An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem

Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

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Review of job shop scheduling research and its new perspectives under Industry 4.0

A research survey: review of flexible job shop scheduling techniques

Flexible job shop scheduling problems &#x0028 FJSP &#x0029 have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence &#x0028 SI &#x0029 and evolutionary algorithms &#x0028 EA &#x0029 are developed, employed and improved for solving them. More than 60&#x0025 of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm &#x0028 GA &#x0029 and one newest imperialist competitive algorithm &#x0028 ICA &#x0029 with variables neighborhood search &#x0028 VNS &#x0029 for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems

This paper focus on artificial intelligence approaches to NP-hard job shop scheduling (JSS) problem In the literature successful approaches of artificial intelligence techniques such as neural network, genetic algorithm, multi agent systems, simulating annealing, bee colony optimization, ant colony optimization, particle swarm algorithm, etc are presented as solution approaches to job shop scheduling problem These studies are surveyed and their successes are listed in this article

A research survey: review of AI solution strategies of job shop scheduling problem

Based on Genetic Algorithm (GA) and Grouping Genetic Algorithm (GGA), this research develops a scheduling algorithm for job shop scheduling problem with parallel machines and reentrant process. This algorithm consists of two major modules: machine selection module (MSM) and operation scheduling module (OSM). MSM helps an operation to select one of the parallel machines to process it. OSM is then used to arrange the sequences of all operations assigned to each machine. A real weapon production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of late delivery in military orders and high cost of equipment investment, total tardiness, total machine idle time and makespan are important performance measures used in this study. Based on the design of experiments, the parameters setting for GA and GGA are identified. Simulation results demonstrate that MSM and OSM respectively using GGA and GA outperform current methods used in practice.

Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm

Leprosy is a neglected infectious disease caused by acid-fast bacillus Mycobacterium leprae. It primarily affects the skin and then progresses to a secondary stage, causing peripheral neuropathy with potential long-term disability along with stigma. Leprosy patients account for a significant proportion of the global disease burden. Previous efforts to improve diagnostic and therapeutic techniques have focused on leprosy in adults, whereas childhood leprosy has been relatively neglected. This review aims to update the diagnostic and therapeutic recommendations for adult and childhood leprosy. This review summarizes the clinical, bacteriological, and immunological approaches used in the diagnosis of leprosy. As strategies for the diagnosis and management of leprosy continue to develop better and more advanced knowledge, control and prevention of leprosy are crucial.

/pdf/leprosy-a-review-of-epidemiology-clinical-diagnosis-and-2syftpmj.pdf

Leprosy: A Review of Epidemiology, Clinical Diagnosis, and Management

This research developed a capacity planning heuristic for a weapon production system (WPS) with characteristics of low order volume, high order variety, complex processes, uncertain order and frequent changes of orders. The heuristic capacity planning algorithm allocates orders to resources, determines appropriate order release time to the factory, and estimates the expected loading of all machines. With these, plant managers can take actions to manage capacity shortage of bottleneck machines and make necessary change of order planning. The proposed algorithm was programmed in Microsoft Visual Basic, and real data from a weapon production factory were used to evaluate the effectiveness and efficiency of this algorithm. Comparing the performance of results, it reveals that the proposed capacity planning heuristics outperforms current manual capacity planning policy used in practice.

A study of a heuristic capacity planning algorithm for weapon production system

https://doi.org/10.1097/md.0000000000031570

Prevalence and risk factors associated with poor sleep among firefighters in Taiwan: An observational study

: This paper is a response to the paper by De, Biswas, and Roy that was published in Fuzzy Sets and Systems, 2001, 209-213. They considered an application of intuitionistic fuzzy sets in medical diagnosis. We point out that their approach contains questionable results that may lead to false diagnoses of patients’ symptoms. Consequently, we advise researchers and physicians not to apply their approach to avoid risk to human life. Based on the similarity of pattern recognition, we provide a new approach to recognize the pattern of patients that will help physicians to determine the preliminary check for further laboratory examination in medical diagnosis.

Kou-Huang Chen

Papers

Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm

Leprosy: A Review of Epidemiology, Clinical Diagnosis, and Management

A study of a heuristic capacity planning algorithm for weapon production system

Prevalence and risk factors associated with poor sleep among firefighters in Taiwan: An observational study

Medical Diagnosis Based On Pattern Recognition