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Modeling of the methods for providing improvements in business processes by value adding is researched with an integrated approach. The main purpose in this approach is improving the processes by determining and analyzing the weak points and reducing the weakness degrees.

The designed model determines weak points that need to be improved, analyzes them to find privileged processes in improvement by considering improvement costs and obtains the improvement degrees for defining the improvement strategy by four‐phase business process improvement framework: start‐up, self analysis, defining improvement strategy for making changes, feedback, and continuous improvement.

The key factor in keeping up with changes in market conditions is systematic application of improvement efforts and providing planned and controlled value addition with these improvements. Decision problems in process improvement can be structured to provide input data suitable for multi‐criteria decision making techniques and results meet the solution expectations.

The reason for using analytical hierarchy process, goal programming, and linear programming model is to select the process and determine its improvement degree. Solving the decision problems by these techniques is a time consuming process, so forming suitable programs with decision support systems will be useful.

The theoretical structure of the modeled techniques in this study was examined with an industrial application. The application process and the results suitability were determined.

The proposed model shows improvement amounts according to the amount of defined importance degrees. It presents an advantage to decision makers by giving concrete improvement values from model results determining their improvement strategies.

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.