐shop scheduling problem when processing times are stochastic, and propose a simheuristic algorithm and the use of survival analysis concepts to deal with it; Gonzalez‐Neira et al. () introduce a simheuristic for the distributed assembly permutation flow‐shop problem with stochastic processing times; likewise, Hatami et al. () make use of a similar approach to set up starting times in a stochastic version of the parallel flow‐shop problem. Logistics : Juan et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Raba

Estrada‐Moreno

Panadero

et al. 2020

Int Trans Operational Res

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In the context of a supply chain for the animal‐feed industry, this paper focuses on optimizing replenishment strategies for silos in multiple farms. Assuming that a supply chain is essentially a value chain, our work aims at narrowing this chasm and putting analytics into practice by identifying and quantifying improvements on specific stages of an animal‐feed supply chain. Motivated by a real‐life case, the paper analyses a rich multi‐period inventory routing problem with homogeneous fleet, stochastic demands, and maximum route length. After describing the problem and reviewing the related literature, we introduce a reactive heuristic, which is then extended into a biased‐randomized simheuristic. Our reactive approach is validated and tested using a series of adapted instances to explore the gap between the solutions it provides and the ones generated by existing nonreactive approaches.

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Section: Literature Reviewmentioning

confidence: 99%

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Raba

Estrada‐Moreno

Panadero

et al. 2020

Int Trans Operational Res

Self Cite

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“…In the optimization module, we use a multi-start framework to implement biased-randomization techniques within the constructive phase. The efficiency of these techniques combined with classical heuristics has been proved in some studies such as scheduling applications (Gonzalez-Neira et al 2017) as well as vehicle routing problems (Dominguez et al 2016;Martin et al 2016). Figure 2 provides a flowchart overview of our simheuristic algorithm, which encompasses three stages: • In the first step, an initial 'dummy' solution is constructed by generating one round-trip route from the depot to each target zone.…”

Section: Overview Of Our Simheuristic Approachmentioning

confidence: 99%

A Simheuristic for the Unmanned Aerial Vehicle Surveillance-Routing Problem with Stochastic Travel Times and Reliability Considerations

Panadero

Juan

Freixes

et al. 2019

2019 Winter Simulation Conference (WSC)

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In the unmanned aerial vehicle (UAV) surveillance-routing problem, a limited fleet of UAVs with drivingrange limitations have to visit a series of target zones in order to complete a monitoring operation. This operation typically involves taking images and / or registering some key performance indicators. Whenever this surveillance action is repetitive, a natural goal to achieve is to complete each cycle of visits as fast as possible, so that the number of times each target zone is visited during a time interval is maximized. Since many factors might influence travel times, they are modeled as random variables. Reliability issues are also considered, since random travel times might prevent a route from being successfully completed before the UAV runs out of battery. In order to solve this stochastic optimization problem, a simheuristic algorithm is proposed. Computational experiments contribute to illustrate these concepts and to test the quality of our approach.

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“…In summary, as pointed out by [33], the cardinality and quantity constraints make large-size instances of the problem to be computationally intractable using traditional optimization approaches. Because our research involves the CEF, we devise a matheuristic algorithm for rich portfolio optimization (ARPO) that is based on the combination of an iterated local search (ILS) metaheuristic [34,35], quadratic programming, and biased randomization strategies [36][37][38]. The contribution of this study is fourfold.…”

Section: Introductionmentioning

confidence: 99%

A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization

et al. 2019

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This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints. ARPO proceeds in two steps. First, a feasible initial solution is constructed by allocating portfolio weights according to the individual return rate. Secondly, an iterated local search framework, which makes use of quadratic programming, gradually improves the initial solution throughout an iterative combination of a perturbation stage and a local search stage. According to the experimental results obtained, ARPO is very competitive when compared against existing state-of-the-art approaches, both in terms of the quality of the best solution generated as well as in terms of the computational times required to obtain it. Furthermore, we also show that our algorithm can be used to solve variants of the portfolio optimization problem, in which inputs (individual asset returns, variances and covariances) feature a random component. Notably, the results are similar to the benchmark constrained efficient frontier with deterministic inputs, if variances and covariances of individual asset returns comprise a random component. Finally, a sensitivity analysis has been carried out to test the stability of our algorithm against small variations in the input data.

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A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times

Cited by 99 publications

References 81 publications

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

A Simheuristic for the Unmanned Aerial Vehicle Surveillance-Routing Problem with Stochastic Travel Times and Reliability Considerations

A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization

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