A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

García-Santiago, Carlos; Ser, Javier Del; Upton, Connor; Quilligan, F.; Gil-López, Sergio; Salcedo‐Sanz, Sancho

doi:10.1080/0305215x.2014.971778

Cited by 22 publications

(12 citation statements)

References 36 publications

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“…Although studies with partial power states generally consider idle and processing states, they ignore some basic power states, such as shutdown [21,22] as well as startup and shutdown states [26]. The investigations that neglect power states often classify the total machine energy consumption into unload and cutting energy [19,23].…”

Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

“…Therefore, this needs novel modeling and effective optimization with more constraints. Garcia-Santiago, et al [22] mentioned that a human operator is required to change the ancillary part of a machine upon a change of product type, but they did not investigate how to economically match machine and human resources. The only relevant literature that considers the labor aspect is [6].…”

Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

“…A common objective in these investigations is makespan, as it not only directly measures the time to complete a set of jobs, but also is linked to other important production metrics, e.g., earliness/tardiness of finished jobs and workload. The energy-related objectives include minimization of total energy consumption to process a set of jobs [3,16,19,22,28,33], non-processing energy [16,25], peak power consumption [17], and energy cost [20,25].…”

Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

“…A genetic algorithm (GA) [19,21,27,32] as well as its variants NSGA-II [18,25] and memetic algorithm (MA) [3,31] are the most prevalent among these metaheuristics. Besides, Xu, et al [28] used a bee algorithm, Garcia-Santiago, et al [22] utilized a harmony search algorithm, and [23] employed a nested partitions algorithm. Furthermore, two metaheuristics are often hybridized for enhanced search capability in the solution space.…”

Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

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

Gong

Pessemier

Martens

et al. 2019

Journal of Cleaner Production

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Energy-aware production scheduling is a promising way to adapt the factories' energy consumption behavior to the volatile electricity prices in the demand response initiative of smart grids. However, it may not be economical by simply scheduling production loads to the periods with lower electricity prices, as these periods often have higher labor wage, e.g., nights and weekends. Based on this gap, this paper proposes a many-objective integrated energy-and labor-aware flexible job shop scheduling model. Many objectives refer to the number of optimization objectives surpasses three (i.e., five objectives: makespan, total energy cost, total labor cost, maximal workload, and total workload), whereas the existing energy-aware production scheduling research is limited within three objectives. To enable energy awareness in the conventional production scheduling algorithms, a state-based shop floor wide energy model is proposed. To enable labor awareness, the number and type of human workers are matched to the scheduled production loads, with varying labor wage over shifts. As one of the most complex shop floor configurations, the partial flexible job shop further considers job recirculation and operation sequence-dependent machine setup times. The recently-proposed nondominated sorting genetic algorithm-III (NSGA-III) is tailored for this many-objective optimization problem (MaOP), including scheduling solution encoding and decoding, crossover, mutation, and solution evaluation using the energy-and labor-aware discrete-event simulation framework. Through numerical experiments under real-time pricing (RTP) and time-of-use pricing (ToUP), insights are statistically obtained on the relation among these five production objectives; the effectiveness and efficiency of NSGA-III in solving a MaOP are also demonstrated. This proposed scheduling method can be used to automated and enhance the decision making of factory managers in jointly allocating machine, human worker, and energy resources on the shop floor, such that the production cost is minimized even under time-varying electricity and labor prices.

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Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

See 2 more Smart Citations

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

Gong

Pessemier

Martens

et al. 2019

Journal of Cleaner Production

View full text Add to dashboard Cite

show abstract

“…Other contributions have also resorted to RK for job-shop problems where a computer simulation of the plant provides a quantitative measure of the optimality fitness that guides the search process [27]. The actual proposed algorithm is based on a previous RK-HS based approach [28] but incorporates a multi-objective approach for obtaining a wide set of solutions.…”

Section: Considered Algorithmsmentioning

confidence: 99%

Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics

Landa-Torres

Manjarres

Bilbao

et al. 2017

Sensors

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Robotics deployed in the underwater medium are subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this context the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by devising three heuristic solvers aimed at efficiently scheduling tasks in robotic swarms, which collaborate together to accomplish a mission, and by presenting experimental results obtained over realistic scenarios in the underwater environment. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks and the relative execution order of such tasks within the schedule of certain robots. The obtained results reveal interesting differences in terms of Pareto optimality and spread between the algorithms considered in the benchmark, which are insightful for the selection of a proper task scheduler in real underwater campaigns.

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