Research on production planning and scheduling based on improved collaborative optimization

Zheng, Shuguo; Gao, Jiaxin; Xu, Jian

doi:10.1177/1063293x19842253

Cited by 7 publications

(8 citation statements)

References 35 publications

(33 reference statements)

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“…Meanwhile, production planning is an amalgamation of various production elements, from the daily activities of staff to the ability to realize accurate delivery times for customers. The production planning aimed for the overall production plan formulation and distribution (Zheng, Gao, & Xu, 2019). For example, simulations and genetic algorithm tools evaluate several non-parameter strategies and optimize the two parameterized strategies (Xu & Song, 2020).…”

Section: Introductionmentioning

confidence: 99%

Interior Design Production Plan Simulation Modeling with Dynamics System: A Case Study of PT. Panutan Sejati

Riyanto

Santoso

2022

jhsss

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This research was to make interior design production planning more controlled and optimal. The simulation method was considered able to represent the dynamic form of the system and can find scenarios to solve the problems at PT Panutan Sejati. The qualitative research was at PT. Panutan Sejati models located at the Workshop: Jl. Kartini Health Center No. 113 Kalisari, East Jakarta from June to September 2021. The results showed that from the modeling designed based on existing conditions, in the design production planning at PT Panutan Sejati, the factors influencing production planning were: market demand and the production process. While the factors that affected profit were production costs, labor resource costs, overhead costs, commercial costs, and net revenue. It was necessary for improving the proposed system improvement to create and implement scenarios to fulfill effective and efficient production planning to get increased profits. The scenarios included adding workers and creating a design division. The scenario that gave optimal results in increasing profit was scenario 1. This scenario changed the parameter value of the workforce. Based on changes in labor parameters as a variable in the model, if the percentage of labor was set to 45%, then production costs would decrease every year. It was predicted to reach 21.40% and an increase in profit by 13%, with an average decrease in production costs until 2026.

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Section: Introductionmentioning

confidence: 99%

Interior Design Production Plan Simulation Modeling with Dynamics System: A Case Study of PT. Panutan Sejati

Riyanto

Santoso

2022

jhsss

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“…Production scheduling is defined as a process that combines sales forecasting and manufacturing planning [5]. They are important to control resources in a complex production environment with many different machines and jobs.…”

Section: Introductionmentioning

confidence: 99%

How to boost the flow shop manufacturing agility using hybrid Genetic Tabu Search in scheduling

Umam

Gondohanindijo²

2022

Sinergi

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The hybridization between evolutionary genetic algorithm and tabu search has been proposed in this paper to address flow shop scheduling. It accommodates jobs that need to be rearranged and executed on identical machines serially. High agility is required in the manufacturing process, especially for the garment industry to be able to stand facing competitors. The manufacturing related to scheduling to deliver a product as early as possible, the tardiness, and waiting time are also concerned. A Genetic Algorithm was widely used to deal with this; which finds an optimal solution to the problems because it can obtain a more optimal solution. Unfortunately, it is easy to get stuck in optimum local (early convergence is faster). The tabu search algorithm works as a local explorer to better find and exploit the optimum local area, which can be combined with a Genetic Algorithm. This study aims to minimize the three objectives mentioned above to increase production agility. These strategies are evaluated on Taillard benchmark problems to show the significance of the proposed algorithm. The outcomes prove that the hybrid mechanism can boost the solution quality by 2.75% compared to our previous work and can resolve all of Taillard instances better. It has been proven by a 0.28% percentage relative deviation, which shows the error rate is lower and means better.

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“…(2) using a penalty function method to obtain an approximate solution [17]; (3) using the response surface method to estimate the equation constraint and solve the approximate model [18]. However, most previous improvements to the CO algorithm focused on system level optimization and ignored discipline level optimization.…”

Section: Introductionmentioning

confidence: 99%

Operation Cost Optimization on an Ultralow Emission System Based on Improved Collaborative Optimization

et al. 2020

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Due to the lack of an effective overall coordinated treatment method, it is difficult to achieve low cost and efficient removal of pollutants from coal-fired flue gas. This paper establishes a collaborative optimization model for ultralow emission systems, including a system level model of operation cost and three discipline level models for denitration, desulfurization, and dust removal. An improved collaborative optimization method with a dynamic penalty function is proposed to optimize an ultralow emission system. Simulation results show that the improved method achieves better global optimization and effectively reduces the operation cost of the system under ultralow emission constraints. INDEX TERMS Coal-fired flue gas pollutant, collaborative removal, dynamic penalty function, collaborative optimization, operation cost.

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Research on production planning and scheduling based on improved collaborative optimization

Cited by 7 publications

References 35 publications

Interior Design Production Plan Simulation Modeling with Dynamics System: A Case Study of PT. Panutan Sejati

Interior Design Production Plan Simulation Modeling with Dynamics System: A Case Study of PT. Panutan Sejati

How to boost the flow shop manufacturing agility using hybrid Genetic Tabu Search in scheduling

Operation Cost Optimization on an Ultralow Emission System Based on Improved Collaborative Optimization

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