scholarly journals Hybrid Estimation of Distribution Algorithm for Solving Three-Stage Multiobjective Integrated Scheduling Problem

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Chao Deng ◽  
Rong Hu ◽  
Bin Qian ◽  
Huai P. Jin
Keyword(s):  
Energy Consumption ◽  
Local Search ◽  
Manufacturing Industry ◽  
Search Strategies ◽  
Estimation Of Distribution Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Hybrid Estimation ◽  
Integrated Scheduling ◽  
Estimation Of Distribution

Aiming at reducing the total energy consumption of three stages processing-transportation-assembly in the assembly manufacturing industry, a three-stage multiobjective integrated scheduling problem with job batch transportation considering the energy consumption (3sMISP_JBTEC) is proposed, and a comprehensive energy consumption model of multistage of 3sMISP_JBTEC with an improved turn off/on strategy in the processing stage and considering speed in the transportation stage is formulated. Then, a hybrid estimation of distribution algorithm with variable neighborhood search (HEDA_VNS) is developed to solve the scheduling problem. In the HEDA_VNS, the reasonable coding/decoding rules and speed scheduling scheme (SSS) are designed. Moreover, two local search strategies are designed to further enhance the performance of HEDA_VNS. Among them, three types of neighborhood search strategies are devised in Local Search I to improve the search efficiency while retaining the structure of the original high-quality solution. A variable neighborhood hybrid operation based on the speed scheduling set is designed in Local Search II to further improve the quality of the solution while balancing the optimization goals. Finally, simulations and comparisons show the efficiency of the proposed HEDA_VNS.

Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

2013 ◽  
Vol 13 (10) ◽  
pp. 4063-4076 ◽  
Author(s):  
Christopher Expósito-Izquierdo ◽  
José Luis González-Velarde ◽  
Belén Melián-Batista ◽  
J. Marcos Moreno-Vega
Keyword(s):  
Estimation Of Distribution Algorithm ◽  
Scheduling Problem ◽  
Quay Crane Scheduling ◽  
Crane Scheduling ◽  
Hybrid Estimation ◽  
Estimation Of Distribution

scholarly journals An Enhanced MOPSO Algorithm for Energy-Efficient Single-Machine Production Scheduling

2019 ◽  
Vol 11 (19) ◽  
pp. 5381 ◽  
Author(s):  
Yueyue Liu ◽  
Xiaoya Liao ◽  
Rui Zhang
Keyword(s):  
Energy Consumption ◽  
Local Search ◽  
Single Machine ◽  
Optimization Algorithm ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Scheduling Problem ◽  
Nsga Ii ◽  
Multi Objective

In recent years, the concerns on energy efficiency in manufacturing systems have been growing rapidly due to the pursuit of sustainable development. Production scheduling plays a vital role in saving energy and promoting profitability for the manufacturing industry. In this paper, we are concerned with a just-in-time (JIT) single machine scheduling problem which considers the deterioration effect and the energy consumption of job processing operations. The aim is to determine an optimal sequence for processing jobs under the objective of minimizing the total earliness/tardiness cost and the total energy consumption. Since the problem is NP -hard, an improved multi-objective particle swarm optimization algorithm enhanced by a local search strategy (MOPSO-LS) is proposed. We draw on the idea of k-opt neighborhoods and modify the neighborhood operations adaptively for the production scheduling problem. We consider two types of k-opt operations and implement the one without overlap in our local search. Three different values of k have been tested. We compare the performance of MOPSO-LS and MOPSO (excluding the local search function completely). Besides, we also compare MOPSO-LS with the well-known multi-objective optimization algorithm NSGA-II. The experimental results have verified the effectiveness of the proposed algorithm. The work of this paper will shed some light on the fast-growing research related to sustainable production scheduling.

scholarly journals A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

2021 ◽  
Vol 26 (3) ◽  
pp. 64
Author(s):  
Ricardo Pérez-Rodríguez
Keyword(s):  
Solution Space ◽  
Estimation Of Distribution Algorithm ◽  
Space Distribution ◽  
Scheduling Problem ◽  
Probability Models ◽  
Quay Crane Scheduling ◽  
Crane Scheduling ◽  
Hybrid Estimation ◽  
Ranking Model ◽  
Estimation Of Distribution

The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.

scholarly journals An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints

2019 ◽  
Vol 11 (11) ◽  
pp. 3085 ◽  
Author(s):  
Min Dai ◽  
Ziwei Zhang ◽  
Adriana Giret ◽  
Miguel A. Salido
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Manufacturing Industry ◽  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Estimation Of Distribution Algorithm ◽  
Mixed Integer ◽  
Shop Scheduling ◽  
Estimation Of Distribution

Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.

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