Enhanced NSGA-II for multi-objective energy-saving flexible job shop scheduling

In the context of Industry 5.0, worker well-being is a central tenet within the production process. This research work aims to contribute to this goal by integrating ergonomic factors into task allocation and scheduling while maintaining high productivity. An ergonomics-aware operator-task allocation and scheduling algorithmic framework is developed for a flexible job shop production system to minimise the makespan while ensuring that the maximum physical burden on each operator is not exceeded. The proposed algorithmic framework can integrate any method that assesses the composite ergonomic load on an operator who has been assigned multiple tasks. It can handle several ergonomic measurements of different risk factors with their predefined threshold to ensure safety and efficiency. This flexibility enables decision-makers to select the best measures for their needs. This framework is tested on small to moderate-sized instances, and the numerical experiments show that the proposed algorithmic framework converges to a solution satisfying the ergonomic risk requirement, at the expense of only a slight increase in the makespan. The expected reduction in ergonomics-related injuries in companies adopting this framework would result in an actual productivity gain, which may turn out to be significantly higher than the minor loss due to the increased makespan.

We propose an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, we consider time to be a triangular fuzzy number and apply an expert system to infer cost. The objective is to find a path minimising both the time and cost criteria, aggregately. Since time and cost have different scales, a normalisation procedure is proposed to remove the scales. The model being bi-objective, the analytical hierarchy process weighing method is applied to construct a single objective. Finally, a dynamic programming approach is presented for computing a shortest path in the network. The efficiency of the proposed approach is illustrated by a numerical example.

Flexible job shop scheduling problem (FJSP) is a discrete combinatorial optimisation problem. By considering its characteristics, a kind of adaptive discrete cat swarm optimisation algorithm (ADCSO) is proposed with three objectives to be optimised simultaneously, i.e., makespan, maximal machine workload and total workload. Firstly, a two-phase encoding mechanism is proposed to represent each individual, and a heuristic-based strategy is employed to generate the initial population. Secondly, the discrete seeking mode and tracing mode are developed to make the algorithm directly work in a discrete domain. Thirdly, four adaptive adjustment approaches with different decline curves are used to balance the abilities of global search and local search. Furthermore, a local search strategy is embedded into the algorithm to improve the searching capacity. Finally, extensive experimental data are obtained based on the benchmark instances. The comparison results show that ADCSO is effective for the problem under study.

On-site study was held to investigate the influences of load factor on power consumption and energy performance of water-cooled air conditioning (WCAC) system. A comprehensive measurement has been conducted to a WCAC plant of a supermall building located in West Java, Indonesia. Hourly and daily power consumption and performance characteristics of the WCAC system at varied load factors for a period of one year were evaluated. Regression analyses were developed to evaluate the influences of load factor on the WCAC power consumption and performance. The WCAC system was found to operate with varied load factor ranging from 70 percent to 100 percent. Power consumption of the system was strongly influenced by the load factor. Power consumption boosted when the power factor increased. The increase of load factor was also found to significantly improve the cooling capacity but reduce the coefficient of performance (COP). The best potential performance of the investigated WCAC system could be obtained at load factor between 70 percent and 85 percent.

Automated guided vehicles (AGVs) are widely used for transportation in flexible job shop (FJS) systems, and their transportation task scheduling has the same substantial impact on production efficiency as machine scheduling does. However, traditional FJS scheduling methods often prioritize job sequencing and machine selection while ignoring the impact of AGV transportation, resulting in suboptimal scheduling solutions and even difficulties in implementation. To address this issue, this paper formulates a cooperative scheduling model by introducing the AGV scheduling problem into the classical FJS scheduling problem, abbreviated as the FJS-AGV problem, with the objective of minimizing the makespan. With respect to the FJS-AGV problem, a heuristic-assisted deep Q-network (HA-DQN) algorithm is proposed, which leverages heuristic rules to enable the decision agent to perform multiple actions at each decision point, which includes determining the responses to the following questions: Which operation should be processed next? On which machine? By which AGV? This decision mechanism enables the agent to make more informed decisions, leading to improved performance and resource allocation in the FJS-AGV system. The practicability of the proposed FJS-AGV model and the efficiency of the HA-DQN algorithm in solving the FJS-AGV problem are verified through various international benchmarks. Specifically, when solving instances in a large benchmark, the HA-DQN algorithm yields a significant 12.63% reduction in makespan compared with that when traditional heuristics are employed.

Recently, the semiconductor manufacturing industry has exhibited not only fast growth, but intense power consumption. Consequently, reducing power consumption is critical for running reliability. A view of literature reveals that the power consumption of facility system is 56.6 % in the fabs. Among all facility systems, chiller plants are the largest energy users, consuming 27.2 % of the total power consumption. Therefore, saving power consumption for chiller plants involves a considerable economic benefit. In addition, cooling the water temperature further improves the efficiency of chillers. Hence, this report analyzes the optimal temperature between the chiller and cooling tower. Currently, controlling the chiller and cooling tower are separate processes, though, in fact, they should not be. This is because the water cooling temperature affects the efficiency of the chiller. Each reduced degree of the chiller condenser temperature reduces the electrical power by approximately 2 % in the cooling tower, in contrast to the chiller. Therefore, the optimal water cooling water temperature must be analyzed. The analysis method in this report is linear regression. First, determine the equations of power consumption for the chiller and cooling tower with variables representing the water cooling temperature, water supply temperature of the chiller, and outdoor loading and wet-bulb temperatures. Second, add the coefficient of the same variable to obtain the total power consumption equation for the chiller and cooling tower. The result shows the relationships of power consumption with water cooling temperature under identical conditions of the water cooling temperature, water supply temperature of chiller, and outdoor loading and wet-bulb temperatures. Finally, use the differential method to determine the optimal water cooling temperature.

Production scheduling in plants, due to their hybrid nature, can become very complex. Manufacturers developed various manufacturing operations to facilitate better scheduling, while effective production schedules ensure smoothness of downstream operations. This paper deals with a production scheduling problem in a flexible (or hybrid) job-shop with particular constraints: batch production; existence of two steps: production of several sub-products followed by the assembly of the final product; possible overlaps for the processing periods of two successive operations of a same job. Different objectives should be considered simultaneously, among the makespan, the mean completion time, the maximal tardiness, the mean tardiness. The main contribution of this paper is the presentation of a novel approach based on a genetic algorithm as a suitable tool for scheduling of hybrid systems. The major benefit of this approach is a significant reduction in complexity during problem formulation. The proposed method is explained through a mill case study.

Homomorphic encryption for smart grids has been investigated in many studies. It is possible to estimate the total power consumption in an area without knowing the consumption data of individual households. In the case of demand response (DR), it is important to calculate the total electric power consumption in an area because DR reports are published accordingly to reduce peak power consumption when the demand is high. However, the published data may reveal private information about residents, such as the timings of specific activities (leaving from and returning home), and device details. To overcome this problem, we propose a method specialized to enable energy providers to securely share electric power consumption data. The proposed method uses a self-organizing map (SOM), which is an unsupervised learning method. In order to share power consumption data while preserving privacy, the SOM is shared without the raw data. In this framework, a target accuracy of nearly 3% is achieved, while actual data are not published by any company.

We investigate the impact of level-1 cache (CL1) parameters, level-2 cache (CL2) parameters, and cache organizations on the power consumption and performance of multi-core systems. We simulate two 4-core architectures - both with private CL1s, but one with shared CL2 and the other one with private CL2s. Simulation results with MPEG4, H.264, matrix inversion, and DFT workloads show that reductions in total power consumption and mean delay per task of up to 42% and 48%, respectively, are possible with optimized CL1s and CL2s. Total power consumption and the mean delay per task depend significantly on the applications including the code size and locality.

The ever-growing amount of Internet traffic places a large burden on the power consumption of network systems, especially IP routers. The IP packet forwarding off-load using an optical cut-through technique has been studied for this purpose, but the total power consumption of the network should be more effectively reduced by combining this optical cut-through with power-aware network systems. This paper proposes a low power consumption technique using an adaptive optical path control and power-aware rearrangeable network systems in response to network traffic patterns and volumes. The total power-consumption can be minimized by optimizing the optical path bandwidth and packet forwarding capacities. This paper also presents some initial evaluation results on the effect of our proposal on the power consumption reduction.

In this paper author demonstrates the relationship between spectral efficiency and energy efficiency for 10 Gbps, 40 Gbps and 100 Gbps transmission systems with NRZ-OOK, NRZ-DPSK and DP-QPSK modulation formats respectively. The focus of this research is the wavelength division multiplexed (WDM) transmission systems with Forward Error Correction (FEC) technology with required Bit Error Rate (BER) level that does not exceed 10−3. For this purpose the signal BER level versus cross-channel interval response is evaluated. Obtained data was used for the regeneration length calculations with purpose to define the number of regeneration steps for 200km and 2600km transmission systems. In order to observe relationship between spectral and energy efficiency, power consumption was calculated using different crosschannel intervals −37.5 GHz, 50 GHz and 100 GHz, by means of what different spectral efficiencies were reached. The total power consumption of transmission system was calculated by defining the quantity and power consumption values for all WDM system elements that require electrical energy. In this paper author demonstrates how the amount of transmitted information influence the total system power consumption in each considered transmission system and by using obtained data and spectral efficiency data, the systems energy efficiencies were calculated.

Background and study aimsLight-emitting diodes are being developed as a light source for an endoscope system instead of xenon lamps, contributing to improved diagnostic capability. This system also has the advantage of consuming less power than a conventional endoscope system using xenon lamps. Power consumption associated with endoscopy is an important factor in reducing greenhouse gas emissions. This study estimated the reduction in greenhouse gas emissions by using a light-emitting diode endoscope system.MethodsWe calculated the power consumption and carbon dioxide emission reduction of the light-emitting diode endoscope system in comparison with a conventional endoscope system using a xenon light source. Calculations were based on actual data regarding operating time and the annual number of endoscopic procedures at Jichi Medical University Hospital. Estimates were also extended to a nationwide scale.ResultsIf each type of endoscope system were used for all endoscopic procedures, the light-emitting diode endoscope system would reduce power consumption by 49% compared with the xenon endoscope system, resulting in a total power reduction of 838.7kWh, equivalent to 394.2 kg of carbon dioxide emissions. In Japan, the total power consumption would be reduced by 53%, corresponding to a total power reduction of 428,628.8 kWh, equivalent to 201,455.5 kg of carbon dioxide emissions.ConclusionsWe estimated the effect of reducing carbon dioxide emissions using the light-emitting diode endoscope system. Wider adoption of the light-emitting diode endoscope system is expected to contribute to usefulness of endoscopic diagnosis and reduction in environmental impact.

Information systems based on the cloud computing model and peer-to-peer (P2P) model are now getting popular. In the cloud computing model, a cloud of servers support thin clients with various types of service like Web pages and databases. On the other hand, every computer is peer and there is no centralized coordinator in the P2P model. It is getting more significant to discuss how to reduce the total electric power consumption of computers in information systems to realize eco-society. In this paper, we consider a Web type of application on P2P overlay networks. First, we discuss a model for showing how much each server peer consumes electric power to perform Web requests from client peers. Then, we discuss algorithms for a client peer to select a server peer in a collection of server peers so that the total power consumption can be reduced while some constraint like deadline one is satisfied. Lastly, we evaluate the algorithms in terms of the total power consumption and throughput compared with traditional round robin algorithms.

Information systems are composed of various types of computers interconnected in networks. In addition, information systems are being shifted from the traditional client-server model to the peer-to-peer (P2P) model. P2P systems are scalable and fully distributed without any centralized coordinator. Here, it is getting more significant to discuss how to reduce the total electric power consumption of computers in addition to developing distributed algorithms to minimize the computation time and memory space. In this paper, we do not discuss microlevel models like the hardware specifications of computers like low-energy CPUs. We rather discuss a macrolevel model to show the relation of the amount of computation and the total power consumption of multiple peer computers to perform Web types of application processes. We also discuss algorithms for allocating a process to a computer so that the deadline constraint is satisfied and the total power consumption is reduced.

Information systems are composed of various types of computers interconnected in networks. In addition, information systems are being shifted from the traditional client-server model to the peer-to-peer (P2P) model. The P2P systems are scalable and fully distributed without any centralized coordination. It is getting more significant to discuss how to reduce the total electric power consumption of computers in information systems in addition to developing distributed algorithms to minimize the computation time. In this paper, we do not discuss the micro level like the hardware specification of each computer. We discuss a model to show the relation of the computation and the total power consumption of multiple peer computers to perform Web types of processes at macro level. We also discuss algorithms for allocating a process to a computer so that the deadline constraint is satisfied and the total power consumption is reduced.