A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Khouadjia, Mostepha; Sarasola, Briseida; Alba, Enrique; Jourdan, Laëtitia; Talbi, El‐Ghazali

doi:10.1016/j.asoc.2011.10.023

Cited by 117 publications

(74 citation statements)

References 24 publications

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“…A similar ACO approach was proposed in [122] and a PSO approach in [135]. More PSO variations using time slices were proposed by Khouadjia et al [128,129]. Each particle keeps track of its best solution which is later used to form the initial swarm for the next time slice.…”

Section: Increasing Diversity After a Changementioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

475

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Increasing Diversity After a Changementioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

475

213

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“…There are six columns in Tables 1 and 2: the instance, our MBO algorithm, the best known solutions (BKS), the algorithm to find the BKS, the relative error (RE) (i.e., RE = (MBO − BKS)/BKS × 100%) and the computational time by MBO (note that we cannot compare with other algorithms as there is no such data available in the public domain or in the literature). There are other best and average results in Tables 1 and 2 provided by some heuristic algorithms as follows: GA2007 and TS by Hanshar and Ombuki-Berman [29], DAPSO and VNS by [16], GA2014 by [21], HLNS by [17] and AAC-2OPT by [22]. As you can see, our approach is very competitive.…”

Section: Comparison With the Literature For Dvrpmentioning

confidence: 96%

“…Other optimization techniques have been applied. Khouadjia et al [16] presented particle swarm optimization. Yang et al [17] proposed a hybrid large neighborhood search.…”

Section: Related Workmentioning

confidence: 99%

A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem

Chen

Gao

2017

Algorithms

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Abstract:The dynamic vehicle routing problem (DVRP) is a variant of the Vehicle Routing Problem (VRP) in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a monarch butterfly optimization (MBO) algorithm to solve DVRPs, utilizing a greedy strategy. Both migration operation and the butterfly adjusting operator only accept the offspring of butterfly individuals that have better fitness than their parents. To improve performance, a later perturbation procedure is implemented, to maintain a balance between global diversification and local intensification. The computational results indicate that the proposed technique outperforms the existing approaches in the literature for average performance by at least 9.38%. In addition, 12 new best solutions were found. This shows that this proposed technique consistently produces high-quality solutions and outperforms other published heuristics for the DVRP.

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“…Dynamic routing, on the day of operation, may also be envisaged for this type of problem, although not considered here, with the remote monitoring data being used to dynamically reschedule collections during the day based on updated information received. Dynamic routing is more typically used in courier services, ambulance and dial-a-ride applications (21)(22)(23).…”

Section: Related Workmentioning

confidence: 99%

Dynamic Collection Scheduling Using Remote Asset Monitoring

McLeod

Erdoğan

Cherrett

et al. 2013

Transportation Research Record

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In the waste collection sector, remote sensing technology is now coming onto the market, allowing waste and recycling receptacles to report their fill levels at regular intervals. This enables collection schedules to be dynamically optimised to better meet true servicing needs, so reducing transport costs and ensuring that visits to clients are made in a timely fashion. This paper describes a real-life logistics problem faced by a leading UK charity in servicing its textile and book donation banks and its High Street stores using a common fleet of vehicles with varying carrying capacities. This gives rise to a vehicle routing problem whereby visits to stores are on fixed days of the week, with time window constraints, and visits to banks (fitted with remote fill monitoring technology) are made in a timely fashion to avoid them becoming full before collection. A tabu search algorithm was developed to provide vehicles routes for the next day of operation, based on maximising profit. A longer look-ahead period was not considered on the basis that donation rates to banks are highly variable. The algorithm included parameters specifying the minimum fill level (e.g. 50%) required to allow a visit to a bank and a penalty function used to encourage visits to banks that are becoming full. The results showed that the algorithm significantly reduced visits to banks and increased profit by up to 2.4% with best performance obtained the more variable the donation rates.

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A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Cited by 117 publications

References 24 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem

Dynamic Collection Scheduling Using Remote Asset Monitoring

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