Special issue: M&S optimization applications in industry and engineering

Modeling and simulation (M&S) optimization applications in industry and engineering are as old as M&S itself, but the growth in computational power has allowed the description and resolution of complex issues using heuristic optimizations and M&S outside computational laboratories and in direct support of the workforce. This resulted in recent advancements in methodologies and frameworks of heuristic optimizations in industrial environments which have increased the spectrum of design and analysis tools to study complex engineering and business systems. In numerous business and engineering applications, optimization or M&S tools have been used independently to face and generate solutions and understanding of these applications. Further, many simulation-based optimization approaches have been designed and implemented to adequately estimate feasible solutions to major intricate problems that require capable tools that capture and process the inherent complexities associated with complex issues. The industrial application domain that typically considers this type of issue can be described as large, ill-defined, and multifaceted problems whose analysis entails the utilization of capable methods able to characterize and process the large and often conflicting or absent information. To maintain a competitive edge given the ever-changing nature of customer demands and interdependent restrictions in operational and financial environments in the industrial setting, is necessary to understand the fundamentals, applicability, and extent of collectively using heuristic optimizations and M&S. This special issue focuses on highlighting the academic foundations as well as real-world industrial applications and generalizability of proposed methods and lessons learned from implemented solutions. Thus, simulationbased optimization may be perceived as new domain that considers advanced quantitative methods that combine the ability of simulation models of characterizing and processing complex models while being able to seek solutions that satisfy intricate constraints. The papers selected for this special issue range from reporting innovative approaches that employ M&S and optimization heuristics as core technology in advancing knowledge in specific domains to proposing novel approaches or enhancements to a domain characterized by the closer integrative use of both heuristic optimization and M&S. The overwhelming response we received in the form of more than 50 submissions clearly shows the interest of the community in this topic. Most business and engineering applications papers presented here are focused on solving complex issues in the manufacturing and service industry. Advances in technology that combine both optimization and M&S papers explore novel techniques that contribute to the current body of knowledge. Otamendi presents a simulation–optimization approach based on using a multicriteria process capability index and evolutionary algorithms. The author investigates the development of periodic schedule problem at a ship building factory and employs simulation modeling to represent the stochastic behavior of the input data. The simulation model is employed to properly characterize the complex operations performed by the real system. Farughi et al. examine the flexible job shop scheduling problem (FJSP). The authors suggest an innovative memetic algorithm (MA) approach for solving the FJSP with overlapping operations. They propose a technique that uses the critical path method (CPM) to enhance the output of the MA while reducing the objective function. The results from this paper suggest that the proposed approach is able to achieve optimal solutions for small size problems and near-optimal solutions for medium to large size problems. Laslo and Gurevich consider time–cost tradeoffs under uncertainty for PERT-type projects and develop a new stochastic procedure to address failures in meeting on-time and on-budget objectives. This paper suggests establishing objective functions to minimize the project budget or any chance-constrained project cost, subject to any chance-