A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance

Liu, Ying; Dong, Haibo; Lohse, Niels; Petrović, Sanja

doi:10.1016/j.ijpe.2016.06.019

Cited by 117 publications

(68 citation statements)

References 25 publications

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“…The electricity cost was minimized considering the trade-off with the makespan. Liu et al (2016) studied a job shop energy-efficient scheduling problem. Energy consumption was decreased by turning off underutilized machines, accounting for the trade-off with total weighted tardiness.…”

Section: Energy-efficient Production Schedulingmentioning

confidence: 99%

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Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

Gong

Wee

Pessemier

et al. 2017

Journal of Cleaner Production

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With the penetration of smart grid into factories, energyefficient production scheduling has been emerged. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, medium, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. 1Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: an empirical study AbstractWith the penetration of smart grid into factories, energy-efficient production scheduling has been emerged. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and laboraware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instan...

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Section: Energy-efficient Production Schedulingmentioning

confidence: 99%

“…Some of GHG emissions are caused by unnecessary machine idling (Liu et al, 2016) and peak power consumption in the electricity grid (Gong et al, 2016a), which are solvable by production scheduling.…”

Section: Introductionmentioning

confidence: 99%

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

Gong

Wee

Pessemier

et al. 2017

Journal of Cleaner Production

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“…Lu et al [10] proposed an energy-efficient permutation flow shop scheduling model considering controllable transportation times solved by backtracking search algorithm and developed a new energy saving strategy. In the job shop environment, Liu et al [11] used a novel multiobjective GA to minimize the total nonprocessing electricity consumption and total weighted tardiness and employed a "Turn On/Turn Off" method to save electricity.…”

Section: Related Workmentioning

confidence: 99%

A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

Zhang

Dai

Zhang

et al. 2017

Mathematical Problems in Engineering

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Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS).

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“…Higher energy prices and strict green legislation are big challenges for today's manufacturing industry. Liu et al [10] developed a bi-objective optimization problem to reduce idle time energy consumption and devise energy saving strategies. A comprehensive review of energy efficient production planning was provided by Biel and Glock [9].…”

Section: Introductionmentioning

confidence: 99%

Economic Analysis of an Integrated Production–Inventory System under Stochastic Production Capacity and Energy Consumption

2019

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Expensive power cost is a significant concern in today’s manufacturing world. Reduction in energy consumption is an ultimate measure towards achieving manufacturing efficiency and emissions control. In the existing literature of scheduling problems, the consumption of energy is considered uncertain under the dimensions of uncertain demand and supply. In reality, it is a random parameter that also depends on production capacity, manufacturing technology, and operational condition of the manufacturing system. As the unit production cost varies with production rate and reliability of the manufacturing system, the energy consumption of the system also varies accordingly. Therefore, this study investigated an unreliable manufacturing system under stochastic production capacities and energy consumption. A stochastic production–inventory policy is developed to optimize production quantity, production rate, and manufacturing reliability under variable energy consumption costs. As energy consumption varies in different operational states of manufacturing, we consider three specific states of power consumption, namely working, idle, and repair time, for an integrated production–maintenance model. The considered production system is subjected to stochastic failure and repair time, where productivity and manufacturing reliability is improved through additional technology investment. The robustness of the model is shown through numerical example, comparative study, and sensitivity analysis of model parameters. Several graphical illustrations are also provided to obtain meaningful managerial insights.

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A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance

Cited by 117 publications

References 25 publications

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

Economic Analysis of an Integrated Production–Inventory System under Stochastic Production Capacity and Energy Consumption

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