Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations. (June 2021)

• Record Type: Journal Article
• Authors: Theophilus, Oluwatosin; Dulebenets, Maxim A.; Pasha, Junayed; Lau, Yui-yip; Fathollahi-Fard, Amir M.; Mazaheri, Arash
• Is Part Of: Computers & industrial engineering. Volume 156(2021)
• Publication Date: 2021-06
• Subjects: Cold supply chains -- Cross-docking -- Perishable products -- Truck scheduling -- Optimization -- Metaheuristics; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107240
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A truck scheduling problem is studied at a cross-docking terminal. The cold-chain terminal handles perishable products. The product decay is modeled using an exponential function. A customized Evolutionary Algorithm is developed to solve the model. The computational experiments demonstrate the algorithm efficiency and additional insights. Abstract: An efficient product distribution is critical for proper supply chain operations. Many supply chains handle perishable products that decay over time. Due to mismanagement of supply chain operations, a significant portion of perishable products is wasted, resulting in substantial monetary losses. Cross-docking terminals (CDTs) have been widely used in cold supply chains for the product distribution but have not received adequate attention in the scientific literature. To improve the efficiency of perishable product distribution, this study introduces for the first time a novel mixed-integer mathematical formulation for the truck scheduling optimization at a cold-chain CDT. The model explicitly captures the decay of perishable products throughout the service of arriving trucks and accounts for the presence of temperature-controlled storage areas that are specifically designated for perishable products. The objective minimizes the total cost incurred during the truck service. Considering the complexity of the proposed model, a customized Evolutionary Algorithm is developed to solve it. The computational performance of the developed algorithm is assessed throughout the numerical experiments based on a detailed comparative analysis against the other metaheuristics. The developed Evolutionary Algorithm is found to be the most promising metaheuristic, considering both solution quality and CPU time perspectives. Furthermore, the proposed algorithm demonstrates an acceptable stability of the solution quality at termination. A set of additional sensitivity analyses are performed in order to draw some significant managerial implications, which would be of potential interest to the supply chain stakeholders that are involved in the distribution of perishable products in cold supply chains.