Analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times

Stochastic dynamic job shop scheduling pro- blem with consideration of sequence-dependent setup times are among the most difficult classes of scheduling problems. This paper assesses the performance of nine dispatching rules in such shop from makespan, mean flow time, maximum flow time, mean tardiness, maximum tardiness, number of tardy jobs, total setups and mean setup time performance measures viewpoint. A discrete event simulation model of a stochastic dynamic job shop manufacturing system is developed for investigation purpose. Nine dispatching rules identified from literature are incorporated in the simulation model. The simulation experiments are conducted under due date tightness factor of 3, shop utilization percentage of 90 % and setup times less than processing times. Results indicate that shortest setup time （SIMSET） rule provides the best performance for mean flow time and number of tardy jobs measures. The job with similar setup and modified earliest due date （JMEDD） rule provides the best performance for make- span, maximum flow time, mean tardiness, maximum tardiness, total setups and mean setup time measures.

Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a real project and production environment.To solve MS-RCPSP,it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme.This paper proposes an improved gene expression programming(IGEP)approach to explore newly dispatching rules that can broadly solve MS-RCPSP.A new backward traversal decoding mechanism,and several neighborhood operators are applied in IGEP.The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process,and improves the algorithm’s performance.Several neighborhood operators improve the exploration of the potential search space.The experiment takes the intelligent multi-objective project scheduling environment(iMOPSE)benchmark dataset as the training set and testing set of IGEP.Ten newly dispatching rules are discovered and extracted by IGEP,and eight out of ten are superior to other typical dispatching rules.

Heuristic Method for Job Shop Scheduling Problem with Combination Processing

A job shop scheduling problem with a combination processing in complex production environment is proposed. Based on the defining of ＂non-elastic combination processing relativity＂ and ＂virtual process＂, the problem can be simplified and transformed to a traditional one. On the basis of the dispatching rules select engine and considered factors of complex production environment, a heuristic method is designed. The algorithm has been applied to a mould enterprise in Shenzhen for half a year. The practice showed that by using the method suggested the number of delayed orders was decreased about 20% and the productivity was increased by 10 to 20%.

HBD:Towards Efficient Reactive Rule Dispatching in Software-Defined Networks

Most types of Software-Defined Networking （SDN） architectures employ reactive rule dispatching to enhance real-time network control. The rule dispatcher, as one of the key components of the network controller, generates and dispatches the cache rules with response for the packet-in messages from the forwarding devices. It is important not only for ensuring semantic integrity between the control plane and the data plane, but also for preserving the performance and efficiency of the forwarding devices. In theory, generating the optimal cache rules on demands is a knotty problem due to its high theoretical complexity. In practice, however, the characteristics lying in real-life traffic and rule sets demonstrate that temporal and spacial localities can be leveraged by the rule dispatcher to significantly reduce computational overhead. In this paper, we take a deep-dive into the reactive rule dispatching problem through modeling and complexity analysis, and then we propose a set of algorithms named Hierarchy-Based Dispatching （HBD）, which exploits the nesting hierarchy of rules to simplify the theoretical model of the problem, and trade the strict coverage optimality off for a more practical but still superior rule generation result. Experimental result shows that HBD achieves performance gain in terms of rule cache capability and rule storage efficiency against the existing approaches.

Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

For increasing the overall performance of modem manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling （IPPS） problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS （MOIPPS） problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatch- ing rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

Support optimal scheduling with weighted random forest for operation resources

Operation-related resources are lots of manpower and material with the characteristics of high cost and high income in hospitals,and scheduling optimization is a very important research issue in medical service.In this paper,to cope with the actualities of operation resources scheduling,such as poor planning,lack of standardized scheduling rules,chaotic use of the operating rooms,and many human interference factors,we propose a systematic approach to optimize scheduling problems based on multiple characteristics of operating resources.We frst design a framework that includes the composite dispatching rules(CDR),optimization ideology,and feedback mechanism,in which the CDR integrates fexible operating time,hold-up time of medical facilities,available time of medical staf,and multiple constraints.The optimization ideology is carried out through a learning model based on the weighted random forest(WRF)algorithm.The feedback mechanism enables the approach to realize closed-loop optimizations adaptively.Finally,the superiority of the systematic scheduling approach(SSA)is analyzed through numerical experiments on a simulation platform.Results of the simulation experiments show that the proposed scheduling method can improve performances signifcantly,especially in the waiting time of patients.