Lean production of ship-pipe parts based on lot-sizing optimization and PFB control strategy

Kybernetes ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fuli Zhou ◽  
Panpan Ma ◽  
Yandong He ◽  
Saurabh Pratap ◽  
Peng Yu ◽  
...  

Purpose With an increasingly fierce competition of the shipbuilding industry, advanced technologies and excellent management philosophies in the manufacturing industry are gradually introduced to domestic shipyards. The purpose of this study is to promote the lean management of Chinese ship outfitting plants by lean production strategy. Design/methodology/approach To promote the lean implementation of Chinese shipyards, the lean practice of ship-pipe part production is highlighted by lot-sizing optimization and strategic CONWIP (constant work-in-process) control. A nonlinear programming model is formulated to minimize the total cost of ship-pipe part manufacturing and the particle swarm optimization (PSO)-based algorithm is designed to resolve the established model. Besides, the pull-from-the-bottleneck (PFB) strategy is used to control ship-pipe part production, verified by Simulink simulation. Findings Results show that the proposed lean strategy of the programming model and strategic PFB control could assist Chinese ship outfitting plants to leverage competitive advantage by waste reduction and lean achievement. Specifically, the PFB double-loop control strategy shows better performance when there is high productivity and the PFB single-loop control outperforms at lower productivity scenarios. Practical implications To verify the effectiveness of the proposed lean strategy, a case study is performed to validate the formulated model. Also, simulation experiments realized by FlexSim software are conducted to testify results obtained by the constructed programming model. Originality/value Lean production management practice of the shipyard building industry is performed by the proposed lean production strategy through lot-sizing optimization and strategic PFB control in terms of ship-pipe part manufacturing.

2020 ◽  
Vol 15 (4) ◽  
pp. 1363-1387
Author(s):  
Mohammad Saeid Atabaki ◽  
Seyed Hamid Reza Pasandideh ◽  
Mohammad Mohammadi

Purpose Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs. Design/methodology/approach The problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO. Findings Taking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible. Originality/value Developing a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.


2021 ◽  
Author(s):  
Waldemar Kaczmarczyk

Abstract The planning horizon of small bucket models is often divided into many fictitious micro-periods, with non-zero demand only in the last micro-period of each real (macro-)period. On the one hand, such models ensure schedules with short cycle times and low work-in-process inventory in multilevel systems; on the other, they make setup times that are longer than a single period more likely. This paper presents a new mixed-integer programming model for the case with setup operations that overlap multiple periods. The new model assumes that the capacity is constant in the whole planning horizon and explicitly determines the entire schedule of each changeover. Moreover, a two-level MIP heuristic is presented that uses model-specific cuts to fix a priori some minor decisions. The results of the computational experiments show that the new model and MIP heuristic require a substantially smaller computational effort from a standard MIP solver than the known models.MSC Classification: 90B30 , 90C11


2014 ◽  
Vol 722 ◽  
pp. 182-189
Author(s):  
Li Gang Ma ◽  
Chang Le Xiang ◽  
Tian Gang Zou ◽  
Fei Hong Mao

The paper proposes a cascade control strategy of speed feedback in inner loop and temperature feedback in outer ring for hydro-viscous driven fan cooling system, and compares the simulation of PID and fuzzy PID. The simulation result shows that the double-loop control system while the response time longer, but much smaller overshoot, can achieve a good feedback to adjust the fan speed and temperature and realize stepless speed regulation of hydro-viscous driven fan cooling system under the premise of stability for fan speed and system temperature.


2017 ◽  
Vol 2 (2) ◽  
pp. 114-125 ◽  
Author(s):  
Jianfeng Zheng ◽  
Cong Fu ◽  
Haibo Kuang

Purpose This paper aims to investigate the location of regional and international hub ports in liner shipping by proposing a hierarchical hub location problem. Design/methodology/approach This paper develops a mixed-integer linear programming model for the authors’ proposed problem. Numerical experiments based on a realistic Asia-Europe-Oceania liner shipping network are carried out to account for the effectiveness of this model. Findings The results show that one international hub port (i.e. Rotterdam) and one regional hub port (i.e. Zeebrugge) are opened in Europe. Two international hub ports (i.e. Sokhna and Salalah) are located in Western Asia, where no regional hub port is established. One international hub port (i.e. Colombo) and one regional hub port (i.e. Cochin) are opened in Southern Asia. One international hub port (i.e. Singapore) and one regional hub port (i.e. Jakarta) are opened in Southeastern Asia and Australia. Three international hub ports (i.e. Hong Kong, Shanghai and Yokohama) and two regional hub ports (i.e. Qingdao and Kwangyang) are opened in Eastern Asia. Originality/value This paper proposes a hierarchical hub location problem, in which the authors distinguish between regional and international hub ports in liner shipping. Moreover, scale economies in ship size are considered. Furthermore, the proposed problem introduces the main ports.


2015 ◽  
Vol 27 (1) ◽  
pp. 19-33 ◽  
Author(s):  
Annika Lantz ◽  
Niklas Hansen ◽  
Conny Antoni

Purpose – The purpose of this paper is to explore job design mechanisms that enhance team proactivity within a lean production system where autonomy is uttermost restricted. We propose and test a model where the team learning process of building shared meaning of work mediates the relationship between team participative decision-making, inter team relations and team proactive behaviour. Design/methodology/approach – The results are based on questionnaires to 417 employees within manufacturing industry (response rate 86 per cent) and managers’ ratings of team proactivity. The research model was tested by mediation analysis on aggregated data (56 teams). Findings – Team learning mediates the relationship between participative decision-making and inter team collaboration on team proactive behaviour. Input from stakeholders in the work flow and partaking in decisions about work, rather than autonomy in carrying out the work, enhance the teams’ proactivity through learning processes. Research limitations/implications – An investigation of the effects of different leadership styles and management policy on proactivity through team-learning processes might shed light on how leadership promotes proactivity, as results support the effects of team participative decision-making – reflecting management policy – on proactivity. Practical implications – Lean production stresses continuous improvements for enhancing efficiency, and such processes rely on individuals and teams that are proactive. Participation in forming the standardization of work is linked to managerial style, which can be changed and developed also within a lean concept. Based on our experiences of implementing the results in the production plant, we discuss what it takes to create and manage participative processes and close collaboration between teams on the shop floor, and other stakeholders such as production support, based on a shared understanding of the work and work processes. Social implications – Learning at the workplace is essential for long-term employability, and for job satisfaction and health. The lean concept is widely spread to both public bodies and enterprises, and it has been shown that it can be linked to increased stress and an increase in workload. Finding the potential for learning within lean production is essential for balancing the need of efficient production and employees’ health and well-being at work. Originality/value – Very few studies have investigated the paradox between lean and teamwork, yet many lean-inspired productions systems have teamwork as a pillar for enhancing effectiveness. A clear distinction between autonomy and participation contributes to the understanding of the links between job design, learning processes and team proactivity.


Author(s):  
William J. Emblom ◽  
Klaus J. Weinmann

This paper describes the development and implementation of closed-loop control for oval stamp forming tooling using MATLAB®’s SIMULINK® and the dSPACE®CONTROLDESK®. A traditional PID controller was used for the blank holder pressure and an advanced controller utilizing fuzzy logic combining a linear quadratic gauss controller and a bang–bang controller was used to control draw bead position. The draw beads were used to control local forces near the draw beads. The blank holder pressures were used to control both wrinkling and local forces during forming. It was shown that a complex, advanced controller could be modeled using MATLAB’s SIMULINK and implemented in DSPACE CONTROLDESK. The resulting control systems for blank holder pressures and draw beads were used to control simultaneously local punch forces and wrinkling during the forming operation thereby resulting in a complex control strategy that could be used to improve the robustness of the stamp forming processes.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Liu ◽  
Jiahong Xu ◽  
Yuhong Liu

Purpose The purpose of this research on the control of three-axis aero-dynamic pendulum with disturbance is to facilitate the applications of equipment with similar pendulum structure in intelligent manufacturing and robot. Design/methodology/approach The controller proposed in this paper is mainly implemented in the following ways. First, the kinematic model of the three-axis aero-dynamic pendulum is derived in state space form to construct the predictive model. Then, according to the predictive model and objective function, the control problem can be expressed a quadratic programming (QP) problem. The optimal solution of the QP problem at each sampling time is the value of control variable. Findings The trajectory tracking and point stability tests performed on the 3D space with different disturbances are validated and the results show the effectiveness of the proposed control strategy. Originality/value This paper proposes a nonlinear unstable three-axis aero-dynamic pendulum with less power devices. Meanwhile, the trajectory tracking and point stability problem of the pendulum system is investigated with the model predictive control strategy.


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