Multi-objective bacterial colony optimization algorithm for integrated container terminal scheduling problem

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.

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An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

Journal of Systems Engineering and Electronics ◽

10.23919/jsee.2021.000024 ◽

2021 ◽

Vol 32 (2) ◽

pp. 272-285

Author(s):

Wu Xiuli ◽

Peng Junjian ◽

Xie Zirun ◽

Zhao Ning ◽

Wu Shaomin

Keyword(s):

Optimization Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Flexible Job Shop ◽

Scheduling Problem ◽

Flexible Job Shop Scheduling ◽

Job Shop Scheduling Problem ◽

Multi Objective Optimization ◽

Shop Scheduling ◽

Multi Objective

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Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem

Computers & Industrial Engineering ◽

10.1016/j.cie.2021.107345 ◽

2021 ◽

pp. 107345

Author(s):

Junqi Cai ◽

Zhihong Peng ◽

Shuxin Ding ◽

Jingbo Sun

Keyword(s):

Optimization Algorithm ◽

Scheduling Problem ◽

Invasive Weed Optimization ◽

Invasive Weed ◽

Multi Objective

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

RAIRO - Operations Research ◽

10.1051/ro/2021087 ◽

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

Sustainability ◽

10.3390/su11195381 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5381 ◽

Cited By ~ 2

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.

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