Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for product...
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Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for production planning and decision-making process have emerged in that resource scheduling and have gained the most attention, and there is an urgent need to determine the current status and identify issues and matters to be addressed in the future. This review paper is aiming to discuss aspects of the cloud-based resource scheduling problem through investigating the literature to date to identify the existing gaps and recommending the potential paths moving forward for researchers in this field. So far, literature reviews focused on a broad scope of cloud-based scheduling, as a new approach taking a "narrow scope" by focusing on resource scheduling and various steps of it in the cloud environment are considered. Using the data gathered from the popular databases, a comprehensive statistical analysis on the existing literatures is provided, and the rational sequences of the systematic literature review (SLR) are elaborated. The mathematical models in resource scheduling are thoroughly elucidated. Then, a comprehensive analysis of the main aspects of resource scheduling including the objective functions, constraints, and optimization algorithms is presented. Discussion of the findings of the review paper illustrates that time and cost gain more attention (almost 80%) among all objective functions, and the metaheuristic algorithms are the most widely used in the recent research papers. Finally, suggestions for potential future research to further consolidate this field have been enumerated.
Economic dispatch of a multi-area interconnected power system with wind, solar, and energy storage units is a typical non-convex and nonlinear optimization problem, which is difficult to be solved by the existing math...
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Accurately categorizing communities within a social network is a crucial aspect of community detection, carrying significant practical relevance. To achieve a higher quality of community division, we combine reinforce...
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In this paper, we exploit necessary/sufficient optimality conditions for Ε-quasi positively properly efficient solutions of the semi-infinite multiobjective optimization problems with data uncertainty. We also consid...
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Unbalanced power systems cause transformers and generators to overheat, system losses to climb, and protective devices to trigger. An optimization-based control technique for distributed generators (DG) balances deman...
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Unbalanced power systems cause transformers and generators to overheat, system losses to climb, and protective devices to trigger. An optimization-based control technique for distributed generators (DG) balances demand and improves power quality in three imbalanced distribution systems with 10, 13, and 37 nodes. Each system phase has its own DG. Particle Swarm optimization (PSO) and Dynamic Arithmetic optimization Algorithm (DAOA) determine each phase's best locations, sizes, and power factors. The PSO and DAOA algorithms optimize the three imbalanced distribution systems at full load and throughout the day. The three DG sources are at the same node for easy operation, maintenance, and control. Each system's voltage, power, and current imbalance factors (VUF, PUF, CUF) are determined according to ANSI and IEEE standards. optimization techniques lower VUF to meet the criteria for all studied systems. PUF values drop from 116%, 28%, and 17% to virtually zero for the 10-, 13-, and 37-bus systems, while CUF improves similarly. Power losses are minimized by 80%, 51%, and 52% for each system. The voltage profile improves, reducing voltage variance across all three systems.
Recently, quantum computing has emerged as a new paradigm that promises to improve artificial intelligence techniques. One of the research fields that is certainly benefiting from this new computational paradigm is ev...
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Since the original TSA has problems such as easy to fall into local optimum and limited population diversity, this study proposes an optimized Tree-Seed Algorithm(TSA), which significantly improves the search efficien...
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The Artificial Bee Colony (ABC) algorithm is a simple and efficient optimization method with few control parameters and robust global search capabilities. It performs well in solving complex optimization problems but ...
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This paper focuses on the application of optimisation algorithms for intelligent optimisation decisions in production processes. Focusing on adaptive sampling methods, 0-1 planning models and genetic algorithms constr...
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This paper provides a comprehensive investigation into computational models based on dynamics and discrete global optimization algorithms. It explores the theoretical foundations and practical applications of these mo...
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