Scott Mason, an expert in microelectronics manufacturing, has always been a man with a plan. "I've known since I was 14 that I wanted to teach college and play golf," he said. "And that's exactly what I'm doing."

That is when he's not in class, in a lab or in his office carrying out administrative duties as associate head of the industrial engineering department. He came to the University of Arkansas after several years in the private sector.

"It's fulfilling to teach, to help a struggling student understand something and finally get it," he said. "I also love to solve problems, which is what research means to me."

One of Mason's research projects involves finding innovative solutions to a classic logistics problem: how to deliver many different products to many different groups with many different specifications using limited transportation resources. The basic problem is the same whether it involves consumer goods, military troops and supplies or the next generation of semiconductor manufacturing.

Let's work backward to explain this version of multiple-order job scheduling: cell phones, computers and other electronic devices continue to get smaller. As they shrink, so do the size of the chips that power them. As the chips become smaller, more can be fabricated on a wafer. Generally, those silicon wafers are eight inches in diameter. But companies are now moving to 12-inch wafers -- the bigger the wafer, the cheaper the per-chip cost.

"Say a manufacturer has 25 wafers in a lot with 1,000 chips on each wafer, making 25,000 chips in a single lot," said Mason. One client may have ordered 10,000 chips in that lot, with another three clients wanting 5,000 chips each. Each client probably has different requested delivery dates, different priorities and other potentially different specifications. In addition, semiconductor manufacturers transport the valuable, heavy wafers in packages called Front Opening Unified Pods or FOUPs. "If those FOUPs are less than full, that wasted space costs money," he said.

So Mason, along with three graduate students and colleagues at other institutions, is developing both mathematical programming and heuristic solution approaches, building upon previous research results to develop innovative ways to examine larger, more complex versions of the multiple-order job scheduling problem. The team is starting with a simple, single machine version of the problem. Their goal is to extend it much broader, to the U.S. military's entire supply chain, for example.

"I enjoy focusing my efforts on investigating practical problems rather than purely theoretical problems," he said. "That's why I became an engineer. To solve problems."