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

Abstract

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...

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

“…Unfortunately, the existing methods widely ignore the interdependence of energy and labor costs [8]. Shifting production loads from a day to a night or a weekend for energy cost reduction is usually compromised by an increased labor wage and thus a rising labor cost [9][10][11]. Consequently, the overall production cost may risk to rise.…”

“…Nevertheless, the following limitations are observed: (1) the labor rate does not vary among workers; (2) the productionprohibited period, which is often introduced by the labor shift, and its constrained influence on production operations are not modeled; (3) the exhaustive search is a rude solution method with poor scalability. A similar problem was investigated in [10] in a single objective optimization manner. Consequently, the trade-off relations in these cost parts and other important production metrics were not quantified, besides the aforementioned ignorance of production-prohibited periods.…”

“…Due to the weekend production constraint introduced by the labor model, a job contains one or more sub-durations, as indicated in Eq. (10).…”

Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation

“…The intrinsic trade-off relation between the energy and labor costs was pointed out in [7]: the lower-priced periods for the electricity consumption, e.g., nights and weekends, are usually higher-priced periods for the labor. This trade-off was further statistically quantified in an extensive sensitivity analysis with empirical data from a Belgium plastic bottle manufacturer [8]. The energy cost and labor cost were recommended to be jointly considered in a production scheduling algorithm.…”

New Evolutionary Algorithm for Optimizing Hydropower Generation Considering Multireservoir Systems

“…Therefore, optimization algorithms, such as metaheuristic and evolutionary algorithms, could be used to search for the better operation of dam and reservoir systems to maximize the generation of hydropower. In fact, one or some objective functions are defined for these problems with the aim of increasing hydropower generation in hydropower plants based on released water from upstream dams [5]. Thus, released water from upstream dams is considered to be an unknown variable or decision variable, where the accurate determination of this variable has an effect on hydropower generation [6,7].…”