A Sequential Route-Building Algorithm Employing a Generalised Savings Criterion

Mole, R. H.; Jameson, S. R.

doi:10.2307/3008819

Cited by 42 publications

(51 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, X ⊕ SS = (1, 3, 5, 2, 4) ⊕ SO(2, 3) ⊕ SO(4, 5) = (1, 5, 3, 2, 4) ⊕ SO(4, 5) = (1,5,3,4,2). This definition helps to make a swap sequence or velocity from Equation (11) and then swap sequence will apply on a particle according to Equation (10) to move the particle from one position to other.…”

Section: Pso For Cvrpmentioning

confidence: 99%

“…Hence, to apply PSO in discrete domain, we adopt the swap sequence-based PSO [54]. We need to map the meaning of Equations (10) and (11) so that PSO can deal a discrete type problem. In this purpose [54], authors proposed a new definition to the characteristics of two symbols, ⊕ and →.…”

Section: Pso For Cvrpmentioning

confidence: 99%

“…These influences are basically the strength of PSO algorithm which leads a particle to a better position. Thus, we have utilized the proposal of Shi and Eberhart [53] instead of using the straightforward PSO velocity update equation from Eberhart and Kennedy [40] to try to keep the preeminent balance among the influential portions in Equation (11).…”

Section: Pso For Cvrpmentioning

confidence: 99%

“…This technique is basically a constructive heuristic which builds tours gradually by inserting a node at each time with respect to some saving conditions till no more cluster can be constructed. There are many improvements and variations available in the literature from many researchers [10][11][12]. "Cluster-First Route-Second" [13,14] and "Route-First Cluster-Second" [15] are other two well-known algorithms for handling CVRP.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bilayer Local Search Enhanced Particle Swarm Optimization for the Capacitated Vehicle Routing Problem

Ahmed

Sun

2018

Algorithms

View full text Add to dashboard Cite

Abstract:The classical capacitated vehicle routing problem (CVRP) is a very popular combinatorial optimization problem in the field of logistics and supply chain management. Although CVRP has drawn interests of many researchers, no standard way has been established yet to obtain best known solutions for all the different problem sets. We propose an efficient algorithm Bilayer Local Search-based Particle Swarm Optimization (BLS-PSO) along with a novel decoding method to solve CVRP. Decoding method is important to relate the encoded particle position to a feasible CVRP solution. In bilayer local search, one layer of local search is for the whole population in any iteration whereas another one is applied only on the pool of the best particles generated in different generations. Such searching strategies help the BLS-PSO to perform better than the existing proposals by obtaining best known solutions for most of the existing benchmark problems within very reasonable computational time. Computational results also show that the performance achieved by the proposed algorithm outperforms other PSO-based approaches.

show abstract

Section: Pso For Cvrpmentioning

confidence: 99%

Section: Pso For Cvrpmentioning

confidence: 99%

Section: Pso For Cvrpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bilayer Local Search Enhanced Particle Swarm Optimization for the Capacitated Vehicle Routing Problem

Ahmed

Sun

2018

Algorithms

View full text Add to dashboard Cite

show abstract

“…In these algorithms, CVRP instances involving more than 100 customers can rarely be solved to optimality due to a huge amount of computation time. Second, a heuristic algorithm, which is an algorithm that should find solutions among all feasible ones, composed of savings algorithm 11 , sweep algorithm 12,13 , sequential insertion algorithm 14 , petal algorithm 15,16 , two-phase insertion 17 , cluster-first route-second algorithm 18 , 2-petals algorithm 19 , k-opt heuristic 20 , Orexchanges 21 , and λ-interchanges 22 . These algorithms usually find a feasible solution (near optimal) fast and easily but they do not guarantee that the optimal solution will be found.…”

Section: Introductionmentioning

confidence: 99%

An improved Clarke and Wright savings algorithm for the capacitated vehicle routing problem

Pichpibul¹,

Kawtummachai²

2012

ScienceAsia

View full text Add to dashboard Cite

In this paper, we have proposed an algorithm that has been improved from the classical Clarke and Wright savings algorithm (CW) to solve the capacitated vehicle routing problem. The main concept of our proposed algorithm is to hybridize the CW with tournament and roulette wheel selections to determine a new and efficient algorithm. The objective is to find the feasible solutions (or routes) to minimize travelling distances and number of routes. We have tested the proposed algorithm with 84 problem instances and the numerical results indicate that our algorithm outperforms CW and the optimal solution is obtained in 81% of all tested instances (68 out of 84). The average deviation between our solution and the optimal one is always very low (0.14%).

show abstract

Solving min-max shortest-path problems on a network

Murthy¹,

Her²

1992

Naval Research Logistics

View full text Add to dashboard Cite

In this article we consider the problem of determining a path between two nodes in a network that minimizes the maximum of r path length values associated with it. This problem has a direct application in scheduling. It also has indirect applications in a class of routing problems and when considering multiobjective shortest‐path problems. We present a label‐correcting procedure for this problem. We also develop two pruning techniques, which, when incorporated in the label‐correcting algorithm, recognize and discard many paths that are not part of the optimal path. Our computational results indicate that these techniques are able to speed up the label‐correcting procedure by many orders of magnitude for hard problem instances, thereby enabling them to be solved in a reasonable time. © 1992 John Wiley & Sons, Inc.

show abstract

A Sequential Route-Building Algorithm Employing a Generalised Savings Criterion

Cited by 42 publications

References 0 publications

Bilayer Local Search Enhanced Particle Swarm Optimization for the Capacitated Vehicle Routing Problem

Bilayer Local Search Enhanced Particle Swarm Optimization for the Capacitated Vehicle Routing Problem

An improved Clarke and Wright savings algorithm for the capacitated vehicle routing problem

Solving min-max shortest-path problems on a network

Contact Info

Product

Resources

About