A firefly algorithm for the environmental prize-collecting vehicle routing problem

被引:44
作者
Trachanatzi, Dimitra [1 ]
Rigakis, Manousos [1 ]
Marinaki, Magdalene [1 ]
Marinakis, Yannis [1 ]
机构
[1] Tech Univ Crete, Sch Prod Engn & Management, Univ Campus, Iraklion, Greece
关键词
Environmental vehicle routing problem; Prize-collecting vehicle routing problem; Firefly algorithm; Coordinates related encoding/decoding process; LOCAL SEARCH ALGORITHM; FUEL CONSUMPTION; HYBRID; DISCRETE; DESIGN; MODEL; INTELLIGENCE; NEIGHBORHOOD; COLONY; SWARM;
D O I
10.1016/j.swevo.2020.100712
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the present research, a new variant of the Vehicle Routing Problem (VRP), the Environmental Prize-Collecting Vehicle Routing Problem (E-PCVRP), is introduced. The E-PCVRP is a selective routing problem that focuses on the maximization of the aggregated prize values collected from the visited nodes while minimizing the fixed and variable cost of the formed routes. In terms of variable cost, the CO2 emissions of the vehicles performing the routes are considered as a load-distance function. The presented solution approach is based on the Firefly Algorithm (FA). The FA is an optimization algorithm, designed for the solution of continuous problems, while the proposed E-PCVRP, requires a discrete solution approach. Addressing the above discrepancy, the Firefly Algorithm based on Coordinates (FAC) is introduced, which incorporates the proposed "Coordinates Related" (CR) encoding/decoding process in the original FA scheme. The CR is a novel process that allows for algorithms designed for continuous optimization to by employed in the solution of discrete problems, such as the VRP. Specifically, the CR utilizes auxiliary vectors for solution representation, containing the Cartesian coordinates of each node, that allows for the original movement equation of the FA to be applied directly. The effectiveness of the FAC algorithm is showed over computational experiments and statistical analysis, in comparison to the performance of other bio-inspired algorithms and a mathematical solver.
引用
收藏
页数:15
相关论文
共 60 条
[1]   A novel comprehensive macroscopic model for time-dependent vehicle routing problem with multi-alternative graph to reduce fuel consumption: A case study [J].
Alinaghian, Mehdi ;
Naderipour, Mansoureh .
COMPUTERS & INDUSTRIAL ENGINEERING, 2016, 99 :210-222
[2]   An improved hybrid firefly algorithm for capacitated vehicle routing problem [J].
Altabeeb, Asma M. ;
Mohsen, Abdulqader M. ;
Ghallab, Abdullatif .
APPLIED SOFT COMPUTING, 2019, 84
[3]   Selective multi-depot vehicle routing problem with pricing [J].
Aras, Necati ;
Aksen, Deniz ;
Tekin, Mehmet Tugrul .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2011, 19 (05) :866-884
[4]  
Ayadi R, 2014, PROCEEDINGS OF 2014 2ND IEEE INTERNATIONAL CONFERENCE ON LOGISTICS AND OPERATIONS MANAGEMENT (GOL 2014), P148, DOI 10.1109/GOL.2014.6887432
[5]   Discrete Optimum Design of Truss Structures by an Improved Firefly Algorithm [J].
Baghlani, A. ;
Makiabadi, M. H. ;
Sarcheshmehpour, M. .
ADVANCES IN STRUCTURAL ENGINEERING, 2014, 17 (10) :1517-1530
[6]   The Pollution-Routing Problem [J].
Bektas, Tolga ;
Laporte, Gilbert .
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2011, 45 (08) :1232-1250
[7]   The vehicle routing problem with service level constraints [J].
Bulhoes, Teobaldo ;
Minh Hoang Ha ;
Martinelli, Rafael ;
Vidal, Thibaut .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2018, 265 (02) :544-558
[8]   A HEURISTIC FOR THE MULTIPLE TOUR MAXIMUM COLLECTION PROBLEM [J].
BUTT, SE ;
CAVALIER, TM .
COMPUTERS & OPERATIONS RESEARCH, 1994, 21 (01) :101-111
[9]   Open vehicle routing problem with demand uncertainty and its robust strategies [J].
Cao Erbao ;
Lai Mingyong ;
Yang Hongming .
EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (07) :3569-3575
[10]   Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review [J].
Carrasco, J. ;
Garcia, S. ;
Rueda, M. M. ;
Das, S. ;
Herrera, F. .
SWARM AND EVOLUTIONARY COMPUTATION, 2020, 54