A multi-objective pigeon inspired optimization algorithm for fuzzy production scheduling problem considering mould maintenance

2019 ◽  
Vol 62 (7) ◽  
Author(s):  
Xiaoyue Fu ◽  
Felix T. S. Chan ◽  
Ben Niu ◽  
Nick S. H. Chung ◽  
Ting Qu
Keyword(s):  
Optimization Algorithm ◽  
Production Scheduling ◽  
Scheduling Problem ◽  
Multi Objective

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.

Multi-objective discrete water wave optimization algorithm for solving the energy-saving job shop scheduling problem with variable processing speeds

2021 ◽  
pp. 1-15
Author(s):  
Huiqi Zhu ◽  
Tianhua Jiang ◽  
Yufang Wan ◽  
Guanlong Deng
Keyword(s):  
Energy Saving ◽  
Optimization Algorithm ◽  
Production Scheduling ◽  
Water Wave ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Total Energy Consumption ◽  
Multi Objective ◽  
Variable Processing

For the job shop with variable processing speeds, the aim of energy saving and consumption reduction is implemented from the perspective of production scheduling. By analyzing the characteristics of the workshop, a multi-objective mathematical model is established with the objective of reducing the total energy consumption and shortening the makespan. A multi-objective discrete water wave optimization (MODWWO) algorithm is proposed for solving the problem. Firstly, a two-vector encoding method is adopted to divided the scheduling solution into two parts, which represent speed selection and operation permutation in the scheduling solution, respectively. Secondly, some dispatching rules are used to initialize the population and obtain the initial scheduling solutions. Then, three operators of the basic water wave optimization algorithm are redesigned to make the algorithm adaptive for the multi-objective discrete scheduling problem under study. A new propagation operator is presented with the ability of balancing global exploration and local exploitation based on individual rank and neighborhood structures. A novel refraction operator is designed based on crossover operation, by which each individual can learn from the current best individual to absorb better information. And a breaking operator is modified based on the local search strategy to enhance the exploitation ability. Finally, extensive simulation experiments demonstrate that the proposed MODWWO algorithm is effective for solving the considered energy-saving scheduling problem.

scholarly journals An Application of the Harmony-Search Multi-Objective (HSMO) Optimization Algorithm for the Solution of Pump Scheduling Problem

2016 ◽  
Vol 162 ◽  
pp. 494-502 ◽  
Author(s):  
Francesco De Paola ◽  
Nicola Fontana ◽  
Maurizio Giugni ◽  
Gustavo Marini ◽  
Francesco Pugliese
Keyword(s):  
Optimization Algorithm ◽  
Harmony Search ◽  
Scheduling Problem ◽  
Multi Objective ◽  
Pump Scheduling

scholarly journals Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092523 ◽  
Author(s):  
Xiaoyu Wen ◽  
Xinyu Li ◽  
Liang Gao ◽  
Kanghong Wang ◽  
Hao Li
Keyword(s):  
Process Planning ◽  
Optimization Algorithm ◽  
Honey Bees ◽  
Processing Time ◽  
Intelligent Manufacturing ◽  
Scheduling Problem ◽  
Integrated Process ◽  
Due Date ◽  
Planning And Scheduling ◽  
Multi Objective

The new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing. Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. At present, the research on integrated process planning and scheduling is insufficient to consider the multi-objective and uncertain characteristics widely existing in real manufacturing environment. Therefore, multi-objective integrated process planning and scheduling problem with uncertain processing time and due date is addressed in this article. The mathematical model of multi-objective uncertain integrated process planning and scheduling problem with uncertain processing time and fuzzy due date is established based on fuzzy set, in which the calculation method of uncertainty measurement objective is designed. An effective modified honey bees mating optimization algorithm has been designed to solve the proposed model. Queens set is constructed to maintain the non-dominated solutions found in the optimization process. The calculation method of mating probability between drone and queen bee based on Euclidean distance is designed. Fuzzy operators were utilized to evaluate fitness, judge the non-dominated relationship, and decode the scheduling solution. Different instances were designed and carried out to test the performance of the proposed method. The results show that the proposed method is very effective for solving multi-objective uncertain integrated process planning and scheduling.

scholarly journals A multi-objective multi-agent optimization algorithm for the multi-skill resource-constrained project scheduling problem with transfer times

2021 ◽  
Author(s):  
Amirhossein Hosseinian ◽  
Vahid Baradaran
Keyword(s):  
Optimization Algorithm ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Multi Objective ◽  
Proposed Model ◽  
Multi Agent ◽  
Project Scheduling Problem ◽  
Transfer Times

This paper addresses the Multi-Skill Resource-Constrained Project Scheduling Problem with Transfer Times (MSRCPSP-TT). A new model has been developed that incorporates the presence of transfer times within the multi-skill RCPSP. The proposed model aims to minimize project’s duration and cost, concurrently. The MSRCPSP-TT is an NP-hard problem; therefore, a Multi-Objective Multi-Agent Optimization Algorithm (MOMAOA) is proposed to acquire feasible schedules. In the proposed algorithm, each agent represents a feasible solution that works with other agents in a grouped environment. The agents evolve due to their social, autonomous, and self-learning behaviors. Moreover, the adjustment of environment helps the evolution of agents as well. Since the MSRCPSP-TT is a multi-objective optimization problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used in different procedures of the MOMAOA. Another novelty of this paper is the application of TOPSIS in different procedures of the MOMAOA. These procedures are utilized for: (1) detecting the leader agent in each group, (2) detecting the global best leader agent, and (3) the global social behavior of the MOMAOA. The performance of the MOMAOA has been analyzed by solving several benchmark problems. The results of the MOMAOA have been validated through comparisons with three other meta-heuristics. The parameters of algorithms are determined by the Response Surface Methodology (RSM). The Kruskal-Wallis test is implemented to statistically analyze the efficiency of methods. Computational results reveal that the MOMAOA can beat the other three methods according to several testing metrics. Furthermore, the impact of transfer times on project’s duration and cost has been assessed. The investigations indicate that resource transfer times have significant impact on both objectives of the proposed model

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