Transportation scheduling optimization by a collaborative strategy in supply chain management with TPL using chemical reaction optimization

• Published in: Neural computing & applications Vol. 32; no. 8; pp. 3649 - 3674
• Main Authors: Islam, Md. Rafiqul, Mahmud, Md. Riaz, Pritom, Rayhan Morshed
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2020; Springer Nature B.V
• Subjects: Algorithms; Ant colony optimization; Artificial Intelligence; Chemical reactions; Collaboration; Combinatorial analysis; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer simulation; Computing time; Data Mining and Knowledge Discovery; Datasets; Heuristic methods; Image Processing and Computer Vision; Linear programming; Logistics management; Nodes; Optimization; Original Article; Probability and Statistics in Computer Science; Resource management; Route planning; Scheduling; Supply chain management; Supply chains; Transportation; Collaborative transportation scheduling; Supply chain management; Third-party logistics; Random matrix generation; Chemical reaction optimization; Meta-heuristics algorithms
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04218-5

Optimization of supply chain management is a way of ensuring the usability of resources and related technologies at the best possible way. Transportation scheduling of vehicle and transportation nodes in supply chain management is an important factor in order to create a stable chained network by ensuring the highest amount of product distribution and lowest logistics cost. In recent years, a number of programming models like linear programming, heuristics and meta-heuristics optimization approaches are proposed by the researchers to solve this combinatorial NP-hard problem. In this paper, we have studied and analyzed the nature of transportation vehicle scheduling problem in a supply chain network with the help of third-party logistics enterprise by using a meta-heuristic algorithm called chemical reaction optimization (CRO). At first, we have classified all the transportation nodes into three distinct classifications. Then, a collaborative transportation scheduling strategy is used which is based on two significant kinds of transportation nodes. For the first two kinds of nodes, we have randomly created a large number of combined transportation routes, and the vehicle scheduling for the last standalone nodes is created by a random matrix generation. Then, we have proposed a CRO algorithm using four reaction operators with an additional repair operator to find out the best transportation routes within shortest computing time. We named our proposed algorithm as chemical reaction optimization for supply chain management (CRO-SCM). The proposed CRO-SCM algorithm is analyzed with the standard dataset from the proposed model using modified ACO-NSO algorithm which is the state of the art. In addition, a random dataset of different scales of transportation nodes is considered to evaluate the efficiency of the algorithm. Moreover, six different scales of problem sets consisting different number of nodes are adopted to analyze the performance of the proposed CRO algorithm. The simulation results demonstrate that the proposed approach is practical and efficient than existing ACO-based solutions and the experimental results are more efficient and optimal.