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Article
Publication date: 7 May 2019

Faqun Qi and Binghai Zhou

The purpose of this paper is to develop novel preventive maintenance (PM) modeling methods for a cold standby system subject to two types of failures: random failure and…

Abstract

Purpose

The purpose of this paper is to develop novel preventive maintenance (PM) modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure.

Design/methodology/approach

The system consists of two components and a single repair shop, assuming that the repair shop can only service for one component at a time. Based on semi-Markov theory, transition probabilities between all possible system states are discussed. With the transition probabilities, Markov renewal equations are established at regenerative points. By solving the Markov regenerative equations, the mean time from the initial state to system failure (MTSF) and the steady state availability (SSA) are formulated as two reliability measures for different reliability requirements of systems. The optimal PM policies are obtained when MTSF and SSA are maximized.

Findings

The result of simulation experiments verifies that the derived maintenance models are effective. Sensitivity analysis revealed the significant influencing factors for optimal PM policy for cold standby systems when different system reliability indexes (i.e. MTSF and SSA) are considered. Furthermore, the results show that the repair for random failure has a tremendous impact on prolonging the MTSF of cold standby system and PM plays a greater role in promoting the system availability of a cold standby system than it does in prolonging the MTSF of system.

Practical implications

In practical situations, system not only suffers normal deterioration caused by internal factors, but also undergoes random failures influenced by random shocks. Therefore, multiple failure types are needed to be considered in maintenance modeling. The result of the sensitivity analysis has an instructional role in making maintenance decisions by different system reliability indexes (i.e. MTSF and SSA).

Originality/value

This paper presents novel PM modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure. The sensitivity analysis identifies the significant influencing factors for optimal maintenance policy by different system reliability indexes which are useful for the managers for further decision making.

Details

Journal of Quality in Maintenance Engineering, vol. 25 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 7 December 2020

Binghai Zhou, Xiujuan Li and Yuxian Zhang

This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been…

Abstract

Purpose

This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.

Design/methodology/approach

First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.

Findings

Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.

Research limitations/implications

This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.

Originality/value

Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

Details

Assembly Automation, vol. 41 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 3 April 2020

Binghai Zhou and Zhexin Zhu

This paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been…

Abstract

Purpose

This paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been formulated to minimize total line-side inventory for all stations over the planning horizon by specifying the departure time, parts quantity of each delivery and the destination station.

Design/methodology/approach

An immune clonal selection algorithm (ICSA) combined with neighborhood search (NS) and simulated annealing (SA) operators, which is called the NSICSA algorithm, is developed, possessing the global search ability of ICSA, the ability of SA for escaping local optimum and the deep search ability of NS to get better solutions.

Findings

The modifications have overcome the deficiency of insufficient local search and deepened the search depth of the original metaheuristic. Meanwhile, good approximate solutions are obtained in small-, medium- and large-scale instances. Furthermore, inventory peaks are in control according to computational results, proving the effectiveness of the mathematical model.

Research limitations/implications

This study works out only if there is no breakdown of tow trains. The current work contributes to the in-plant milk-run delivery scheduling for MMALs, and it can be modified to deal with similar part feeding problems.

Originality/value

The capacity limit of line-side inventory for workstations as well as no stock-outs rules are taken into account, and the scheduling and loading problems are solved satisfactorily for the part distribution of MMALs.

Details

Assembly Automation, vol. 40 no. 3
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 9 October 2017

Binghai Zhou, Faqun Qi and Hongyu Tao

The purpose of this paper is to develop a condition-based maintenance (CBM) model for those systems subject to the two-stage deterioration including a deterioration…

Abstract

Purpose

The purpose of this paper is to develop a condition-based maintenance (CBM) model for those systems subject to the two-stage deterioration including a deterioration pitting initiation process and a deterioration pitting growth process.

Design/methodology/approach

Regarding environmental changes as random shocks, the effect of environmental changes on the deterioration process is considered. Then, non-homogeneous Poison process and non-stationary gamma process are introduced to model the deterioration pitting initiation process and the deterioration pitting growth process, respectively. Finally, based on the deterioration model, a CBM policy is put forward to obtain the optimal inspection interval by minimizing the expected maintenance cost rate. Numerical simulations are given to optimize the performance of the deteriorating system. Meanwhile, comparisons between a single-stage deterioration model and a two-stage deterioration model are conducted to demonstrate the application of the proposed approach.

Findings

The result of simulation verifies that the deterioration rate is not constant in the life cycle and is affected by the environment. Furthermore, the result shows that the two-stage deterioration model proposed makes up for the shortage of single-stage deterioration models and can effectively reduce system failures and unreasonable maintenance caused by optimistic prediction using single-stage deterioration models.

Practical implications

In practical situations, except for normal deterioration caused by internal factors, many systems are also greatly influenced by the random shocks during operation, which are probably caused by the environmental changes. What is more, most systems have self-protection ability in some extent that protects them to keep running as new ones for some time. Under such circumstances, the two-stage deterioration model proposed can effectively reduce system failures and unreasonable maintenance caused by optimistic prediction using single-stage deterioration models. In the combination with the bootstrap estimation, the paper obtains the life distributions with approximate 95 percent confidence intervals which can provide valuable information for practical system maintenance scheduling.

Originality/value

This paper presents a new CBM model for those systems subject to the two-stage deterioration including a deterioration pitting initiation process and a deterioration pitting growth process. Considering the effect of the environmental change on the system deterioration process, a two-stage deterioration model with environmental change factors is proposed to describe the system deterioration.

Details

Journal of Quality in Maintenance Engineering, vol. 23 no. 4
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 8 July 2019

Binghai Zhou and Qiong Wu

The extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing…

Abstract

Purpose

The extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation.

Design/methodology/approach

This research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced.

Findings

A set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems.

Originality/value

For the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.

Details

Engineering Computations, vol. 36 no. 6
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 12 September 2016

Binghai Zhou and Zilong Liu

Making decisions on preventive maintenance (PM) policy and buffer sizing, as is often studied, may not result in overall optimization. The purpose of this paper is to…

Abstract

Purpose

Making decisions on preventive maintenance (PM) policy and buffer sizing, as is often studied, may not result in overall optimization. The purpose of this paper is to propose a joint model that integrates PM and buffer sizing with consideration of quality loss for a degenerating system, which aims to minimize the average operation cost for a finite horizon. The opportunistic maintenance (OM) policy which could increase the output and decrease the cost of the system is also explored.

Design/methodology/approach

A joint PM and buffer size model considering quality loss is proposed. In this model, the time-based PM and the condition-based PM are taken on the upstream and the downstream machine, respectively. Further, the OM policy based on the theory of constraints (TOC) is also considered. An iterative search algorithm with Monte Carlo is developed to solve the non-linear model. A case study is conducted to illustrate the performance of the proposed PM policies.

Findings

The superiority of the proposed integrated policies compared with the separate PM policy is demonstrated. Effects of the policies are testified. The advantages of the proposed TOC-based OM policy is highlighted in terms of low-cost and high-output.

Originality/value

Few studies have been carried out to integrate decisions on PM and buffer size when taking the quality loss into consideration for degenerating systems. Most PM models treat machines equally ignoring the various roles of them. A more comprehensive and integrated model based on TOC is proposed, accompanied by an iterative search algorithm with Monte Carlo for solving it. An OM policy to further improve the performance of system is also presented.

Details

Industrial Management & Data Systems, vol. 116 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

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Article
Publication date: 6 August 2018

Tao Peng and Binghai Zhou

With regard to product variety and cost competition, just-in-time (JIT) part-supply has become a critical issue in automobile assembly lines (AALs). This paper aims to…

Abstract

Purpose

With regard to product variety and cost competition, just-in-time (JIT) part-supply has become a critical issue in automobile assembly lines (AALs). This paper aims to investigate a multiple server scheduling problem (MSSP) encountered in the JIT part-supply process of AALs. Parts are stored in boxes and allotted from the JIT-supermarket to consumptive stations with a multiple server system. The schedule is to dispatch and sequence material boxes on each server for minimizing line-side inventory levels.

Design/methodology/approach

A mixed integer linear programming (MILP) model is established to formulate the proposed MSSP to pave the way for CPLEX procedure. Considering the high complexity of MSSP, a hybrid ant colony optimization (HACO) approach is developed by integrating basic ant colony optimization (ACO) with local optimizers that comprise of a fast local search and a tailored breadth-first tree search method.

Findings

Both CPLEX and HACO approach are capable of solving small-scale instances to optimality within reasonable computation time. The proposed HACO has been well enhanced with the embedded fast local search and tailored breadth-first tree search, and it performs robustly in a statistically significant manner when applied to real-world scale instances.

Originality/value

No stock-outs constraints and weighted line-side inventory level are considered in this paper, and the MSSP is solved satisfactorily to facilitate an efficient JIT part-supply of the AAL. In terms of the algorithm design, a tree search-based local optimizer is embedded into ACO to combine the mechanisms of ACO and problem-specific optimization.

Details

Assembly Automation, vol. 38 no. 3
Type: Research Article
ISSN: 0144-5154

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Article
Publication date: 6 February 2017

Binghai Zhou and Tao Peng

This paper aims to investigate the just-in-time (JIT) in-house logistics problem for automotive assembly lines. A point-to-point (P2P) JIT distribution model has been…

Abstract

Purpose

This paper aims to investigate the just-in-time (JIT) in-house logistics problem for automotive assembly lines. A point-to-point (P2P) JIT distribution model has been formulated to specify the destination station and parts quantity of each delivery for minimizing line-side inventory levels.

Design/methodology/approach

An exact backtracking procedure integrating with dominance properties is presented to cope with small-scale instances. As for real-world instances, this study develops a modified discrete artificial bee colony (MDABC) metaheuristic. The neighbor search of MDABC is redefined by a novel differential evolution loop and a breadth-first search.

Findings

The backtracking method has efficaciously cut unpromising branches and solved small-scale instances to optimality. Meanwhile, the modifications have enhanced exploitation abilities of the original metaheuristic, and good approximate solutions are obtained for real-world instances. Furthermore, inventory peaks are avoided according to the simulation results which validates the effectiveness of this mathematical model to facilitate an efficient JIT parts supply.

Research limitations/implications

This study is applicable only if the breakdown of transport devices is not considered. The current work has effectively facilitated the P2P JIT logistics scheduling in automotive assembly lines, and it could be modified to tackle similar distribution problems featuring time-varying demands.

Originality/value

Both limited vehicle capacities and no stock-outs constraints are considered, and the combined routing and loading problem is solved satisfactorily for an efficient JIT supply of material in automotive assembly lines.

Details

Assembly Automation, vol. 37 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 3 September 2018

Binghai Zhou and Qiong Wu

The balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to…

Abstract

Purpose

The balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.

Design/methodology/approach

This research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.

Findings

Theory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.

Research limitations/implications

The additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.

Originality/value

For the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.

Details

Assembly Automation, vol. 38 no. 4
Type: Research Article
ISSN: 0144-5154

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Article
Publication date: 9 March 2015

Binghai Zhou, Jiadi Yu, Jianyi Shao and Damien Trentesaux

The purpose of this paper is to develop a bottleneck-based opportunistic maintenance (OM) model for the series production systems with the integration of the imperfect…

Abstract

Purpose

The purpose of this paper is to develop a bottleneck-based opportunistic maintenance (OM) model for the series production systems with the integration of the imperfect effect into maintenance activities.

Design/methodology/approach

On the analysis of availability and maintenance cost, preventive maintenance (PM) models subjected to imperfect maintenance for different equipment types are built. And then, a cost-saving function of OM is established to find out an optimal OM strategy, depending on whether the front-bottleneck machines adopt OM strategy or not. A numerical example is given to show how the proposed bottleneck-based OM model proceeded.

Findings

The simulation results indicate that the proposed model is better than the methods to maintain the machines separately and the policy to maintain all machines when bottleneck machine reaches its reliability threshold. Furthermore, the relationship between OM strategy and corresponding parameters is identified through sensitivity analysis.

Practical implications

In practical situations, the bottleneck machine always determines the throughput of the whole series production system. Whenever a PM activity is carried out on the bottleneck machine, there will be an opportunity to maintenance other machines. Under such circumstances, findings of this paper can be utilized for the determination of optimal OM policy with the objective of minimizing total maintenance cost of the system.

Originality/value

This paper presents a bottleneck-based OM optimization model with the integration of the imperfect effect as a new method to schedule maintenance activities for a series production system with buffers. Furthermore, to the best of the knowledge, this paper presents the first attempt to considering the bottleneck constraint on system capacity and diverse types of machines as a means to minimize the maintenance cost and ensure the system throughput.

Details

Journal of Quality in Maintenance Engineering, vol. 21 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

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