Timeline and Process:
Description of the Special Issue:
Production scheduling and vehicle routing are two of the most studied fields in operations research. In the past four decades we have witnessed significant advances in both fields. However, the majority of existing research in both domains uses optimization based models and methodologies such as integer programming, dynamic programming and local search. In optimization, a problem is usually formulated into a mathematical model embedded with innate problem structures and characteristics. Such modeling and solution methods require the values of problem parameters to be available (i.e. in the form of either their deterministic values or their stochastic distributions) before the underlying mathematical models can be formulated and solved. However, real-life problems often involve a large amount of data which often contains a lot of uncertainty and changes over time. Optimization methods are often criticized for their inflexibility or ineffectiveness to deal with complex problems involving a large amount of data or a high degree of data uncertainty.
Analytic approaches, on the other hand. are entirely driven by data and often do not rely on rigid optimization models. Although such methods are more flexible than optimization methods, the resulting models and solutions have poor interpretability and may lack of insights that can be easily explained and understood by human users.
In the past several years, there has been growing research effort that attempts to bridge the gap between optimization and analytics, including methods that integrate optimization and machine learning.
This special issue aims to promote the use of this type of modeling and solution methods in production scheduling and vehicle routing. Specifically, we are seeking high quality scheduling and routing research papers that develop or apply integrated analytics and optimization methods that are not only flexible and robust under uncertainty, but can also generate models and solutions that are insightful and (relatively) easy to interpret. We are especially interested in papers that use one or more of the following modeling and solution methods: robust optimization, approximate dynamic programming, simulation optimization, stochastic programming, integer programming, and meta-heuristics, and their integration with data analytic tools such as optimal learning, machine learning, neural networks, and data mining. We are open to any interesting scheduling and routing applications including problems that arise in traditional areas such as production scheduling, vehicle routing, as well as applications from emerging areas such as supply chain scheduling, healthcare operations scheduling, routing with drones, ride sharing etc.
Ruibin Bai,University of Nottingham,Ningbo, Zhejiang Province, ChinaRuibin.BAI@nottingham.edu.cn
Zhi-Long Chen,University of Maryland,College Park, MD 20742, USA
Graham Kendall,University of Nottingham,UK & Malaysia
The Institute for Operations Research and the Management Sciences
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