Datadriven oriented optimization of resource allocation in the forging process using Biobjective Evolutionary Algorithm Abstract   Resource allocation in the forging process of the steel production industry is an important element of the material supply in upstream processes, and the subsequent processing of semifinished products downstream. However, the existing literature rarely discusses issues related to this problem. In this study, the information flow is built by referring to specifications and process logic, which is a kind of preprocessing in data analysis flow in order to break the initial barrier of resource allocation in forging. In addition, Biobjective Evolutionary Algorithm (BOEA) is proposed to optimize the resource allocation in the forging process. Using the built information flow, the available multiple forging process resources can be effectively connected, and information of available resource combinations can be established for orders. Since real users have preferences for different objectives in practice, experiment results show that the proposed BOEA can deal with these preferences by effectively optimizing both the remnants (the remaining materials) and the execution cost, and the profit contribution is also proved by effective cost savings.

Simulation Based Capacity Optimization of a General Assembly Line with Extremely Unbalanced Station Process Time A general assembly shop in an aerospace industry has extremely unbalanced workstation time and several reentry processes. The capacity allocation of bufferlike workstations has been identified as a crucial factor of production improvement. Experience based whatif analyses based on simulation of feasible alternatives are timeconsuming and cannot get the optimization solutions. In this study, a data driven simulationbased optimization framework is presented and used to redesign the capacity of bufferlike workstations in the line. The assembly processes with distinct characteristics are introduced and a data driven method is adopted to build the different structures of the simulation model. An optimization problem with constraints is formulated and solved based on genetic algorithm, which uses selfadoptive refusal and penalty function strategy to improve the performance. The case study of the real system is carried out and the results show that the data driven simulationbased optimization is efficient to the problem.