Two-phase integrated multi-criteria and multi-objective framework for green and resilient food supply chains under uncertainty
EGYPTIAN INFORMATICS JOURNAL, vol.34, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 34
- Publication Date: 2026
- Doi Number: 10.1016/j.eij.2026.100996
- Journal Name: EGYPTIAN INFORMATICS JOURNAL
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Directory of Open Access Journals
- Azerbaijan State University of Economics (UNEC) Affiliated: Yes
Abstract
Green and resilient supplier selection and order allocation (SSOA) is becoming increasingly challenging because decision data are often incomplete, vague, or conflicting. Many fuzzy multi-attribute decision making (MADM) and multi-objective decision making (MODM) methods still face two key limitations: uncertainty may not be captured sufficiently, and SSOA are frequently handled as separate steps, which can lead to inconsistent or sub-optimal decisions. To overcome these issues, this study proposes a two-phase decision-support framework developed in the p,q-quasirung orthopair fuzzy set (p,q-QOFS) environment. In Phase I, suppliers are evaluated using a hybrid MADM approach under p,q-QOFS. Reliable criteria weights are computed via a p,q-QOFS-based fuzzy weighted zero-inconsistency (FWZIC) method, and suppliers are then ranked using a p,q-QOFS-based DOmbi-Bonferroni weighted ASsessment (DOBAS) method incorporating traditional, environmental, and resilience criteria. In Phase II, a multi-objective programming model is formulated for order allocation with cost, capacity, and resilience constraints. NSGA-III is employed to generate Pareto-optimal solutions, and a TOPSIS-based search-filter step is used to select the best compromise solution. The framework is tested using a real case of food manufacturing company that want to solve SSOA problem involving multiple suppliers and several materials. Results show that resilience criteria strongly influence order allocation, and sensitivity analyses confirm that the solutions remain stable under different settings. Pareto analysis indicates that Total Relevant Cost (TRC) and Environmental Impact (EI) often increase together; in contrast, higher Resilient Sourcing Value (RSV) usually requires higher TRC and EI, with diminishing gains at higher RSV levels. These findings suggest selecting a balanced solution that improves resilience without a large rise in TRC and EI for practical decision-making. Although the case study is limited to one sector, the proposed framework can be adapted to SSOA problems in other industries.