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A supply chain waste reduction optimization model using beam search algorithms for two-dimensional cutting problems with defects

Afsharian, Mohsen - In: Supply chain analytics 2 (2023), pp. 1-15

reduce computation times while yielding high-quality solutions. A simulation model is used to assess the performance of the …

Persistent link: https://www.econbiz.de/10014516480

A multi-agent-based real-time truck scheduling model for cross-docking problems with single inbound and outbound doors

Torbali, Bilge; Alpan, Gülgün - In: Supply chain analytics 3 (2023), pp. 1-19

Cross-docking is a logistics methodology employed in warehouses to gain a competitive advantage by consolidating and transferring freights directly from an inbound supplier to an outbound client with no or restricted storage. Real-time data processing is required for fast synchronisation of...

Persistent link: https://www.econbiz.de/10014516505

An agent-based simulation and logistics optimization model for managing uncertain demand in forest supply chains

Helo, Petri; Rouzafzoon, Javad - In: Supply chain analytics 4 (2023), pp. 1-12

inventory, and travel distance. In addition, various simulation scenarios are used to investigate potential facility locations …

Persistent link: https://www.econbiz.de/10014516531

A systematic review of vehicle routing problems and models in multi-echelon distribution networks

Nielsen, Peter; Dahanayaka, Mahekha; Perera, H. Niles; … - In: Supply chain analytics 7 (2024), pp. 1-12

The vehicle routing problem (VRP) is a combinatorial optimization problem that determines optimal routes to enhance distribution efficiency. One of the most popular strategies in freight distribution is multi-echelon distribution. Multi-echelon distribution networks often apply to supply chain...

Persistent link: https://www.econbiz.de/10015077546

A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains

Lahmiri, Salim - In: Supply chain analytics 2 (2023), pp. 1-7

-nearest neighbor algorithm, naïve Bayes, kernel naïve Bayes, decision trees, and support vector machine to distinguish between eleven … analysis (84.5%), k-NN algorithm (80.5%), and Gaussian naïve Bayes (71.3%). Accordingly, the choice of statistical machine … learning algorithm influences classification accuracy related to electric drive fault diagnosis. In addition, decision trees …

Persistent link: https://www.econbiz.de/10014516481

A bi-objective model for scheduling green investments in two-stage supply chains

Caramia, Massimiliano; Stecca, Giuseppe - In: Supply chain analytics 3 (2023), pp. 1-9

Investing in green technologies to increase sustainability in supply chains has become a common practice for two reasons: the first is directly related to the defense of the environment and people's health to smooth the emissions of pollutants; the second is the increasing consumer awareness of...

Persistent link: https://www.econbiz.de/10014516517

A multi-period discrete event simulation model for comparing synchronous and asynchronous facility reopening in global supply chains affected by disruption

Jha, Harshit; Mohan, Usha - In: Supply chain analytics 2 (2023), pp. 1-8

chains. This study extends the previous literature by using a multi-period discrete event simulation model to compare … results of simulation experiments show that for a very low demand upon reopening, asynchronous reopening gives higher supply …

Persistent link: https://www.econbiz.de/10014516479

A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality

Tadayonrad, Yasin; Ndiaye, Alassane Ballé - In: Supply chain analytics 3 (2023), pp. 1-15

Forecasting demand and determining safety stocks are key aspects of supply chain planning. Demand forecasting involves predicting future demand for a product or service using historical data and other external and internal drivers. Stockouts and excess production can be reduced by accurately...

Persistent link: https://www.econbiz.de/10014516504