Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem Considering Energy Consumption

Jiang, Tianhua; Deng, Guanlong

doi:10.1109/access.2018.2866133

Cited by 56 publications

(29 citation statements)

References 25 publications

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“…In our previous work, an energy-aware flexible job shop scheduling problem is studied without the consideration of the dual-resource constraints [12]. Based on the existing model, a new mathematical model of the EDRCFJSP problem is modeled as below.…”

Section: Problem Modellingmentioning

confidence: 99%

“…For any two operations on the same machine, the processing of the operation cannot be started until its preceding operation is completed and the setup of the machine is finished; Constraint (11) represents that each operation is processed by one machine and the assigned machine is operated by one worker. Constraint (12) shows the workload of each machine; Constraint (13) gives the total setup time of each machine; Constraint (14) denotes the completion time of each machine; Constraint (15) defines the start time of each machine; Constraints (16) and 17show the binary variables.…”

Section: Problem Modellingmentioning

confidence: 99%

“…Six mixed-integer linear programming models were presented in the study, whose correctness and effectiveness were tested by using CPLEX solver through numerical experiments. Jiang and Deng [12] established a mathematical model for the energy-aware flexible job shop scheduling problem to minimize the energy cost and the earliness/tardiness cost. A bi-population based discrete cat swarm optimization algorithm was developed to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

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Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

Zhu

Jiang

2020

Algorithms

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In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.

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Section: Problem Modellingmentioning

confidence: 99%

Section: Problem Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

Zhu

Jiang

2020

Algorithms

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“…In recent years, a large number of bio‐inspired optimization algorithms have been proposed, such as genetic algorithm (GA), bat algorithm (BA), differential evolution (DE), firefly algorithm (FA), and so on, which have been applied to various fields, including the malicious code detection, LEACH protocol optimization, privacy preservation, intrusion detection system, job scheduling problem, and so on . Hence, more scholars began to use them to decrease the error of the DVHop positioning algorithm because of their good global convergence and high robustness.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian error correction multi‐objective positioning model with NSGA‐II

Wang

Huang

Cui

et al. 2019

Concurrency and Computation

167

140

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Distance vector-hop (DVHop), as a range-independent positioning algorithm, is a significant positioning method in wireless sensor networks (WSNs). It is composed of three parts, including connectivity detection, distance estimation, and position estimation. However, this simple positioning method results in a larger positioning error. Therefore, to enhance the positioning precision, this paper investigates the characteristic of error distribution between the estimated and real distance in the DVHop algorithm and reveals that the error is subjecting to the Gaussian distribution, N∼(0,1/3CR). Furthermore, to improve positioning accuracy, we propose a Gaussian error correction multi-objective positioning model with non-dominated sorting (NSGA-II), which named GGAII-DVHop. Finally, this model is tested on three complex network topologies, and the results demonstrate that it is significantly superior to other four algorithms in both positioning precision and robustness. KEYWORDSGaussian error correction, GGAII-DVHop, multi-objective DVHop model, wireless sensor networks INTRODUCTIONRecently, the smaller and smarter sensors are deployed in WSNs 1-3 with the micro electromechanical systems technologies advanced. These seemingly tiny nodes are made up of devices such as memory, transceivers, power supplies, etc, which constitute inexpensive, independent, and low-powered WSNs. In addition, WSNs are widely applied to industry, 4 medical treatment, home care, machine learning, and so on. However, their communication radius (CR) is limited due to their low power consumption, which determines the nodes that communicate with each other by transmitting packets, that is multi-hop routing. The Internet of Things (IoT) 5-7 is composed of many such small and relatively independent WSNs.In these applications, the location of the sensor nodes is an unavoidable topic because many of the data will be meaningless without location information. For this reason, various localization algorithms were designed to sharpen the positioning accuracy of nodes, including the range-dependent and range-independent localization algorithms. Among them, the received signal strength indicator (RSSI), 8 time of arrival (TOA), 9 and Ad hoc positioning system (APS) 10 are typically range-dependent positioning algorithms; the distance vector hop (DVHop), 11 convex position estimation (CPE), 12 and multi-dimensional scaling (MDS) 13 are representatives of range-independent localization algorithms, such as Figure 1. Because of the limitation of the computer hardware and overall cost, the range-independent localization algorithms 14,15 are more popular with researchers.The DVHop is widely used due to its simple operation and low hardware requirements. It uses the multi-hop routing between sensor nodes, and the average distance of per hop between each anchor node to locate. The positioning process of DVHop can be divided into three parts, including connectivity detection, distance estimation, and position estimation. However, this simple positioning principle us...

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“…Wu and Wu [27] pronounced an elitist quantum evolutionary algorithm for the FJSP with a criterion for makespan. Jiang and Deng [28] presented a discrete cat swarm optimization algorithm (DCSO) for solving the FJSP with the consideration of energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Luan

Cai

et al. 2019

Mathematics

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The flexible job shop scheduling problem (FJSP) is a difficult discrete combinatorial optimization problem, which has been widely studied due to its theoretical and practical significance. However, previous researchers mostly emphasized on the production efficiency criteria such as completion time, workload, flow time, etc. Recently, with considerations of sustainable development, low-carbon scheduling problems have received more and more attention. In this paper, a low-carbon FJSP model is proposed to minimize the sum of completion time cost and energy consumption cost in the workshop. A new bio-inspired metaheuristic algorithm called discrete whale optimization algorithm (DWOA) is developed to solve the problem efficiently. In the proposed DWOA, an innovative encoding mechanism is employed to represent two sub-problems: Machine assignment and job sequencing. Then, a hybrid variable neighborhood search method is adapted to generate a high quality and diverse population. According to the discrete characteristics of the problem, the modified updating approaches based on the crossover operator are applied to replace the original updating method in the exploration and exploitation phase. Simultaneously, in order to balance the ability of exploration and exploitation in the process of evolution, six adjustment curves of a are used to adjust the transition between exploration and exploitation of the algorithm. Finally, some well-known benchmark instances are tested to verify the effectiveness of the proposed algorithms for the low-carbon FJSP.

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Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem Considering Energy Consumption

Cited by 56 publications

References 25 publications

Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

A Gaussian error correction multi‐objective positioning model with NSGA‐II

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

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