A Simulated Annealing Heuristic for the Heterogeneous Fleet Pollution Routing Problem

Yu, Vincent F.; Redi, Anak Agung Ngurah Perwira; Jewpanya, Parida; Lathifah, Artya; Maghfiroh, Meilinda Fitriani Nur; Masruroh, Nur Aini

doi:10.1007/978-981-13-0451-4_10

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The average total cost of emissions was reduced by 18.88% for all instances.The reduction in total costs on larger data is caused by several things including differences in the types of different vehicles used, the routes formed and the depots that are opened. This is shown in the table that there are differences in the use of vehicle types when compared with the [13].…”

Section: Results Discussionmentioning

confidence: 99%

“…Hopefully it can provide more comprehensive exploration for better results because this study only uses the SA algorithm with basic environmental movements.We also recommend using metaheuristics that is suitable for combinatorial cases such as routing problems in this study, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). [13] No Instances…”

Section: Resultsmentioning

confidence: 99%

“…The running results will be compared with [13] which discusses the Heterogeneous Fleet Pollution Routing Problem (HFPRP) in which results are running with CPLEX for exact solutions and computational results for HFPRP, so that there will be differences in the total emissions costs if there are potential depots others to choose from. For small instances (10 Nodes) [13] done by two methods which are CPLEX and Heuristics using Simulated Annealing, on the other hand due to the computational time, the larger instances (50) nodes, they used only heuristics method (Simulated Annealing).…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Development of an Environmentally-Friendly Logistics Model by Integrating Decisions of Location, Multi-Capacity Vehicle, and Routing Problem

Lathifah

Sulistyo

Murti

et al. 2018

JEMIS

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Transportation and distribution are closely related to logistics. However, these activities sometimes damage the environment. Emissions from the fuels caused by transportation and distribution activities accounted for 29.4% of the total transportation costs. As a result, many organizations include environmentally planning as priority in their activities, where the goal are minimizing distribution costs and maintaining sustainability of environment simultaneously. To improve this, some factors that can be improved are: determination of the depot location, combination of vehicle and the route. Therefore, this study aims to develop mathematical model that optimize these integrated factors to minimize the emission cost. The results of this research are the mathematical model and optimization of the model using Simulated Annealing (SA) method. This method can obtain a reduction of the total emissions costs up to 18.8%.

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Section: Results Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

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Development of an Environmentally-Friendly Logistics Model by Integrating Decisions of Location, Multi-Capacity Vehicle, and Routing Problem

Lathifah

Sulistyo

Murti

et al. 2018

JEMIS

Self Cite

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“…The VRP is solved by minimizing the total distance under certain constraints, such as, capacity limit, route length limit and time window based on the assumption that transportation with a shorter route leads to a lower cost. Yu et al (2018) utilized SA to solve a variant of the VRP, called the heterogeneous fleet pollution routing problem (HFPRP). The performance of the SA algorithm was confirmed using the benchmark instance of the pollution pouting where it showed better results compared to adaptive large neighborhood search (ALNS) and the solution obtained by CPLEX.…”

Section: Introductionmentioning

confidence: 99%

Solving vehicle routing problem with time windows using metaheuristic approaches

Birecik

Özgen

2022

IJICC

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PurposeDrugs are strategic products with essential functions in human health. An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and adverse effects on human health. The vehicle-routing problem, focused on finding the lowest-cost routes with available vehicles and constraints, such as time constraints and road length, is an important aspect of this. In this paper, the vehicle routing problem (VRP) for a pharmaceutical company in Turkey is discussed.Design/methodology/approachA mixed-integer programming (MIP) model based on the vehicle routing problem with time windows (VRPTW) is presented, aiming to minimize the total route cost with certain constraints. As the model provides an optimum solution for small problem sizes with the GUROBI® solver, for large problem sizes, metaheuristic methods that simulate annealing and adaptive large neighborhood search algorithms are proposed. A real dataset was used to analyze the effectiveness of the metaheuristic algorithms. The proposed simulated annealing (SA) and adaptive large neighborhood search (ALNS) were evaluated and compared against GUROBI® and each other through a set of real problem instances.FindingsThe model is solved optimally for a small-sized dataset with exact algorithms; for solving a larger dataset, however, metaheuristic algorithms require significantly lesser time. For the problem addressed in this study, while the metaheuristic algorithms obtained the optimum solution in less than one minute, the solution in the GUROBI® solver was limited to one hour and three hours, and no solution could be obtained in this time interval.Originality/valueThe VRPTW problem presented in this paper is a real-life problem. The vehicle fleet owned by the factory cannot be transported between certain suppliers, which complicates the solution of the problem.

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Green Vehicle Routing Problem (GVRP): State-of-the-Art

Oumachtaq

Ouzizi

Douimi

2023

Lecture Notes in Mechanical Engineering

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A Simulated Annealing Heuristic for the Heterogeneous Fleet Pollution Routing Problem

Cited by 6 publications

References 25 publications

Development of an Environmentally-Friendly Logistics Model by Integrating Decisions of Location, Multi-Capacity Vehicle, and Routing Problem

Development of an Environmentally-Friendly Logistics Model by Integrating Decisions of Location, Multi-Capacity Vehicle, and Routing Problem

Solving vehicle routing problem with time windows using metaheuristic approaches

Green Vehicle Routing Problem (GVRP): State-of-the-Art

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