Optimizing Logistics with Mathematical Methods: Assessment Answer

Introduction

The logistics industry can be one in which mathematical optimization can be essential. It includes all the activities about sourcing the inputs, allocating them effectively, and distributing outputs or finished products generated to the end consumers. It encompasses transportation, warehousing, inventory management, and others. This report will help to comprehend why a logistics organization should use the mathematical optimization technique and how it can be utilized to get the best solution. 

What Is Mathematical Optimization?

Mathematical optimization includes using technologies for analysis purposes so that organizations can solve issues concerning business processes and optimal use of present inputs and resources. Organizations can comprehend the attributes of real-life problems with the assistance of optimization models. Optimization techniques demand considering elements such as reasonable goals, decision variables, and constraints and finding an optimal solution that helps the organization achieve outcomes. Mathematical programming software is used to input the optimization equation and get the most practical and feasible solution (Nguyen et al., 2021).

What Are the Main Elements of Mathematical Optimization?

Mathematical optimization encompasses the following three broad elements namely decision variables, objective or goal functions, and constraints. Decision variables are organizational inputs that can be presented in quantitative or numerical form. Objective or goal function tells how an organization can examine the decision variables expressed in numbers and coefficients. For illustration, the objective function can help denote the profits or costs involved in the production and sale of products. It assists in the optimization of inputs or outputs in the form of minimization or maximization. It serves as a criterion for finding the most appropriate solution. Constraints are specific operational equalities and inequalities, such as changes in physical, technological, legal, and ethical forces. The decision variables get their quantitative values based on these forces. They help prevent the over-allocation of resources that are present within the organization (Llivisaca et al., 2021).

Why Can Mathematical Optimization Benefit the Organization?

Mathematical optimization technology is applied in various industries, such as the electric, finance, manufacturing, logistics, and government sectors. The broad benefits of using the mathematical optimization approach include cost reduction and revenue maximization. The implementation of mathematical optimization technology in the logistics industry can benefit a logistics organization in many ways. The organization can minimize the costs associated with transportation routes for inputs and final products. It can also solve practical issues related to business processes, such as disintegration in logistics functions and thus, it can help maximize profits (Vanderplaats, 1989).

How Can Mathematical Optimization Be Utilized?

A logistics organization uses mathematical optimization by deploying the latest available data to find solutions to logistics issues in real time (Snyman & Wilke, 2005). For example, historical organizational and industry data cannot solve pandemic-related matters. Therefore, one can use analysis approaches, convert the latest data into decision variables, and set objective functions to resolve disruptions in the processes of a logistics organization (Nguyen et al., 2021).

Conclusion

Discussing the mathematical optimization approach and how logistics organizations can benefit from and use it helps conclude that the organization can get the best solutions with this approach. Moreover, cost minimization and profit maximization, which are the main goals for any organization, can be achieved.

References

Darwish, M. A., & Duffuaa, S. O. (2010). A mathematical model for the joint determination of optimal

process and sampling plan parameters. Journal of Quality in Maintenance Engineering.

Llivisaca, J., Jadan-Avilés, D., Guamán, R., Arcentales-Carrion, R., Peña, M., & Siguenza-Guzman, L.

(2021, October). An Overview of Optimization Models and Technological Trends of Logistics in

the Retail Sector. In International Conference on Applied Technologies (pp. 484-496). Springer, 

Cham.

Nguyen, T. D., Nguyen-Quang, T., Venkatadri, U., Diallo, C., & Adams, M. (2021). Mathematical 

Programming Models for Fresh Fruit Supply Chain Optimization: A Review of the Literature and

Emerging Trends. AgriEngineering, 3(3), 519-541.

Snyman, J. A., & Wilke, D. N. (2005). Practical mathematical optimization (pp. 97-148). Springer

Science+ Business Media, Incorporated.

Vanderplaats, G. N. (1989). Methods of mathematical optimization. In Optimization: Methods and

Applications, Possibilities and Limitations (pp. 22-41). Springer, Berlin, Heidelberg.

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