**5. Conclusions**

Agri-production is intrinsically connected with several uncertain sources, mainly the demand patterns and production yields. From the viewpoint of sugar-cane supply chain planning, few contributions take on tactical and strategic decisions. This paper proposes an optimization model for sugarcane supply chain planning, integrating several agricultural decisions from a strategic-tactical planning perspective. Uncertainties implicate surges in demand which need to be tackled by a variable production supply chain. Indeed, technology development has enforced the agri-business stakeholders to rethink strategies and adopt a better, eco-friendly production environment with optimized costs. Despite high tech procedures, intelligent systems are an hour of need, and this work has proposed an e fficient human-machine interactive model embedded, thus going a step ahead for greener production. Further, scrap and reworked products are also dealt with in the model. The right decision at the right time will lead the agricultural industry towards intelligent and smart systems. The study is a form of strategic managemen<sup>t</sup> approach for the traders, logistics, retailers, manufacturers in agri-SCM to manage resources and to control carbon emissions for cleaner production.

In this work, a non-derivative technique is designed to integrate an algebraic approach in the agri-product based supply chain to optimize the resources and coup with variable demands through a controllable production rate. The analysis is providing a platform for manufacturing managers to invest in favor of advanced technology in agri-SCM, which ultimately leads to a less rejection production environment for clean manufacturing. The solution methodology of the proposed model included manufacturing limitations in the integration of the objective formulations with the developed system. Results findings and sensitivity analysis. The focus of these analyses is to evaluate sensitivity for an optimal solution to the value of uncertain parameters, providing confidence in the solution of the model. Managerial insights are largely beneficial to agri-SCM for the agri-food processing industry and to the people with cleaner production and carbon emission prioritized policies.

The research work can be extended into a three-echelon agri-SCM model by considering the farming industry and agri-retailer. The uncertain factors in the form of costs, prices, inflation, and time value can be dealt with using the fuzzy set theorems. The deterministic model can be converted into probabilistic or stochastic theorems for the implication of real scenarios. Overall, the agri-product supply chain needs be developed globally to make food more secure and accessible.

**Author Contributions:** Conceptualization, M.A. and M.O.; methodology, M.A and Q.S.K.; software, Q.S.K.; validation, M.A. and Q.S.K.; formal analysis, M.A and G.H.; investigation, G.H and B.S.; data curation, M.O and G.H.; writing—original draft preparation, M.A.; writing—review and editing, Q.S.K and B.S.; supervision, B.S.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Researchers Supporting Project Number (RSP-2020/274), King Saud University, Riyadh, Saudi Arabia.

**Acknowledgments:** The work was supported by Researchers Supporting Project Number (RSP-2020/274), King Saud University, Riyadh, Saudi Arabia. The authors are also thankful to University of Engineering and Technology, Peshawar and GIK Institute for providing necessary technical assistance.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A. Model Notation**

The list of notation for agri-processing firm and vendor are given in the form of indices, decision variables, manufacturer, and vendor parameters.
