Dataset for SR-SCND of Printer Products

Description

This dataset contains the input parameters and optimization results used to develop and evaluate a Sustainable and Resilient Supply Chain Network Design (SR-SCND) model for a closed-loop supply chain of printer products. The mathematical model integrates economic, environmental, and social objectives using a weighted sum multi-objective optimization approach. The dataset includes synthetic but reproducible data describing facility capacities, transportation characteristics, consumer demand, environmental awareness, cost parameters, environmental parameters, social parameters, and transportation modes. It also contains optimization results for three decision scenarios: Baseline scenario representing normal operating conditions. Disruption scenario representing a transportation disruption between the manufacturer and a distribution center. Disruption mitigation scenario using lateral transshipment to improve network resilience. The optimization results include production quantities, remanufacturing quantities, facility opening decisions, material flows, transportation decisions, economic performance, environmental impacts, social performance, facility utilization, fleet utilization, and lateral transshipment activities. The dataset is intended to support reproducibility of the proposed optimization model and may be used for benchmarking, comparative studies, and future research in sustainable and resilient supply chain network design, reverse logistics, and circular economy.

Steps to reproduce

The dataset can be reproduced by downloading the Excel workbook and reviewing the worksheets containing the input parameters and optimization results. The workbook provides all numerical data used in the computational experiments, including facility capacities, customer demand, transportation characteristics, cost, environmental and social parameters, and consumer environmental awareness. The optimization results are organized into three scenarios: baseline, disruption, and disruption with lateral transshipment. Researchers can use these data directly for benchmarking, validation, comparative studies, or as input for implementing the mathematical optimization model described in the associated dissertation or publication. Using the same input parameters and optimization settings will enable researchers to reproduce and verify the reported results.

Categories

Funders

• University of Brawijaya

  Malang

Licence