Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job      Shop Scheduling

Xu, Binzi; Mei, Yi; Wang, Yan; Ji, Zhicheng; Zhang, Mengjie

doi:10.1162/evco_a_00273

Cited by 34 publications

(23 citation statements)

References 67 publications

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“…The second most frequently considered objective is the tardiness, which measures the difference between the job due date and the job completion time. About 20% of the reviewed papers optimize some form of tardiness, namely: total tardiness (T) (e.g., [9,63,[102][103][104]), total weighted tardiness (wT) (e.g., [21,23,25,30,105]), total weighted tardiness and earliness (wTE) [59,69,97], tardiness cost (T cost ) [86,106], mean tardiness (T) [107], and maximum tardiness (T max ) [108,109].…”

Section: Other Objective Functionsmentioning

confidence: 99%

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

2022

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Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

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Section: Other Objective Functionsmentioning

confidence: 99%

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

2022

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“…MOP are common in the real world and many advanced methods have been proposed to solve MOPs in different domains [42,52]. For the maximum optimization problem, the problem is defined as follows:…”

Section: Definition Of Multi-objective Problemmentioning

confidence: 99%

A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

Geng

2022

Complex Intell. Syst.

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Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm (D$$^{2}$$ 2 CMOPSO). The sophistication of the D$$^{2}$$ 2 CMOPSO based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.

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“…As a hyper-heuristic method, GPHH has been applied to scheduling tasks. A GPHH program can be seen as a routing policy for routing problems [22], or a dispatching rule for job shop scheduling problems [23] for different decision environments. Weise et al [22] first proposed to apply GPHH for the automated design of routing policy for solving static CARP and tested the performance of the evolved rules for dealing with random disappearance of tasks.…”

Section: Approaches To Uncertain Carpmentioning

confidence: 99%

A New Cooperative Recourse Strategy for Emergency Material Allocation in Uncertain Environments

Liu

Wang

2022

Front. Phys.

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Emergency material allocation is an important issue in the urgent handling of public health emergencies. This article models the relief allocation and transportation route planning as an uncertain capacitated arc routing problem, which is a classic combinatorial optimization problem that considers stochastic factors such as uncertain demand and travel time in the service. The stochastic of demand leads to the route failure that the vehicle cannot serve the tasks successfully unexpected. Most existing research uses the independent recourse strategy. That is, each vehicle takes a back-and-forth trip separately when its remaining capacity cannot meet the actual demand of the task. This leads to a considerable recourse cost. However, a few studies have considered vehicular cooperation to deal with route failure, which is beneficial for pooling the capacity of multiple vehicles. In this paper, we propose a new recourse strategy called OneFAll that lets one vehicle take charge of all the failed tasks. In this case, other vehicles can finish the service once they are full. We develop the genetic programming hyper-heuristic with the OneFAll recourse strategy for solving the uncertain capacitated arc routing problem. The experimental studies show that our proposed method outperforms the existing genetic programming hyper-heuristic with the independent recourse strategy to the uncertain capacitated arc routing problem for the ugdb and uval benchmark instances. Moreover, our strategy outperforms the recourse strategy that failed tasks are returned to the unassigned task set for any vehicle to complete. This reflects that there exists resource waste if all vehicles are involved to repair the failed routes.

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Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Cited by 34 publications

References 67 publications

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

A New Cooperative Recourse Strategy for Emergency Material Allocation in Uncertain Environments

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