Energy- and Labor-Aware Production Scheduling for Industrial Demand Response Using Adaptive Multiobjective Memetic Algorithm

Gong, Xu; Liu, Ying; Lohse, Niels; Pessemier, Toon De; Martens, Luc; Joseph, Wout

doi:10.1109/tii.2018.2839645

Cited by 42 publications

(12 citation statements)

References 29 publications

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“…Thus a master synchronization is still required. The model in [8] optimizes the job scheduling and industry's cost through a local optimization at the industry level in the presence of DRP without taking into consideration the constraints resulting from applying the same at all customers and the capability of the utility to accept the aggregated feedback. In [9], the authors solve the problem of interaction between manufacturers' electricity and natural gas demands and energy providers through reaction to real-time supply notifications.…”

Section: A Industry Specific Process Modeling and Tasks Scheduling Optmentioning

confidence: 99%

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

et al. 2020

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Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction. INDEX TERMS Multiple demand response programs, MOPSO, group method of data handling (GMDH), Kmeans, clustering, smart grid, industrial customers.

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Section: A Industry Specific Process Modeling and Tasks Scheduling Optmentioning

confidence: 99%

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

et al. 2020

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“…MAs can tackle many complex optimization problems across a wide range of industrial application areas, such as assembly [22], vehicle routing [23], real-time production scheduling [24], and wireless sensor networks [25]. In all cases, MAs were shown to be more efficient and/or converge to better solutions compared to their purely-evolutionary counterparts.…”

Section: Memetic Algorithms (Mas)mentioning

confidence: 99%

TeMA: A Tensorial Memetic Algorithm for Many-Objective Parallel Disassembly Sequence Planning in Product Refurbishment

Pistolesi

Lazzerini

2019

IEEE Trans. Ind. Inf.

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The refurbishment market is rich in opportunities, the global refurbished smartphones market alone will be $38.9 billion by 2025. Refurbishing a product involves disassembling it to test the key parts and replacing those that are defective or worn. This restores the product to like-new conditions, so that it can be put on the market again at a lower price. Making this process quick and efficient is crucial. This paper presents a novel formulation of parallel disassembly problem that maximizes the degree of parallelism, the level of ergonomics, and how the workers' workload is balanced, while minimizing the disassembly time and the number of times the product has to be rotated. The problem is solved using the Tensorial Memetic Algorithm (TeMA), a novel two-stage many-objective (MaO) algorithm which encodes parallel disassembly plans by using third-order tensors. TeMA first splits the objectives into primary and secondary on the basis of a decision maker's preferences, and then finds Pareto-optimal compromises (seeds) of the primary objectives. In the second stage, TeMA performs a fine-grained local search that explores the objective space regions around the seeds, to improve the secondary objectives. TeMA was tested on two real-world refurbishment processes involving a smartphone and a washing machine. The experiments showed that, on average, TeMA is statistically more accurate than various efficient MaO algorithms in the decision maker's area of preference.

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“…To name but a few, GAs have been applied in the field of medicine [17], in bank lending [18] and in the optimization of industrial processes [19]. MAs have been applied to energy demand estimation [20] and to task planning [21], while MADS was applied to Bayesian optimization [22] and to the design of new materials [23].…”

Section: Introductionmentioning

confidence: 99%

Speed Control Optimization for Autonomous Vehicles with Metaheuristics

2020

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The development of speed controllers under execution in autonomous vehicles within their dynamic driving task (DDT) is a traditional research area from the point of view of control techniques. In this regard, Proportional – Integral – Derivative (PID) controllers are the most widely used in order to meet the requirements of cruise control. However, fine tuning of the parameters associated with this type of controller can be complex, especially if it is intended to optimize them and reduce their characteristic errors. The objective of the work described in this paper is to evaluate the capacity of several metaheuristics for the adjustment of the parameters Kp, 1/Ti, and 1/Td of a PID controller to regulate the speed of a vehicle. To do this, an adjustment error function has been established from a linear combination of classic estimators of the goodness of the controller, such as overshoot, settling time (ts), steady-state error (ess), and the number of changes of sign of the signal (d). The error obtained when applying the controller has also been compared to a computational model of the vehicle after estimating the parameters Kp, Ki, and Kd, both for a setpoint sequence used in the adjustment of the system parameters and for a sequence not used during the adjustment, and therefore unknown by the system. The main novelty of the paper is to propose a new global error function, a function that enables the use of heuristic optimization methods for PID tuning. This optimization has been carried out by using three methods: genetic algorithms (GA), memetics algorithms (MA), and mesh adaptive direct search (MADS). The results of the application of the optimization methods using the proposed metric show that the accuracy of the PID controller is improved, compared with the classical optimization based on classical methods like the integral absolute error (IAE) or similar metrics, reducing oscillatory behaviours as well as minimizing the analysed performance indexes.

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Energy- and Labor-Aware Production Scheduling for Industrial Demand Response Using Adaptive Multiobjective Memetic Algorithm

Cited by 42 publications

References 29 publications

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

TeMA: A Tensorial Memetic Algorithm for Many-Objective Parallel Disassembly Sequence Planning in Product Refurbishment

Speed Control Optimization for Autonomous Vehicles with Metaheuristics

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