Mathematical models for green vehicle routing problems with pickup and delivery: A case of semiconductor supply chain

Madankumar, Sakthivel; Rajendran, Chandrasekharan

doi:10.1016/j.cor.2016.03.013

Cited by 52 publications

(23 citation statements)

References 4 publications

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“…The following methods were found: branch and cut algorithm (5 papers) [14][15][16][17][18]; branch and price algorithm (2 papers) [19,20]; branch and bound algorithm (1 paper), [21] dynamic programming (5 papers) [22][23][24][25][26]; mixed integer linear programming (5 papers) [27][28][29][30][31]; experiments (1 paper) [32]; column generation scheme (4 papers) [33][34][35][36]; classical theory of programming and graph theory (1 paper) [37].…”

Section: Exact Methodsmentioning

confidence: 99%

“…A single vehicle routing problem with pickups and deliveries, continuous random demands and customers served according to a particular order are treated in [30]. In 2016, [31] proposed two mixed integer linear programming (milp) models for solving the green vehicle routing problems with pickups and deliveries in a semiconductor supply chain (g-vprpd-ssc). Design of experiments: in 2009 [32] load in the delivery and pickup that is affected by the average size, number of sources relating to the destinations, grouping of depots and customer locations.…”

Section: Exact Methodsmentioning

confidence: 99%

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Description of the classification of publications and the models used in solving of the vehicle routing problem with pickup and delivery

Silva

Zuluaga

2016

Rev. ing. univ. Medellín

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This review paper presents the description of the state of the art of the vehicle routing problem with pickups and deliveries, vrppd, product obtained from the literature review of most of the models reported in the international technical literature and used for many years by specialized researchers in the field. This classification considers the different solution methods, the treatment of the problem in time and future trends in its formulation, and proposal of new methods of solution, such as the metaheuristics caused by the complexity of the problem. Finally, several elements are left to discuss about existing and available tools for solving this problem.

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Section: Exact Methodsmentioning

confidence: 99%

Section: Exact Methodsmentioning

confidence: 99%

Description of the classification of publications and the models used in solving of the vehicle routing problem with pickup and delivery

Silva

Zuluaga

2016

Rev. ing. univ. Medellín

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“…The detailed explanation of Fig. 1 is as follows: distance-constrained capacitated vehicle routing problem -DCCVPR [6]; vehicle routing problem with backhauls -VRPB [7]; vehicle routing problem with time window -VRPTW [8], vehicle routing problem with pickup and delivery -VRPPD [9]; vehicle routing problem with backhauls and time window -VRPBTW [10]; vehicle routing problem with pickup and delivery and time window -VRPPDTW [11] In the above vehicle routing problems, the demands of the given transport network are deterministic, i.e. known in advance.…”

Section: Fig 1 Basic Vehicle Routing Problems and Their Relationsmentioning

confidence: 99%

The Vehicle Routing Problem With Stochastic Demands in an Urban Area – A Case Study

Marković¹,

Petrovć²,

Ćojbašić³

et al. 2020

FU Mech Eng

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The vehicle routing problem with stochastic demands (VRPSD) is a combinatorial optimization problem. The VRPSD looks for vehicle routes to connect all customers with a depot, so that the total distance is minimized, each customer visited once by one vehicle, every route starts and ends at a depot, and the travelled distance and capacity of each vehicle are less than or equal to the given maximum value. Contrary to the classical VRP, in the VRPSD the demand in a node is known only after a vehicle arrives at the very node. This means that the vehicle routes are designed in uncertain conditions. This paper presents a heuristic and meta-heuristic approach for solving the VRPSD and discusses the real problem of municipal waste collection in the City of Niš.

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“…The results were found to outperform some genetic algorithm (GA)-based methods. Madankumar and Rajendran [29] studied the green VRP with pickup and delivery, and used the semiconductor supply chain as an example. Three MILP models were constructed.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

Kang

Lee

2018

Symmetry

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The vehicle routing problem (VRP) is a challenging combinatorial optimization problem. This research focuses on the problem under which a manufacturer needs to outsource materials from other suppliers and to ship the materials back to the company. Heterogeneous vehicles are available to ship the materials, and each vehicle has a limited loading capacity and a limited travelling distance. The purpose of this research is to study a multiple vehicle routing problem (MVRP) with soft time window and heterogeneous vehicles. Two models, using mixed integer programming (MIP) and genetic algorithm (GA), are developed to solve the problem. The MIP model is first constructed to minimize the total transportation cost, which includes the assignment cost, travelling cost, and the tardiness cost, for the manufacturer. The optimal solution can present multiple vehicle routing and the loading size of each vehicle in each period. The GA is next applied to solve the problem so that a near-optimal solution can be obtained when the problem is too difficult to be solved using the MIP. A case of a food manufacturing company is used to examine the practicality of the proposed MIP model and the GA model. The results show that the MIP model can obtain the optimal solution under a short computational time when the scale of the problem is small. When the problem becomes non-deterministic polynomial hard (NP-hard), the MIP model cannot find the optimal solution. On the other hand, the GA model can obtain a near-optimal solution within a reasonable amount of computational time. This paper is related to several important topics of the Symmetry journal in the areas of mathematics and computer science theory and methods. In the area of mathematics, the theories of linear and non-linear algebraic structures and information technology are adopted. In the area of computer science, theory and methods, and metaheuristics are applied.Unit of measure: min.Symmetry 2018, 10, 650 9 of 20 Table 6 lists the problem configurations for the three cases, including the number of periods, number of suppliers, number of vehicles, size of the transportation network, number of variables for the MIP, and number of constraints for the MIP. In the first case, there are five periods, the number of suppliers is five, the number of vehicles is three, the transportation network is 6 × 6, and the numbers of variables and constraints for the MIP model are 931 and 1171, respectively. In the second case, there are seven periods, the number of suppliers is nine, the number of vehicles is four, the transportation network is 10 × 10, and the numbers of variables and constraints for the MIP model are 3903 and 3977, respectively. In the third case, there are nine periods, the number of suppliers is 12, the number of vehicles is five, the transportation network is 13 × 13, and the numbers of variables and constraints for the MIP model are 9758 and 9919, respectively.

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Mathematical models for green vehicle routing problems with pickup and delivery: A case of semiconductor supply chain

Cited by 52 publications

References 4 publications

Description of the classification of publications and the models used in solving of the vehicle routing problem with pickup and delivery

Description of the classification of publications and the models used in solving of the vehicle routing problem with pickup and delivery

The Vehicle Routing Problem With Stochastic Demands in an Urban Area – A Case Study

An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

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