Heuristics Algorithms for a Heterogeneous Fleets VRP with Excessive Demand for the Vehicle at the Pickup Points, and the Longest Traveling Time Constraint: A Case Study in Prasitsuksa Songkloe, Ubonratchathani Thailand

Kaewman, Sasitorn; Akararungruangkul, Raknoi

doi:10.3390/logistics2030015

Cited by 11 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article added these two steps to increase the performance of the original DE. The DE can be improved by adding more steps into the original steps and other techniques such as use various mutation and recombination formula [21,22,28], add effective local search techniques [27], add the best vector reproduction process [29], develop new decoding method which is combined with ant colony optimization [30]. Therefore, in this article, the DE has been improved in following ways so that the effectiveness of DE is increased: (1) provided an effective decoding method; (2) presented new rules of the selection process to let DE escape from the local optimal; and (3) used 3 different local searches so that the DE has intensive search efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Equation (27) defines the arrival time of round r truck t at raw milk farm k, while Equation (28) has a similar constraint, but it is used when the i is the factory. Equation (29) defines the departure time of round r truck t from raw milk farm k. Equations (30) and (31) are used to control the arrival time and finish time of round r truck t within the selected time windows. Equation (32) defines the ending time of the tour using truck t in round r as the time that it arrives at the factory after the tour.…”

Section: Objective Functionmentioning

confidence: 99%

“…The case study has been used to execute this experiment. The stopping criteria has been set to 1,5,10,20,30,40, 50 and 60 min. All algorithms have been executed 5 times, and the best solution among 5 solutions generated from each algorithm has been reported.…”

Section: # Of Clientsmentioning

confidence: 99%

See 2 more Smart Citations

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

2019

View full text Add to dashboard Cite

This research aims to solve the problem of the raw milk collection and transportation system which can be interpreted as a special case of the vehicle routing problem. In the proposed problem, the factory will send the trucks, multiple fleets composed of several compartments, to collect the raw milk from the raw milk farms. The objective of this research is to minimize the total transportation cost and the trucks' and tanks' cleaning costs. The transportation cost directly depends on the fuel usage. The fuel usage occurs during the transportation of the milk and during the waiting times when it arrives at the factory and cannot transfer the raw milk into the production tank. We develop the modified differential evolution algorithm (MDE) to solve the proposed problem. The original process of the Differential Evolution algorithm (DE) has been modified in two folds which are as follows: (1) In the recombination process, the 2nd order of trial vectors has been generated using 3 different strategies and compared with the 1st order trial vector; the better from the 1st and the 2nd order of trial vectors will move to the selection process. (2) The probability function has been used to select the new target vector from one of two sources which are the trial vector and the current target vector so that the worse solution can be accepted in order to increase the diversity of the original DE. The computational result shows that the modified DE (MDE) outperforms the original DE in finding a better solution.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%

See 1 more Smart Citation

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

2019

View full text Add to dashboard Cite

show abstract

“…In particular, the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) considers clients that require pick-up and delivery service simultaneously [6,7]. The Heterogeneous fleet VRP (HVRP) denotes different capacities of vehicles [8][9][10][11]. In particular, the paper [8] presents heterogeneous VRP review, including classification, considerable input data characteristics, and decision making approaches, and heuristic and meta-heuristics algorithms.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Vehicle Routing Problem—Predictive and Unexpected Customer Availability

Kucharska

2019

Symmetry

View full text Add to dashboard Cite

The Dynamic Vehicle Routing Problem (DVRP) is one of the most important problems in the area of enterprise logistics. DVRP problems involve these dynamics: the appearance of customers, travel times, service times, or vehicle availability. One of the most often considered aspects of the DVRP is the availability of customers, in which a part or all of the customers are revealed dynamically during the design or execution of the routes. A classification of the DVRP problem due to various elements causing dynamism is proposed. The aim of the paper is to distinguish dynamic VRP, which takes into account the dynamic appearance of customers to serve during the design or execution of the routes. In particular, the difference between the predictive and unexpected aspects of the customer’s availability is considered. Above all, the variant of customer’s availability which is predicted according to an appropriate general rule is modeled using the algebraic-logical meta-model (ALMM). It is a methodology which enables making collective decisions in successive process stages, not separately for individual vehicles. The algebraic-logical model of the dynamic vehicle routing problem with predicted consumer availability is proposed. The paper shows the possibilities of applying the ALMM approach to dynamic problems both with predicted and unexpected customer availability.

show abstract

“…Braekers et al (2015) provided an overview of the scenario and the problem's physical characteristics, extending the basic incapacitated VRP, which was most often considered in the reviewed articles. The special case of the VRP problem was proposed by Kaewman and Akararungruangkul (2018) and Akararungruangkul and Kaewman (2018), where one pickup point can be visited more than once and the road condition is considered as the objective function.…”

Section: Introductionmentioning

confidence: 99%

Methodology to Solve the Combination of the Generalized Assignment Problem and the Vehicle Routing Problem: A Case Study in Drug and Medical Instrument Sales and Service

Thongkham

Kaewman

2018

Administrative Sciences

Self Cite

View full text Add to dashboard Cite

This article presents algorithms for solving a special case of the vehicle routing problem (VRP). We define our proposed problem of a special VRP case as a combination of two hard problems: the generalized assignment and the vehicle routing problem. The different evolution (DE) algorithm is used to solve the problem. The recombination process of the original DE is modified by adding two more sets of vectors—best vector and random vector—and using two other sets—target vector and trial vector. The linear probability formula is proposed to potentially use one out of the four sets of vectors. This is called the modified DE (MDE) algorithm. Two local searches are integrated into the MDE algorithm: exchange and insert. These procedures create a DE and MDE that use (1) no local search techniques, (2) two local search techniques, (3) only the exchange procedure, and (4) only the insert procedure. This generates four DE algorithms and four MDE algorithms. The proposed methods are tested with 15 tested instances and one case study. The current procedure is compared with all proposed heuristics. The computational result shows that, in the case study, the best DE algorithm (DE-4) has a 1.6% better solution than that of the current practice, whereas the MDE algorithm is 8.2% better. The MDE algorithm that uses the same local search as the DE algorithms generates a maximum 5.814% better solution than that of the DE algorithms.

show abstract

Heuristics Algorithms for a Heterogeneous Fleets VRP with Excessive Demand for the Vehicle at the Pickup Points, and the Longest Traveling Time Constraint: A Case Study in Prasitsuksa Songkloe, Ubonratchathani Thailand

Cited by 11 publications

References 36 publications

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

Dynamic Vehicle Routing Problem—Predictive and Unexpected Customer Availability

Methodology to Solve the Combination of the Generalized Assignment Problem and the Vehicle Routing Problem: A Case Study in Drug and Medical Instrument Sales and Service

Contact Info

Product

Resources

About