An Academic Perspective: Closed-Loop Supply Chain Network Design

An Academic Perspective:

Closed-Loop Supply Chain Network Design

By Jemel Derbali, Wise Systems Co-Founder

Exciting news here at Wise Systems! One of our team members, Dr. Ali Haddad Sisakht, an Operations Research Scientist here at Wise, has published a journal article entitled Closed-loop Supply Chain Network Design in the International Journal of Production Economics. Dr. Sisakht graduated from Iowa State University with a PhD in Industrial Engineering and Operations Research, and has extensive experience in computer science.

This paper, which is part of Dr. Sisakht’s PhD dissertation, looks at the complexities of selecting, locating and operating different types of facilities and the related considerations.

He focuses on three key decisions in supply chain network design:

  1. Where to establish facilities such as plants, warehouses, or collections in order to minimize costs;

  2. What type of trucks or transportation modes should be used; and

  3. How many packages should be carried in order to satisfy customers while minimizing overall cost.

Additionally, his model considers uncertainty of demand and the significance of carbon taxes in supply chain network design.

“My interest is in designing and developing models for different supply chain problems,” said Dr. Sisakht, about this paper. “I have numerous papers related to optimization and design algorithms to solve supply chain problems. At Wise Systems, as in my research, I enjoy the challenges in designing and developing different algorithms in last-mile deliveries -- one of the most significant problems in supply chain.”

Here’s the article abstract:

We optimize the design of a closed-loop supply chain network that encompasses flows in both forward and reverse directions and is subject to uncertainty in demands for both new and returned products. The model also accommodates a carbon tax with tax rate uncertainty. The proposed model is a three-stage hybrid robust/stochastic program that combines probabilistic scenarios for the demands and return quantities with uncertainty sets for the carbon tax rates. The first stage decisions are facility investments, the second stage concerns the plan for distributing new and collecting returned products after realization of demands and returns, and the numbers of transportation units of various modes are the third stage decisions. The second- and third-stage decisions may adjust to the realization of the carbon tax rate. For computational tractability, we restrict them to be affine functions of the carbon tax rate. Benders cuts are generated using recent duality developments for robust linear programs. Computational results show that adjusting product flows to the tax rate provides negligible benefit, but the ability to adjust transportation mode capacities can substitute for building additional facilities as a way to respond to carbon tax uncertainty. -- To view the full article, click here.

Congratulations to Dr. Sisakht, making the logistics industry a little wiser!