Enhancing and extending the classical GRASP framework with biased randomisation and simulation

Ferone, Daniele; Gruler, Aljoscha; Festa, Paola

doi:10.1080/01605682.2018.1494527

Cited by 56 publications

(37 citation statements)

References 56 publications

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“…() and Ferone et al. () extend two popular metaheuristic frameworks into simheuristic ones, so they can also deal with stochastic optimization problems in a natural way.…”

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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“…() and Ferone et al. () extend two popular metaheuristic frameworks into simheuristic ones, so they can also deal with stochastic optimization problems in a natural way.…”

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

View full text Add to dashboard Cite

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“…Hence, multiple executions of a BRA-either completed in a sequential or in a parallel mode-will yield a set of alternative solutions, all of them based on the logic behind the heuristic. Since we are executing many biased-random variations of the constructive procedure defined by the heuristic, chances are that some of these "near-greedy" heuristics lead to solutions that outperform the one generated by the greedy heuristic [10]. Algorithm 1 shows a pseudo-code description of a basic BRA that performs in a sequential way.…”

Section: Basic Concepts On Biased-randomized Algorithmsmentioning

confidence: 99%

“…Notice that, by using this approach, a broad exploration of the solution space is carried out, which might be specially beneficial in the case of highly irregular objective functions as the ones characterizing non-smooth OPs. The proposed methodology can be seen as a natural extension of the basic greedy randomized adaptive search procedure (GRASP) [44], as analyzed in Ferone et al [10]. Instead of employing empirical probability distributions-which require time-consuming parameter fine tuning and thus might slow down computations-a theoretical probability distribution such as the geometric distribution or the decreasing triangular distribution can be used.…”

Section: Basic Concepts On Biased-randomized Algorithmsmentioning

confidence: 99%

“…This paper reviews different examples of OPs with non-smooth objective functions and then analyzes how biased-randomized algorithms (BRAs) can constitute an effective methodology to generate reasonably good solutions in very short computing times. As described in Ferone et al [10], BRAs make use of skewed probability distributions to integrate a "biased" (nonuniform) random behavior into a heuristic. This allows one to quickly generate a large set of alternative good solutions by simply changing the seed of the pseudo-random number generator [11,12].…”

Section: Introductionmentioning

confidence: 99%

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On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems

et al. 2019

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Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines.

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“…Our solving approach relies on a simheuristic algorithm, which combines heuristic or metaheuristic algorithms with simulation (in any of its forms). These hybridization has been used to extend metaheuristic frameworks such as the greedy randomized adaptive search procedure (Ferone et al 2018) or the iterated local search (Grasas et al 2016). As any other simheuristic algorithm, the developed model in this study is composed of two different components: an optimization module which searches for promising solutions and a simulation model which assesses the promising solutions in a stochastic environment and might also guide the search process.…”

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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Enhancing and extending the classical GRASP framework with biased randomisation and simulation

Cited by 56 publications

References 56 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

On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems

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

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