Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing

He, Wei; Jia, Guozhu; Zong, Hengshan; Ji-li, Kong

doi:10.3390/su11092619

Cited by 11 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, our study only focuses on the service selection and scheduling problem. [26]. Our study is different from their studies, which only address carbon emissions in manufacturing activities, because we further take into account carbon emissions in transportation activities between cloud manufacturing services, namely carbon footprint from manufacturing to transportation.…”

Section: Problem Descriptionmentioning

confidence: 99%

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Yang

Qi-dong

et al. 2020

Sustainability

View full text Add to dashboard Cite

Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.

show abstract

Section: Problem Descriptionmentioning

confidence: 99%

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Yang

Qi-dong

et al. 2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…Hybrid GA Matlab Rnd C C Max [62] CNBA N/A Rnd C N-C N-C [16] Fuzzy Set N/A N/A C C Max [7] Empirical Knowledge GA C# Collect C C Max [63] GA N/A Rnd C C Max [17] Branch-And-Cut Gams Rnd C C Max [64] GSA [66] Bat Algorithm N/A Rnd C C Min [67] Hybrid ICA Matlab Rnd C C Min [68] BBO C# Real C C Max [69] Hybrid PSO (PSO_SA) Matlab Rnd C N-C N-C [70] SSO Ensemble Python Rnd C C N-C [71] Graph-based N/A SG C C Max [72] GA N/A Rnd C C Max [14] IHDETBO Matlab Rnd C C Max [73] Improved SFLA Visual Studio Rnd C C Max [74] Hybrid GWO Matlab SG C C Max [75] GA Matlab Rnd C C Max [76] Improved GA based on entropy Matlab Rnd C C Max [77] Improved GWO Matlab Rnd C C Max [78] GA…”

Section: Additional Details Extracted From the Primary Studiesmentioning

confidence: 99%

Service Composition and Optimal Selection in Cloud Manufacturing: State-of-the-Art and Research Challenges

et al. 2020

View full text Add to dashboard Cite

“…However, considering its low computation efficiency, some researchers simplified the original optimization problem. Chen and Tsai [29] proposed that objectives with high priority have high satisfactions. According to their ideas, the priority order could be expressed as:…”

Section: Of 24mentioning

confidence: 99%

“…Therefore, considering preference information in the model or algorithm becomes a new research direction, which can provide convenience for DM to choose satisfactory solutions. So, in our previous paper [29], linguistic preference is taken into account, and a two-phase method based on a desirable satisfying degree is proposed. However, in the real environment, preference information will not always be given in terms of linguistic terms; sometimes, it may be expressed as objective priorities, i.e., the order in which only after some objectives have been achieved, other can be considered.…”

mentioning

confidence: 99%

Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities

Jia

Zong³

et al. 2019

Sustainability

View full text Add to dashboard Cite

In recent years, with the support of new information technology and national policies, cloud manufacturing (CMfg) has developed rapidly in China. About CMfg, scholars have conducted extensive and in-depth research, among which multi-objective service selection and scheduling (SSS) attracts increasing attention. Generally, the objectives of the SSS problem involve several aspects, such as time, cost, environment and quality. In order to select an optimal solution, the preference of a decision maker (DM) becomes key information. As one kind of typical preference information, objective priorities are less considered in current studies. So, in this paper, a multi-objective model is first constructed for the SSS with different objective priorities. Then, a two-phase method based on the order of priority satisfaction (TP-OPS) is designed to solve this problem. Finally, computational experiments are conducted for problems with different services and tasks/subtasks, as well as different preference information. The results show that the proposed TP-OPS method can achieve a balance between the maximum comprehensive satisfaction and satisfaction differences, which is conducive to the sustainable development of CMfg. In addition, the proposed method allows the preference information to be gradually clarified, which has the advantage of providing convenience to DM.Sustainability 2019, 11, 4767 2 of 24 common feature of these models is that multiple factors need to be considered in the objectives, for example, time [14][15][16], cost [14][15][16][17], reliability [14-16] and energy consumption [14,[17][18][19], risk [17], service [20], trust [21], availability [22], value [23], reputation [19,24] and workload [25]. Some scholars integrate these factors into a single objective through weight values and design effective algorithms, such as the: parallel method [8], workload-based method [9], two-level method [10], cooperative method [26], chaos control optimal algorithm [16] and improved niche immune algorithm [27]. Some focus on the game theory models [11,12]. Some others pay attention to multi-objective models and algorithms; for instance, Pareto group leader algorithm [14], cloud-entropy enhanced genetic algorithm [23], hybrid artificial bee colony algorithm [18,19], adaptive multi-population differential artificial bee colony algorithm [24], modified particle swarm optimization algorithm [25] and ε-dominance multi-objective evolutionary algorithm [28].In the above studies, novel objective functions attract more attention, and DM's preference for different objectives is rarely considered or simply expressed as a set of weight values [8,17]. In the decision-making process, preference information is critical due to the fact that only one solution needs to be selected as the final scheme. Therefore, considering preference information in the model or algorithm becomes a new research direction, which can provide convenience for DM to choose satisfactory solutions. So, in our previous paper [29], linguistic preference is taken into...

show abstract

Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing

Cited by 11 publications

References 30 publications

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Service Composition and Optimal Selection in Cloud Manufacturing: State-of-the-Art and Research Challenges

Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities

Contact Info

Product

Resources

About