John Fowler

About

John Fowler is the Motorola Professor of Supply Chain Management in the W.P. Carey School of Business at Arizona State University. He has served as the department chair of supply chain management from 2011 to 2016 and previously held the position of Avnet Professor of Industrial Engineering at ASU. His research interests include discrete event simulation, deterministic scheduling, and multi-criteria decision making. Professor Fowler has published more than 120 journal articles and over 100 conference papers, contributing significantly to the fields of operations research, healthcare systems engineering, and semiconductor manufacturing systems. He has held editorial roles such as editor-in-chief for IIE Transactions on Healthcare Systems Engineering and continues as the department editor for Healthcare Operations Management, along with other editorial positions. He is a fellow of the Institute of Industrial and Systems Engineers and has served in leadership roles including vice president for continuing education, INFORMS vice president, and member of the Winter Simulation Conference board of directors. His academic background includes a Ph.D., M.S., and B.S. in Industrial Engineering from Texas A&M University.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Mathematical optimization
• Engineering
• Industrial engineering
• Distributed computing
• Operating system
• Risk analysis (engineering)
• Algorithm

Selected publications

• How simulation modelling can help reduce the impact of COVID-19

  Journal of Simulation · 2020 · 434 citations

  Modelling has been used extensively by all national governments and the World Health\nOrganisation in deciding on the best strategies to pursue in mitigating the effects of COVID19. Principally these have been epidemiological models aimed at understanding the spread of\nthe disease and the impacts of different interventions. But a global pandemic generates a large\nnumber of problems and questions, not just those related to disease transmission, and each\nrequires a different model to find the b…

  DOI

• A survey of scheduling with parallel batch (p-batch) processing

  European Journal of Operational Research · 2021 · 196 citations

  1st authorCorresponding

  Multiple jobs are processed simultaneously on a given batch processing machine in parallel batching. The resulting batch is called a p-batch. Batching can lead to reduced production costs, but depending how the jobs are grouped into a batch can lead to better or worse delivery times of products. Scheduling jobs on batch processing machines requires grouping decisions in addition to the conventional assignment and sequencing decisions. Parallel batching is important in such diverse areas such as…

  PublisherDOI

• Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap

  Journal of Industrial Information Integration · 2024-03-12 · 63 citations

  reviewOpen access

  Production Scheduling (PS) is an essential paradigm within supply and manufacturing systems and an important element of sustainable development. PS, mainly known for its horizontal effects within the operational decision level, directly impacts both tactical and strategical levels of decision-making. In other words, an optimally designed and utilized PS module could bring efficiency towards the whole supply chain network of many manufacturing systems. Simulation Optimization (SO), as a growing D…

  PublisherDOI

• Bi-criteria parallel batch machine scheduling to minimize total weighted tardiness and electricity cost

  Journal of Business Economics · 2020 · 35 citations

  Senior authorCorresponding

  Abstract A bi-criteria scheduling problem for parallel identical batch processing machines in semiconductor wafer fabrication facilities is studied. Only jobs belonging to the same family can be batched together. The performance measures are the total weighted tardiness and the electricity cost where a time-of-use (TOU) tariff is assumed. Unequal ready times of the jobs and non-identical job sizes are considered. A mixed integer linear program (MILP) is formulated. We analyze the special case wh…

  DOI

• Exact and heuristic algorithms for the parallel machine total completion time scheduling problem with dual resources, ready times, and sequence-dependent setup times

  Computers & Operations Research · 2022-03-17 · 24 citations

  articleSenior authorCorresponding
  PublisherDOI

Recent grants

• Collaborative Research: Optimization of the Design and Operation of Surgery Delivery Systems

  NSF · $120k · 2006–2010

Frequent coauthors

• Lars Mönch

  49 shared

• Scott J. Mason

  Amazon (United States)

  38 shared

• Michele E. Pfund

  38 shared

• Gerald T. Mackulak

  Arizona State University

  32 shared

• Esma S. Gel

  University of Nebraska–Lincoln

  26 shared

• Barry L. Nelson

  Northwestern University

  16 shared

• Özgür M. Araz

  University of Nebraska–Lincoln

  16 shared

• Dan Shunk

  CETYS Universidad

  16 shared

Education

• Ph.D.

  Texas A&M University

  1990

• M.S.

  Texas A&M University

  1986

• B.S.

  Texas A&M University

  1982

Awards & honors

• Fellow of the Institute of Industrial and Systems Engineers

Similar researchers at Arizona State University

• Dale Rogersh-28