Moreover, significant shifts towards hybrid and hyper metaheuristics are also highlighted. with a comprehensive reviews on previously applied methods and their performance for solving different real-world problems in the domain of PHEVs. A summary on future perspective of metaheuristic algorithms is also provided, covering Cuckoo Search (CS), Harmony Search (HS), Artificial Bee Colony (ABC), etc. This work presents a holistic review of all substantial research applying metaheuritics optimization for plug-in-hybrid electric vehicles. Recent technical studies regarding various optimization strategies related to PHEV integrated smart grid such as control and battery charging, vehicle-to-grid (V2G), unit commitment, charging infrastructures, integration of solar and wind energy and demand management prove that electrification of transportation as a rapidly growing field of research. Smart grid success with combination of renewable energy exclusively depends upon the large-scale penetration of Plug-in Hybrid Electric Vehicles (PHEVs) for a sustainable and carbon-free transportation. This production planning and scheduling project may be considered as a first step towards the development of a different approach to the scheduling problem for the job-shop production system.Hybrid Vehicles have experienced major modifications since the last decade. Egbelu and Tanchoco (1984) that guide the response and movement of AGVs on shop floors when. The analysis of results showed a significant productivity increase in all the considered production planning and scheduling scenarios with the use of the proposed dispatching rule – DRP. Figure 2.5 Simulation model of shop floor in AnyLogic. Due to the complexity of such a scenario, a parametric simulation model that allows representation of many job-shop production systems was introduced. This work proposed the first decentralized scheduling and planning approach for a job-shop production system. Here is where the dynamic Dispatching Rule (DRP) takes place, for the score evaluation of the jobs in an Industry 4.0 semi-heterarchical job-shop scenario. The resources, during the “Evaluation and Acceptance” phase, assign a score to each proposal, choosing the one with the highest score. When all the proposal has been collected, the Resource switches from the “Wait reply” state to the “Evaluation Proposal and Acceptance” state. The Resource agent receives the Proposals from the Jobs and keeps them in a different proposal’s population inside the Resource agent. The job answer consists of a proposal represented by a Proposal agent. The Job agents receive the call, process the reply, and return their availability. Each job agent contains two additional populations, essential for the work of the Job Shop system: operations and transitions. Jobs are the objects that undergo machining to become, at the end of the technological cycle, finished products. Resources represent the entities responsible for processing the product (e.g. In the production planning and scheduling model, two populations of agents are represented as resources and jobs. Production planning and scheduling simulation modelĪ multi-method approach based on Discrete Event Simulation (DES) and Multi-Agent Systems (MAS) was used to develop the simulation model, using the AnyLogic 8.5.2 as simulation software. Then, the research team proposes a newly dispatching rule for the order admittance in a semi-heterarchical job-shop environment for the lowest machine level. This work aims to extend the decentralized scheduling and planning approach to a job-shop production system.ĭue to the complexity of a Job-Shop Scheduling Problem, the researchers firstly introduce a parametric simulation model to represent a generic job-shop system. In recent years, with the advent of Industry 4.0, the concepts of Cyber-Physical System and Internet of Things arise, allowing to shift from a classical hierarchical approach to the Manufacturing Planning and Control (MPC) system to a new class of more decentralized architecture.
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