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Article
Publication date: 30 January 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

Article
Publication date: 6 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

Article
Publication date: 28 May 2021

Changpu Ma and Binghai Zhou

The use of multiple-capacity rail-guided vehicles (RGVs) has made automated storage and retrieval system (AS/RS) optimization more complex. The paper performs dual-RGV…

224

Abstract

Purpose

The use of multiple-capacity rail-guided vehicles (RGVs) has made automated storage and retrieval system (AS/RS) optimization more complex. The paper performs dual-RGV scheduling considering loading/unloading and collision-avoidance constraints simultaneously as these issues have only been considered separately in the previous literature.

Design/methodology/approach

This paper proposes a novel model for dual-RGV scheduling with two-sided loading/unloading operations and collision-avoidance constraints. To solve the proposed problem, a hybrid harmony search algorithm (HHSA) is developed. To enhance its performance, a descent-based local search with eight move operators is introduced.

Findings

A group of problem instances at different scales are optimized with the proposed algorithm and the results are compared with those of two other high-performance methods. The results demonstrate that the proposed method can efficiently solve realistically sized cases of dual multi-capacity RGV scheduling problems in AS/RSs.

Originality/value

For the first time in the research on dual multi-capacity RGV scheduling in an AS/RS, two-sided loading/unloading operations and collision avoidance constraints are simultaneously considered. Furthermore, a mathematical model for minimizing the makespan is developed and the HHSA is developed to determine solutions.

Article
Publication date: 20 April 2022

Binghai Zhou, Qi Yi, Xiujuan Li and Yutong Zhu

This paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism…

46

Abstract

Purpose

This paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.

Design/methodology/approach

A chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.

Findings

The involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.

Originality/value

This research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

Details

Engineering Computations, vol. 39 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 15 April 2022

Binghai Zhou, Jihua Zhang and Qianran Fei

Facing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an…

Abstract

Purpose

Facing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an important prerequisite to control energy consumption. This paper aims to present a bi-objective green in-house transportation scheduling and fleet size determination problem (BOGIHTS&FSDP) in automobile assembly line to schedule the material delivery tasks, which jointly take the energy consumption into consideration as well.

Design/methodology/approach

This research proposes an optimal method for material handling in automobile assembly line. To solve the problem, several properties and definitions are proposed to solve the model more efficiently. Because of the non-deterministic polynomial-time-hard nature of the proposed problem, a Multi-objective Discrete Differential Evolution Algorithm with Variable Neighborhood Search (VNS-MDDE) is developed to solve the multi-objective problem.

Findings

The performances of VNS-MDDE are evaluated in simulation and the results indicate that the proposed algorithm is effective and efficient in solving BOGIHTS&FSDP problem.

Originality/value

This study is the first to take advantage of the robot's interactive functions for part supply in automobile assembly lines, which is both the challenge and trend of future intelligent logistics under the pressure of energy and resource. To solve the problem, a VNS-MDDE is developed to solve the multi-objective problem.

Details

Engineering Computations, vol. 39 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 8 March 2021

Binghai Zhou and Shi Zong

The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to…

Abstract

Purpose

The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks.

Design/methodology/approach

This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time.

Findings

Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window.

Research limitations/implications

The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies.

Originality/value

For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

Details

Engineering Computations, vol. 38 no. 8
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 1 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

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

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

Article
Publication date: 14 June 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

Keywords

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