Biased randomization of heuristics using skewed probability distributions: A survey and some applications

Grasas, Alex; Juan, Ángel A.; Faulín, Javier; Armas, Jésica de; Ramalhinho, Helena

doi:10.1016/j.cie.2017.06.019

Cited by 94 publications

(57 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of BR techniques inside a constructive heuristic to obtain new solutions is an appealing aspect of our approach. It means that some bias is introduced into the random process to guide the selection of a movement, in such a way that the basic logic behind the deterministic heuristic is conserved . If higher probabilities of selection are assigned to the most promising movements during the constructive phase of the heuristic, the logic of the original procedure is respected.…”

Section: Solving Methodologymentioning

confidence: 99%

“…It means that some bias is introduced into the random process to guide the selection of a movement, in such a way that the basic logic behind the deterministic heuristic is conserved. 13,30 If higher probabilities of selection are assigned to the most promising movements during the constructive phase of the heuristic, the logic of the original procedure is respected. Indeed, asymmetric probability distributions, for instance, the geometric distribution or the decreasing triangular one, are preferred.…”

Section: Solving Methodologymentioning

confidence: 99%

See 1 more Smart Citation

A 2‐stage biased‐randomized iterated local search for the uncapacitated single allocation p‐hub median problem

Fernandez

Ferone

Juan

et al. 2018

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

The hub location problem has gained attention over the last decades. In telecommunications network, a hub is a place of concurrence in where the work of the network is centralized with the purpose of delivering out the data that arrives from one or more directions to other destinations. There are different versions of the hub location problem depending upon (1) the existence or not of restrictions on the capacity related to the volume of flow a hub is allowed to support, (2) the existence or not of a set‐up cost associated with selecting any node as a hub, etc. In these types of configurations, the hubs serve as connection point between 2 installations, allowing, in this way, to replace a large amount of direct connections between all pair of the nodes, therefore, minimizing the total transportation cost of the network. Thus, this work proposes a 2‐stage metaheuristic based on the combination of biased‐randomized technique with an iterated local search framework for solving the uncapacitated single allocation p‐hub median problem, with computational results that validate the methodology for large‐size instances from the literature.

show abstract

Section: Solving Methodologymentioning

confidence: 99%

Section: Solving Methodologymentioning

confidence: 99%

A 2‐stage biased‐randomized iterated local search for the uncapacitated single allocation p‐hub median problem

Fernandez

Ferone

Juan

et al. 2018

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…(). This technique makes possible the generation of numerous lists without losing the logic behind the priority list based on the computed marginal savings (Grasas et al., ). Then, two policies are iteratively considered to assign customers to depots: the first allows the depot with the most remaining serving capacity to choose the next customer from its priority list, while the second employs a round robin tournament criterion to select which depot chooses next.…”

Section: A Simheuristic For the Smdvrpmentioning

confidence: 99%

“…These lists are sorted according to a distance-based criterion called marginal savings: for a given depot r ∈ V d and a customer i ∈ V c , the marginal saving is calculated as the difference between the distance-based cost of assigning i to the closest depot q (q = r), and the distance-based cost of assigning i to r. Afterward, each list is randomized through a geometric distribution as described in Juan et al (2013a). This technique makes possible the generation of numerous lists without losing the logic behind the priority list based on the computed marginal savings (Grasas et al, 2017). Then, two policies are iteratively considered to assign customers to depots: the first allows the depot with the most remaining serving capacity to choose the next customer from its priority list, while the second employs a round robin tournament criterion to select which depot chooses next.…”

Section: Details and Further Considerationsmentioning

confidence: 99%

Solving the multidepot vehicle routing problem with limited depot capacity and stochastic demands

Calvet

Wang²,

Juan

et al. 2018

Int Trans Operational Res

View full text Add to dashboard Cite

When capacity constraints have to be considered at each depot due to the existence of a limited number of capacitated vehicles, the multidepot vehicle routing problem (MDVRP) is a nontrivial extension of the well‐known capacitated vehicle routing problem (CVRP). The MDVRP combines a customer‐to‐depot assignment problem with several CVRPs. In real‐life scenarios, it is usual to find uncertainty in the customers' demands. There are few works on the stochastic MDVRP (SMDVRP) and, to the best of our knowledge, most of them assume the existence of an unlimited fleet of vehicles at each depot. This paper presents a simheuristic framework combining Monte Carlo simulation with a metaheuristic algorithm to deal with the SMDVRP with limited fleets (and, therefore, limited depot serving capacity). Its efficiency is tested on a set of stochastic instances, which extend the ones available in the literature for the deterministic version of the problem.

show abstract

“…In these cases, the probability values employed to select candidate moves within an s-metaheuristic can be intentionally biased based on a set of priorities defined a priori. Recent work [17] suggests that this could be a potential area of future research for those who employ s-metaheuristics. Although biasing a search is not necessarily a new concept in the broader field of operations research, adjustments to the stochastic selection criteria association with the allocation of forest management activities have likely received little attention.…”

Section: Search Reversion Strategiesmentioning

confidence: 99%

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

Bettinger

Boston

2017

Forests

View full text Add to dashboard Cite

Adaptive forest management requires planning and implementation of activities designed to maintain or improve forest conditions, and in support of these endeavors knowledge of silviculture, economics, operations research, and other allied fields are necessary. With regard to forest planning, traditional (exact) mathematical techniques along with heuristics have been demonstrated as useful in developing alternative courses of action for forest managers to consider. In this discussion paper, we present six areas of future work with regard to investigations into the development of heuristics, along with several recommendations that are based on our experiences. These areas include process improvements, reversion strategies, destruction and reconstruction strategies, intelligent or dynamic parameterization approaches, intelligent termination or transitioning approaches, and seeding strategies. We chose the six areas based on our experiences in developing forest planning heuristics. These areas reflect our opinion of where future research might concentrate. All of these areas of work have the potential to enhance the capabilities and effectiveness of heuristic approaches when applied to adaptive forest management problems.

show abstract

Biased randomization of heuristics using skewed probability distributions: A survey and some applications

Cited by 94 publications

References 64 publications

A 2‐stage biased‐randomized iterated local search for the uncapacitated single allocation p‐hub median problem

A 2‐stage biased‐randomized iterated local search for the uncapacitated single allocation p‐hub median problem

Solving the multidepot vehicle routing problem with limited depot capacity and stochastic demands

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

Contact Info

Product

Resources

About