Abstract
As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment.
Highlights
The use of evolutionary computational methods has been used in decision-making processes in production systems for many years
To transfer the optimization results of the Improved Heuristic Kalman Algorithm (IHKA) to the real-world real-world environment, the simulation modeling method in the commercially available environment, the simulation modeling method in the commercially available Simio softSimio software environment is presented the simulation model of benchmark dataset 6×5 ware environment is presented the simulation model of benchmark dataset 6 × 5 is shown is shown in inFigure communication protocol protocolwas was introduced when
The original Heuristic Kalman Algorithm (HKA) and the Genetic Algorithm-Mixed (GAM) [19] were selected as a comparison to prove the performance of the proposed IHKA to solve the Dynamic Job Shop Scheduling Problem (DJSSP)
Summary
The use of evolutionary computational methods has been used in decision-making processes in production systems for many years. Heuristic and metaheuristic methods enable obtaining satisfactorily good optimization results for a wide variety of NP-hard decision problems, where a decision problem H is NP-hard when for every problem L in NP, there is a polynomial-time many-one reduction from L to H. The paper presents a new metaheuristic method based on the Heuristic Kalman Algorithm [1] which enables optimal scheduling of Dynamic Job Shop Scheduling Problem (DJSSP), and the exchange and use of optimization results in a conventional simulation environment. Within the DJSSP optimization problem, thoroughly describes a real dynamic production system in which three dynamic events occur: The arrival of new orders, machine breakdowns and changes in operations’ process times. Unlike the job shop optimization problem, where orders are static and known in advance, it is necessary to change the scheduling of orders due to dynamic events in the DJSSP optimization problem dynamically and ensure optimal operation of the production system
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