Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem

Abstract

Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem. 25(6) Robotics and AbstractMany real-world manufacturing problems are too complex to be modelled analytically. For these problems, simulation can be a powerful tool for system analysis and optimisation. While traditional optimisation methods have been unable to cope with the complexities of many problems approached by simulation, evolutionary algorithms have proven to be highly useful. This paper describes how simulation and evolutionary algorithms have been combined to improve a manufacturing cell at Volvo Aero in Sweden. This cell produces hightechnology engine components for civilian and military airplanes, and also for space rockets. Results from the study show that by using simulation and evolutionary algorithms, it is possible to increase the overall utilisation of the cell and at the same time decrease the number of overdue components.

Selection guide of multi-objective optimization for ergonomic design

“…The evolutionary methods can be easily applied to the design problem without setting parameters regarding design concepts and have been developing rapidly (Mobin et al , 2018) through the application in industry (Syberfeldt et al , 2009). However, they are difficult to deal with the four types of the relationships mentioned above (Figure 1), whereas the scalarization methods can derive the pareto optimal solution according to the four types using the quantitative weight, target value, constraint value, etc.…”

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

“…The optimization algorithm then compares the performance of the system with the performance produced by previous permutations of the parameters in order to generate a new set of parameter values. This process continues until a stop criterion has been met, such as performing a defined number of evaluations, elapsing a specific amount of time or any userspecified criterion (Syberfeldt 2009). The framework of the SBO approach in this study is shown in Figure 1.…”

Scalable modeling and solution of stochastic multiobjective optimization problems

“…One special case of SMOO is deterministic multiobjective optimization (MOO), for which general formulations and computational frameworks are well-established [35,41,22]. SMOO formulations have a a wide range of applications that include finance [2], energy systems [9], chemical processes [43,37,31], transportation logistics [21], facility location [16,14], manufacturing and production planning [42], supply chain management [40], telecommunication, health care management [3,5], budget allocation [23], and project management [20]. SMOO problems present several interesting technical challenges.…”