A Nearest Centroid Classifier-Based Clustering Algorithm for Solving Vehicle Routing Problem

Praveen, V.; Kousalya, K.; Kumar, Kunal

doi:10.1007/978-981-10-3812-9_59

Cited by 8 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Defryn and Sörensen [35] grouped customers into multiple clusters; by doing so, vehicles could be reasonably allocated to each cluster. Praveen et al [36] proposed a new clustering algorithm for effectively mining data, which laid the foundation for further optimization of vehicle routes.…”

Section: Related Solution Methods and Objectives For Cmcvrp-rsmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative multicenter logistics delivery network optimization with resource sharing

et al. 2020

View full text Add to dashboard Cite

Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

show abstract

Section: Related Solution Methods and Objectives For Cmcvrp-rsmentioning

confidence: 99%

“…Eq (35) guarantees that the value of X j (A,V) is between X jmin and X jmax . The Cost reduction percentage η(i, ϕ,u) of participant i in sequence ϕ when the uth participant joins the alliance is calculated using Eq (36 Case study…”

Section: Plos Onementioning

confidence: 99%

Collaborative multicenter logistics delivery network optimization with resource sharing

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Medical Solid Waste (MSW) collection and transportation has become a major concern in large urban areas (Das & Bhattacharyya, 2015; Praveen et al., 2017; Vu et al., 2019). MSW should be collected directly from hospitals and medical centers, and transported to disposal sites as quickly as possible via safe and secure methods.…”

Section: Introductionmentioning

confidence: 99%

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

Bagheri,

Samany,

Toomanian

et al. 2024

Transactions in GIS

View full text Add to dashboard Cite

Medical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.

show abstract

Evolutionary Large‐Scale Multi‐Objective Optimization in Logistics Scheduling

2024

Evolutionary Large‐Scale Multi‐Objective Optimization and Applications

View full text Add to dashboard Cite

A Nearest Centroid Classifier-Based Clustering Algorithm for Solving Vehicle Routing Problem

Cited by 8 publications

References 6 publications

Collaborative multicenter logistics delivery network optimization with resource sharing

Collaborative multicenter logistics delivery network optimization with resource sharing

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

Evolutionary Large‐Scale Multi‐Objective Optimization in Logistics Scheduling

Contact Info

Product

Resources

About