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

Gao, Kaizhou; Cao, Zhiguang; Zhang, Le; Chen, Zhenghua; Han, Yuyan; Pan, Quan-Ke

doi:10.1109/jas.2019.1911540

Cited by 405 publications

(104 citation statements)

References 172 publications

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“…Due to the complexity of the objective function (16) and the large search space, we propose to use a heuristic optimization algorithm to determine a suitable solution. Such algorithms have shown to be suitable in such optimization problems, especially those involving large search space [34][35][36][37]. The efficiency and suitability of the various heuristic optimization algorithms in solving a whole range of complex problems have been receiving a lot of attention from academia for many years.…”

Section: Resultsmentioning

confidence: 99%

“…Desired occupancy of each vehicle: 70; • Possible departure time (in minutes) at the first terminal is defined by the time intervals for each service respectively: [16,30], [31,45] [13,24], [25,36], [37,48] [11,20], [21,30], [31,40], [41,50], [51,60]. Table 4 presents the combined results of the proposed method using MOPSO algorithm and the results from the literature.…”

Section: Methodsmentioning

confidence: 99%

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Optimization of Public Transport Services to Minimize Passengers’ Waiting Times and Maximize Vehicles’ Occupancy Ratios

2020

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Determining the best timetable for vehicles in a public transportation (PT) network is a complex problem, especially because it is just necessary to consider the requirements and satisfaction of passengers as the requirements of transportation companies. In this paper, a model of the PT timetabling problem which takes into consideration the passenger waiting time (PWT) at a station and the vehicle occupancy ratio (VOR) is proposed. The solution aims to minimize PWT and maximize VOR. Due to the large search space of the problem, we use a multiobjective particle swarm optimization (MOPSO) algorithm to arrive at the solution of the problem. The results of the proposed method are compared with similar results from the existing literature.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Optimization of Public Transport Services to Minimize Passengers’ Waiting Times and Maximize Vehicles’ Occupancy Ratios

2020

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“…Therefore, we will seek improvements in computational efficiency in future research. For example, some intelligent optimization methods [61][62][63] or cloud computing techniques [64] can be used to obtain solutions efficiently.…”

Section: Resultsmentioning

confidence: 99%

Designing a New Shuttle Service to Meet Large-Scale Instantaneous Peak Demands for Passenger Transportation in a Metropolitan Context: A Green, Low-Cost Mass Transport Option

Zheng¹,

Chen²,

Zhang³

et al. 2019

Sustainability

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Currently, the green, sustainable development of metropolises is hindered by problems caused by Large-scale Instantaneous Peak-demands for Passenger-transportation (LIPP), such as traffic congestion and air pollution. To mitigate these problems, we propose a new type of demand-responsive service as an alternative to inefficient "door-to-door" service. The proposed service is based on service units designed to aggregate passengers for shuttle service. To guarantee service quality and efficiency, a maximum passenger walking time constraint, a request rejection mechanism and a scheme for ensuring solution feasibility are considered. Through numerical experiments, we prove the following: (i) the proposed transport option exhibits better performance (by 40.37% for passengers and by 35.79% for operators) than the door-to-door transport option for solving real cases. (ii) By testing different datasets, we prove that the proposed service is more suitable for the request distributions that are spatiotemporally concentrated. (iii) Regarding the individual components of the proposed clustering-first, routing-second solution framework, the proposed soft clustering algorithm exhibits better performance than the classical hard clustering method (by 8%), and the proposed routing algorithm is 1.5 times more efficient than the commercial solution software GAMS. Sustainability 2019, 11, 5025 2 of 28This study is motivated by the desire to seek a new type of demand-responsive service, called large-scale instantaneous-peak-demand-responsive express shuttle service (LIPDRS), that can accommodate the LIPP of a metropolis, while balancing the benefits to operators and passengers. As an alternative to "door-to-door" transportation or frequency services, LIPDRS requires passengers to gather at certain locations, from which they are served by shuttles. Therefore, the LIDPRS design process involves first assigning passengers to certain service units and then transporting them along certain routes. LIPDRS has the advantages of a large capacity (ability to accommodate occasional massive demands), high flexibility (planning based on dynamic phone/Internet requests), low cost (affordability for passengers) and an acceptable level of convenience (passengers are required to gather at certain locations to be served in accordance with their requests). With these advantages, LIPDRS is an innovative means of increasing accessibility between high-circulation areas in a metropolis, such as residential districts and central business districts. As a complement to passenger transit systems, LIPDRS can help solve mobility and accessibility problems in a metropolitan context. In addition, this new transport option can encourage people to reduce their proportions of travel by private cars and thus reduce the cost of and pollution due to urban transportation, thereby improving the sustainability of metropolitan development.However, the realization of LIPDRS comes at the cost of additional complexity. Since passengers are required to gather at certain locatio...

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“…In addition, many methods are available to learn image semantics using feedbacks [10], such as Bayesian networks, neural networks, and so on. Based on tagged image datasets, Han and Gao [11,12] predicted the semantics of images with a correlation model, and associated visual features with keywords of images through machine learning, realizing multimodal retrieval of images.…”

Section: Literature Reviewmentioning

confidence: 99%

Query Rewriting and Semantic Annotation in Semantic-Based Image Retrieval under Heterogeneous Ontologies of Big Data

Jia¹,

Meng²,

Zhang³

et al. 2020

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In the era of big data, it is of great significance to retrieve the semantic features from images by big data technique. However, most semantic query models perform poorly in actual images, which are distributed heterogeneously. Image ontology mapping provides a solution to the problem. This paper applies the H-Match algorithm to find the mapping relationship between image ontologies in peer-to-peer (P2P) environment, and rewrite user queries for heterogeneous image ontologies. The H-Match algorithm was developed under the framework called Helios evolving interaction-based ontology knowledge sharing (Helios). The weights of semantic annotation were calculated by a novel method, involving word frequency, position and feedback. The research results have great application potentials in various fields.

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

Cited by 405 publications

References 172 publications

Optimization of Public Transport Services to Minimize Passengers’ Waiting Times and Maximize Vehicles’ Occupancy Ratios

Optimization of Public Transport Services to Minimize Passengers’ Waiting Times and Maximize Vehicles’ Occupancy Ratios

Designing a New Shuttle Service to Meet Large-Scale Instantaneous Peak Demands for Passenger Transportation in a Metropolitan Context: A Green, Low-Cost Mass Transport Option

Query Rewriting and Semantic Annotation in Semantic-Based Image Retrieval under Heterogeneous Ontologies of Big Data

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