Hao Wen Lin

Rule driven multi objective dynamic scheduling by data envelopment analysis and reinforcement learning

This paper presents a rule driven method of developing composite dispatching rule for multi objective dynamic scheduling. Data envelopment analysis is adopted to select elementary dispatching rules, where each rule is justified as efficient for optimizing specific operational objectives of interest. The selected rules are subsequently combined into a single composite rule using the weighted aggregation manner. An intelligent agent is trained using reinforcement learning to acquire the scheduling knowledge of assigning the appropriate weighting values for building the composite rule to cope with the WIP fluctuation of a machine. Implementation of the proposed method in a two objective dynamic job shop scheduling problem is demonstrated and the results are satisfactory.

Cooperative Bayesian Optimization Algorithm: A Novel Approach to Simultaneous Multiple Resources Scheduling Problem

During the past several years, there has been a significant amount of research conducted simultaneous multiple resources scheduling problem (SMRSP) Intelligence manufacturing based on meta-heuristics, such as genetic algorithms (GAs), simulated annealing (SA) particle swarm optimization(PSO), has become a common tool to find satisfactory solutions within reasonable computational times in real settings. However, there are few researches considering interdependent relation during the decision activities, moreover for complex and large problems, local constraints and objectives from each managerial entity cannot be effectively represented in a single model for complex and large problems. In this paper, we propose a novel cooperative Bayesian optimization algorithm (COBOA) undertaking divide-and-conquer strategy and co-evolutionary framework. Considerable experiments are conducted and the results confirmed that COBOA outperforms recent researches for the scheduling problem in FMS.

Application of Negotiable Evolutionary Algorithm in flexible manufacturing planning and scheduling

A Negotiable Evolutionary Algorithm (NEA) meta-heuristic approach is presented in this paper for solving the Integrated Production Planning and Scheduling (IPPS) problem in Flexible Manufacturing Systems (FMS). The NEA approach addresses the rationales that we have identified as the key features required for effectively solving complex IPPS problems. Considerable experiments have been conducted and the results have confirmed that NEA outperforms the conventional EA approach.

A Wireless Sensor Network Topology Design Method Based on Negotiable Evolutionary Algorithm

In Wireless Sensor Networks (WSN), the sensor nodes and its central node are located some distance apart to ensure good coverage over the concerning area. However, the communication distance between sensor nodes is directly proportional to power consumption, and the ultimate effective point-to-point distance is nevertheless limited. to overcome this problem, a cluster based layout formation and an message routing algorithm among the head node of each cluster are suggested to ensure a WSN achieve good coverage, balance workload and traffic-load, and prolong the overall network lifetime. in this paper, we use a Negotiable Evolutionary Algorithm (NEA) to solve the complex multi-object WSN layout and signal routing problem. Experiment result shows that NEA is an effective approach for solving the problem.

Dispatching rule composition method for single machine multi objective dynamic scheduling

This paper presents a novel dispatching rule composition method based on Analytic Hierarchy Process for multi objective dynamic scheduling of single machines. By combining different elementary dispatching rules, the proposed method is capable of solving hetero-objective dynamic scheduling problems. Computer simulations were conducted to demonstrate the performance of the proposed method. Dynamic scheduling problems of various multi objective combinations were tested and the results were satisfactory. The features of the proposed method are discussed.