Advanced Mathematical Methods for Scientists and Engineers

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.

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A survey on metaheuristics for stochastic combinatorial optimization

Mathematical programming models for supply chain production and transport planning

In recent years the unconstrained binary quadratic program (UBQP) has grown in importance in the field of combinatorial optimization due to its application potential and its computational challenge. Research on UBQP has generated a wide range of solution techniques for this basic model that encompasses a rich collection of problem types. In this paper we survey the literature on this important model, providing an overview of the applications and solution methods.

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The unconstrained binary quadratic programming problem: a survey

Simulation optimization for an emergency department healthcare unit in Kuwait

The main objective of this work is to develop an optimization model for the supply chain of a petrochemical company operating under uncertain operating and economic conditions. First, a deterministic model is developed and tested, and then uncertainties in key parameters are introduced. The proposed objective function is based on optimizing the system resources by minimizing the total production costs and raw material procurement, as well as lost demand, backlog, transportation, and storage penalization. A stochastic formulation is then developed, which is based on the two-stage problem method with a finite number of realizations. The optimization model is tested on a typical petrochemical company, manufacturing different grades of polyethylene, operating at a single site and using two reactors. Uncertainties are introduced in demands, market prices, raw material costs, and production yields. The main conclusion of this study is that uncertainties have a dramatic effect on the planning decisions of the pe...

Mohamed A. Ahmed

Papers

Simulation optimization for an emergency department healthcare unit in Kuwait

Optimizing the Supply Chain of a Petrochemical Company under Uncertain Operating and Economic Conditions

Simulation-based optimization using simulated annealing with ranking and selection

Simulated annealing for discrete optimization with estimation

Simulated annealing for the unconstrained quadratic pseudo-Boolean function