An improved ant colony optimization and its application to vehicle routing problem with time windows

Ding, Qiulei; Hu, Xiangpei; Sun, Lixian; Wang, Yunzeng

doi:10.1016/j.neucom.2011.09.040

Cited by 73 publications

(46 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some of the most commonly used are: Tabu search (TS), developed from heuristic from local search while additionally including a solution evaluation, local searching tactics, termination criteria and elements such as tabus in a list and the tabu length (Jia et al, 2013); Ant Colony Optimization (ACO), inspired in the feeding process of the ants and is a collective intelligence algorithm used to solve complex combinatorial optimization problems as shortest path problems (Ding et al, 2012); and Genetic Algorithm (GA), defined as adaptive search heuristic that operates over a population of solutions, based on the evolution principle that improves the solution using crossover and mutation process (Pereira & Tavares, 2009). Another metaheuristic used to solve VRPs using neighborhoods is the work of RinconGarcia et al (2017) that uses Large Neighborhood Search approaches and Variable Neighborhood Search techniques to guide the search.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid expert system, clustering and ant colony optimization approach for scheduling and routing problem in courier services

López-Santana¹,

Rodríguez-Vásquez²,

Méndez-Giraldo³

2018

10.5267/j.ijiec

View full text Add to dashboard Cite

This paper focuses on the problem of scheduling and routing workers in a courier service to deliver packages for a set of geographically distributed customers and, on a specific date and time window. The crew of workers has a limited capacity and a time window that represents their labor length. The problem deals with a combination of multiples variants of the vehicle routing problem as capacity, multiple periods, time windows, due dates and distance as constraints. Since in the courier services the demands could be of hundreds or thousands of packages to be delivered, the problem is computationally unmanageable. We present a threephase solution approach. In the first phase, a scheduling model determines the visit date for each customer in the planning horizon by considering the release date, due date to visit and travel times. We use an expert system based on the know-how of the courier service, which uses an inference engine that works as a rule interpreter. In the second phase, a clustering model assigns, for each period, customers to workers according to the travel times, maximum load capacity and customer's time windows. We use a centroid based and sweep algorithms to solve the resulted problem. Finally, in the third phase, a routing model finds the order in which each worker will visit all customers taking into account their time windows and worker's available time. To solve the routing problem we use an Ant Colony Optimization metaheuristic. We present some numerical results using a case study, in which the proposed method of this paper finds better results in comparison with the current method used in the case study.

show abstract

Section: Background and Literature Reviewmentioning

confidence: 99%

“…In addition, if we consider that ACO metaheuristic has also been used for the VRPTW, more applications can be found in the literature (e.g. Cheng & Mao, 2007;Ding et al, 2012;Pureza et al, 2012;Yu et al, 2011). Cheng and Mao (2007) propose an ACO to solve the TSPTW.…”

Section: Solution Proceduresmentioning

confidence: 99%

A hybrid expert system, clustering and ant colony optimization approach for scheduling and routing problem in courier services

López-Santana¹,

Rodríguez-Vásquez²,

Méndez-Giraldo³

2018

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…FOSP was classified into two categories by Kacem et al performances of our proposed DACO with few strategies including AL + CGA [37]. Operation M1 M2 M3 M4 M5 M6 M7 M8 M9 PSO + TS [17], PVNS [38], KBACO [19], and TSPCB [13].…”

Section: Test On Kacem Benchmark Instancesmentioning

confidence: 99%

“…In addition, the running time(RT) of the proposed DACO is very short; for instance, it can find thebestsolutionforthe10×10T-FOSP in the second option within 5s. Table 4 illustrates the comparison of the best value and the average value between DACO and those from literatures (BEDA [39]; PBABC [40]; EA [41]; and EQEA [9]) on the Kacem benchmark instances [37]. For each instance, all algorithms are run for 10 times independently.…”

Section: Test On Kacem Benchmark Instancesmentioning

confidence: 99%

Single Objective for an Integer Partial Flexible Open Shop Scheduling Problem Using Developed Ant Colony Optimization

Umashankar¹,

Tjprc²

2018

IJMPERD

View full text Add to dashboard Cite

Since, the key ACO has two essential bothers including low computational efficiency and local ideal. In defect these two brothers, a developed ant colony optimization (DACO) is proposed to propel the make span for IPFOSP.The accompanying perspectives are done on our developed ant colony optimization algorithm: select machine govern problems, instate uniform appropriated mechanism for ants, change pheromone's coordinating mechanism, select hub method, and refresh pheromone's mechanism. The genuine production instance and two plans of well -known benchmark instances are inspected and correlations with some unique approaches conform the viability of the proposed DACO. The results reveal that our proposed DACO can give better arrangement in a sensitive computational time.

show abstract

“…Other ants, notice pheromone track, are attracted to go along it. Therefore, the more ants will be attracted as the path will be enhanced [16]. Pheromones have a propensity to evaporate and therefore, over an interval of time, larger extent of pheromone accumulated at the shortest trail as compared to other trails and this becomes the preferred trail.…”

Section: B Proposed Algorithmmentioning

confidence: 99%

Evaluation of Capacitated Vehicle Routing Problem with Time Windows using ACO-GA

Gupta¹,

Diwaker²

2017

IJARCSSE

View full text Add to dashboard Cite

Abstract-The capacitated vehicle routing problem with time windows (CVRPTW), broadly utilize in practice, comes under category of NP-hard problem. This paper introduces an Ant Colony Optimization-Genetic Algorithm (ACO-GA) for solving the CVRPTW. In CVRPTW, vehicles must follow the constraint of time windows allied with every customer along with capacity constraints. In ACO-GA mutation operators of Genetic Algorithm (GA) are introduced as these operators enhance the diversity of population, solution space can be explored completely. The effectiveness of ACO-GA is evaluated by comparing it with other meta-heuristics such as GA and ACO.

show abstract

An improved ant colony optimization and its application to vehicle routing problem with time windows

Cited by 73 publications

References 15 publications

A hybrid expert system, clustering and ant colony optimization approach for scheduling and routing problem in courier services

A hybrid expert system, clustering and ant colony optimization approach for scheduling and routing problem in courier services

Single Objective for an Integer Partial Flexible Open Shop Scheduling Problem Using Developed Ant Colony Optimization

Evaluation of Capacitated Vehicle Routing Problem with Time Windows using ACO-GA

Contact Info

Product

Resources

About