A Review of Dynamic Job Shop Scheduling Techniques

Mohan, Jatoth; Lanka, Krishnanand; Rao, Abhishek

doi:10.1016/j.promfg.2019.02.006

Cited by 96 publications

(40 citation statements)

References 11 publications

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“…First, the MIP model is used to obtain an optimal schedule for production and transportation processes in terms of supply-chain costs, considering the system in a purely deterministic way. Subsequently, the simulation model is used to evaluate the performance of this obtained schedule in terms of the number of resulting delayed orders, where stochastic behavior is based on probability distributions [26,27,[29][30][31][32][33]. It belongs to the group of solutions to a given problem within a short period of time.…”

Section: Hybrid Optimization Scheduling Methodsmentioning

confidence: 99%

“…The purpose of applying a modified genetic algorithm was to minimize the makespan of the JSP. When applying a GA, the following parameters must be set: encoding method, chromosome length, population size, mating method, crossover method and rate, mutation method and rate, fitness method, selection mechanism, and termination condition [27,29,30].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

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Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

et al. 2020

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Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

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Section: Hybrid Optimization Scheduling Methodsmentioning

confidence: 99%

Section: Genetic Algorithmsmentioning

confidence: 99%

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

et al. 2020

View full text Add to dashboard Cite

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“…Naturally, more elaborated problems can be stated by changing the sources of production demand, type of machines, performance index, characteristics of a production environment, processing characteristic of the operation, kind of plan, and resource constraints [69], [70]. Despite seeming like an easy task, the standard version of the JSSP is considered to be NPcomplete [71]. This means there is currently no algorithm that can optimally solve all JSSPs in polynomial time, albeit several solution techniques have been proposed.…”

Section: ) Job Shop Schedulingmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

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“…The JSSP is already known as a typical NP-hard problem [ 29 ]. An n × m JSSP can be formally described as follows [ 30 ].…”

Section: Preliminariesmentioning

confidence: 99%

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

Tian¹,

Liu²,

Zhang³

et al. 2020

PLoS ONE

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A DNA (DeoxyriboNucleic Acid) algorithm is proposed to solve the job shop scheduling problem. An encoding scheme for the problem is developed and DNA computing operations are proposed for the algorithm. After an initial solution is constructed, all possible solutions are generated. DNA computing operations are then used to find an optimal schedule. The DNA algorithm is proved to have an O(n2) complexity and the length of the final strand of the optimal schedule is within appropriate range. Experiment with 58 benchmark instances show that the proposed DNA algorithm outperforms other comparative heuristics.

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A Review of Dynamic Job Shop Scheduling Techniques

Cited by 96 publications

References 11 publications

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

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