A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Fei, Hongying; Li, Qian; Shen, Dan

doi:10.1155/2017/7219656

Cited by 13 publications

(7 citation statements)

References 280 publications

(296 reference statements)

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“…The HEMS also plans the device consumption pattern based on the same information and user setting. The authors present a model of the HEMS in [23]. They suggested meta-heuristic methods for efficient energy consumption optimization accordingly to the RTP and IRB pricing signals.…”

Section: Problem Statementmentioning

confidence: 99%

“…If any of the antlions finds a better position than the elite, it will be replaced. After reaching the condition of stopping, the position of the elite antlion is selected as the final optimal response [23]. To model the interactions between ants and antlions, we first consider the ants to move in search space; then, the antlions are allowed to hunt.…”

Section: Basic Alo Algorithmmentioning

confidence: 99%

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A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

2020

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In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand. Energy consumption management is a set of strategies used to optimize energy consumption which includes a set of interconnected activities between the utility and customers to transfer the load from peak hours to off-peak hours. This reduces the electricity bill. This paper presents an optimal schedule for the consumption of residential appliances based on improved multi-objective antlion optimization algorithm to minimize the electrical cost and the user comfort. To prevent peaks, the peak-to-average ratio is considered as a constraint for the energy cost function. Also, two different tariff signals have been used to measure energy costs. The real-time pricing and critical peak pricing are considered as energy tariffs. The simulations results are compared with other meta-heuristic algorithms, including multi-objective particle swarm optimization, the second version of the non-dominated sorting genetic algorithm, and the basic antlion optimizer algorithm to show the superiority of the proposed algorithm. Final results show that using the proposed scheme reaches electricity bills less than 80%.

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Section: Problem Statementmentioning

confidence: 99%

Section: Basic Alo Algorithmmentioning

confidence: 99%

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

2020

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“…This procedure forms the exploitation capability of the algorithm. ABCs have been widely applied in combinatorial optimization, and more particularly scheduling problems [62], [63].…”

Section: Hybrid Artificial Bee Colonymentioning

confidence: 99%

Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

et al. 2020

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Setup time consists of all the activities that need to be completed before the production process takes place. The extant scheduling predominantly relies on simplistic methods, like the average value obtained from historical data, to estimate setup times. However, such methods are incapable of representing the real industry situation, especially when the setup time is subject to significant uncertainties. In this situation, the estimation error increases proportionally to the problem size. This study proposes a Random-Forest-based metaheuristic to minimize the makespan in an Unrelated Parallel Machines Scheduling Problem (UPMSP) with uncertain machine-dependent and job sequence-dependent setup times (MDJSDSTs). Taking the forging industry as an example, the numerical experiments show that the error percentage for the setup time estimation substantially decreases when the proposed approach is applied. This improvement is particularly significant when large-scale problems are sought. Overall, this study highlights the role of advanced analytics in bridging the gap between scheduling theory and practice. INDEX TERMS Scheduling, unrelated parallel machines, setup times, random-forest, metaheuristic.

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“…While heuristic optimization algorithms do not guarantee the exact solution and provide approximate solutions, however, they are capable of handling complex calculations. In spite of approximate solutions, optimization algorithms are fair enough to converge faster, reach the desired solution, and applicable in all fields of engineering and computer science [31]. For this purpose, we computed our problem as an optimization problem and four heuristic optimization techniques: WDO, HSA, GA, and GHSA are employed.…”

Section: Optimization Techniquesmentioning

confidence: 99%

“…Equation (31) generates the random values using parameters given in Table 6. The newly generated values are stored and named as harmony memory (HM).…”

Section: Maximum Iteration 100mentioning

confidence: 99%

An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid

Hussain¹,

et al. 2018

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Abstract:The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA) to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic algorithms: wind-driven optimization (WDO), harmony search algorithm (HSA), genetic algorithm (GA), and proposed algorithm GHSA. The proposed algorithm GHSA shows higher search efficiency and dynamic capability to attain optimal solutions as compared to existing algorithms. Simulation results also show that the proposed algorithm GHSA outperforms the existing algorithms in terms of reduction in electricity cost, PAR, and maximize user comfort.

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A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Cited by 13 publications

References 280 publications

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid

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