Simulation and Mathematical Programming Based Optimization
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 1818
Special Issue Editor
Special Issue Information
Dear Colleagues,
Numerous industries, including manufacturing, transportation, and mining, have reaped substantial benefits from implementing various optimization strategies in their daily operations over the past several decades. mathematical programming techniques have always received special consideration in making optimal decisions for discrete processes. Most approaches to mathematical programming, such as linear programming and integer programming, are constrained by the number of decision variables and constraints. To create a manageable mathematical model, it is necessary to make simplifying assumptions. Researchers from around the globe have been developing new strategies to circumvent this limitation. This Special Issue seeks to compile original research papers that present the most recent advancements and applications of mathematical programming techniques in various fields. In addition, the aforementioned optimization methods have limitations such as stochastic or unknown input parameters in the real world, which may result in suboptimal or infeasible solutions. In these instances, optimization and simulation can be combined. This strategy involves running a deterministic optimization model and a stochastic simulation model in alternation. In the simulation-based optimization method, optimization serves as the search technique that identifies the alternative space of a simulation model in order to locate solutions that contribute to the desired system performance(s). I also intend to collect original research papers on the most recent advancements and applications of simulation-based optimization methods in this Special Issue.
Best,
Dr. Masoud Soleymani Shishvan
Guest Editor
Manuscript Submission Information
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Keywords
- mathematical programming
- optimization
- simulation-based optimization
- discrete process simulation modeling
- discrete processes
- linear programming (LP)
- integer programming (IP)
- mix-integer linear programming (MIP)
- goal programming
- multiple-criteria decision-making methods
- intelligent decision support systems
- optimization techniques
- production planning
- scheduling
- resource allocation
- supply chain management
- production management
- risk management
- decision analysis for sustainable production
- group decision making
- decision making
- hybrid decision-making analysis
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