Optimal Allocation and Scheduling of Inspection Operations under Multiple Risk Categories

Renee Clarke
Institution: New York City College of Technology/CUNY

Mentors: E.A. Elsayed, Minge Xie
Institution: Rutgers University

Research Partner: Darlena Kern
Institution: Pepperdine University


Project Description

"Containers arriving at ports may contain undesirable contents such as drugs, radioactive material or chemical and biochemical agents. Inspection of a fraction of these containers (randomly or based on some risk factor) might lead to an improved security system. Clearly, there is a probability that a container with undesirable contents might enter into the system without detection or a "clean" container might be subjected to a thorough inspection.... The objective of this project is to determine the optimum inspection strategy and container inspection sequence that minimize the occurrence of the above probabilities as well as the delay of releasing the container at the port. Approaches such as the development of adaptive threshold levels of the inspection sensors based on the assigned risk factor might prove to an efficient inspection strategy. The research investigates new and efficient inspection strategies."-From Proposed Projects page

We plan to build on the work of Dr. Christina Young introducing the element of earliness and using a genetic algorithm to optimize our function.


Weekly Updates



  1. I attended the orientation, did some literature review, met with one of our mentors and was given an overview of potential projects. After deciding on a project, I met with our grad student adviser, did some readings about open shop scheduling problems and worked on our first presentation.

  2. At the beginning of this week, Darlena and I presented our objective for the summer. During the rest of the week, I read Christina Young's dissertation, looked up branch and bound algorithms and genetic algorithm and worked on my website.

  3. I read some more about genetic algorithm and practiced implementing this in the optimization toolbar in Matlab using some simple functions. I also read about neural networks, met with our mentors twice. Darlena and I came up with our new objective function.

  4. We continued to work on the genetic algorithm. We did readings about problems that included a penalty for earliness and also various heuristics and meta- heuristics for solving scheduling.


  1. We read about bottle necking and started to work on an algorithm to use for the scheduling aspect of our problem.

  2. We worked on the final presentation and practiced this with our mentors. We also worked on our scheduling algorithm a bit more.

  3. We revised the final presentation and worked on the scheduling algorithm some more.

  4. This week was spent working on the report.