Abstract

We review dynamic quality models both in single-agent setup and in a competitive framework. Our objectives are: (1) to give the reader a vantage point on the state of the art in this area, (2) to identify the boundaries between the different concepts of quality to help build a bridge between the various communities interested in the management of quality, and (3) to sketch out a research agenda for the area. The paper should not only be of interest to active researchers in the field, but also to a broader community of scholars and practitioners working in operations management, marketing, industrial engineering and operations research, who are interested in quality dynamics.

Introduction

During the three last decades or so, a large literature has developed on product quality management, with one stream using a dynamic model to reflect the idea that firms can improve the quality of their products over time. This paper surveys this dynamic literature both in a one-firm framework and in a competitive setup. There are several reasons for adopting a dynamic model when dealing with product’s quality. First, consumers’ tastes change over time and if the firm wants to remain competitive (not to say survive) in the market, it must upgrade continuously the quality of its products. Second, changing product’s quality requires constant investments over time to, e.g., improve production processes and use better materials. Third, environmental and safety regulations change over time and so must the product’s quality about may impose some. Finally, establishing a high-quality reputation needs sustained marketing as well as operations efforts.

We all have probably witnessed two friends discussing a product quality or brand’s quality and ending in a deadlock. One likely reason for this outcome is that each party had her own personal definition of quality. Such differences in opinion also occasionally involve consumers and firms. For instance, a new product can be a true marvel from an engineering and production point of view, but be snubbed by consumers because it does not suit their tastes, and therefore be labeled a poor-quality product. Then, one can ask the following simple question: can quality be assessed through a reliable and valid measurement scale? In a seminal paper, Garvin (1988) suggested the following eight dimensions to measure this construct:

Conclusion
This paper surveyed the contributions on quality dynamics both in single-agent and in competitive situations. In this section, we propose some ideas for future investigations, with some of them being relatively straightforward extensions to what has been done, and others being more conceptual (and sometime speculative) ones.

Stochastic models: Looking at the “Model” column in the Appendix, we clearly see that the literature has largely adopted a deterministic model. Given that one can hardly argue that e.g., future demands and costs are known with certainty, the choice of a deterministic setup is, at least partially, motivated by mathematical tractability. Still, this choice has allowed researchers to obtain a series of insights into the management of quality over time. Extending most of the proposed deterministic models to a stochastic environment is conceptually easy, but it will come with the cost of losing the opportunity of having closed-form solutions. Also, depending on how uncertainty is introduced, obtaining a numerical solution may be computationally challenging, especially for multi-agent models with strategic interactions. One good place to start the analysis is to consider a two-stage model where quality decision is made once, while other decisions, e.g., pricing and advertising, are made in both periods. This would allow one to gain intuition on how uncertainties in, e.g., demand or cost, affect the quality decision in current and future periods. Next, one can move to a multistage model where quality decision is made in each period.

Abstract

     Management 4.0 assists businesses in evolving, surviving and performing in the competitive and dynamic world. This fourth revolution uses advanced technologies like Artificial Intelligence (AI), Virtual Reality (VR), Internet of Things (IoT), Robotics, Holography, Additive Manufacturing etc., for the proper management systems. These technologies facilitate working personnel and make it more appealing to complete their duties efficiently and accurately. The main aim of this paper is to understand the concept of Management 4.0, its technologies and applications for proper management systems. As Management 4.0 enhances process control, the chance of human error is reduced, leading to increased efficiency. It enables rapid and intelligent decision-making, reduces costs, accelerates growth, and raises profitability. Management 4.0 technologies and advanced data analytics are helpful to make smart supply chain management well suited to fulfil industry 4.0. Thus, to overcome various obstacles and effectively deploy Management 4.0 technologies in manufacturing industries, top management must establish a clear asset performance management plan with the help of process engineers familiar with industrial system failure occurrences and what operators need to improve. Management 4.0 involves advanced technologies, system connectivity, data collection & analysis at the organisation level. Management 4.0 is expected to be a critical component in the long-term survival of any business, either manufacturing or service-providing organisations.

Introduction

     Implementing Management 4.0 technologies provides firms with increased efficiency by connecting automation with data collecting and exchange protocols. Streamlining the procedures and boosting access to valuable data can optimise production with optimal resource utilisation. Advanced technologies are integral to Management 4.0, and their effective use could lead to better production and management. For instance, robots are an essential part of flexible automation and have many services in industrial manufacturing, including welding, assembling, plasma cutting, and finishing [1], [2], [3]. Manufacturers of all sizes may profit from robots in a variety of ways. Proper integration of Robots contributes to improved quality control, a safer work environment, decreased bottlenecks, higher output, enhanced worker satisfaction, and many more. Like this, a new factory is planned, and another powerful digital twin technology could be utilised for new products or processes. It allows for faster and more reliable innovation development with fewer physical prototypes. When a product is manufactured, or a factory is implemented, more data is generated to improve the Management 4.0 environment [4,5].

Conclusion

     Management 4.0 technologies help connect machines to the cloud and construct customised versions of their industrial IoT. These technologies are laying the groundwork for future development and scalability. The ultimate objective of Management 4.0 is to enhance and advance industrial processes. In recent years, the manufacturing business has undergone many changes. Management 4.0 is a comprehensive and unified set of technologies that can transform the entire management system. It has also become a reality in which factory automation is commonplace, and factories are more intelligent than ever before. Technologies aim to collect massive volumes of data from various sources. These provide intelligent production scheduling software, the foundation for smart autonomous decision-making. In this Management 4.0 scenario, supply chain analytics will reach new heights. Manufacturing as a service allows firms to bid on manufacturing a product or a single production step. This specialisation can improve manufacturing efficiency while lowering costs and fine-tuning the supply chain. Production and logistics operations are smartly connected, resulting in a more efficient and adaptable real-time lean manufacturing environment. This will make it easier to build smart value-creation chains that span the whole product life cycle, from conception to manufacture, usage, and maintenance to recycling. These characteristics of the Management 4.0 facilitates the managers to make data-driven decisions and set their strategic goal in that direction. Management 4.0 enabled culture to help the employee develop their own skill set that might help make the process and operations more efficient. The primary role of Management 4.0 is to motivate the managers to develop the Management 4.0 culture. In addition, Management 4.0 technologies will become extremely useful in the manufacturing process and assist with various logistics, including manufacturing, production, and shipping. This study might be helpful for proper management systems in the industries and provide proper information systems.

Abstract

Keywords

1. Introduction

2. Literature review

3. Research methodology

4. Case study

5. Results and discussions

6. Conclusions

7. Compliance with ethical standards

Declaration of Competing Interest

References

Abstract

Lean manufacturing is a profound system designed to enhance every manufacturing industry’s efficiency by reducing waste through internationally recognized tools and techniques. Manufacturing industries strive to adopt lean concepts to maximize their resources like staff, facilities, materials, and schedules to be economically effective. However, managers face difficulty selecting the appropriate lean tools out of the many available LM tools for successful lean implementation. This study suggests an innovative approach to choose suitable lean tools to maximize these essential resources. Herein the Value Stream Mapping and plant layout are considered for waste identification. Fuzzy QFD and FMEA prioritize the crucial resources concerning the defined wastes and determine the risk associated with each failure mode’s sub-element for lean application. It saves time by analyzing only the most critical resources for a successful lean implementation since its focus only on the most important resources. The applicability of the proposed approach is demonstrated through a case study of an Ethiopian shoe manufacturing firm. With the aid of future state plant layout and value stream map, total cycle time is reduced by 56.3%, lead-time is reduced by 69.7%, materials transportation distance and transportation activities are reduced by more than 75%, and workers required are reduced from 202 to 200.

1. Introduction

Companies adopt lean manufacturing (LM) principles and tools for various reasons, including global competition, an uncertain market environment, and rising customers’ expectations. The concepts of LM can make it possible to use their resources effectively and increase their competitiveness. According to Deif and ElMaraghy (2014) and Goshime, Kitaw, and Jilcha (2018), LM is characterized by doing more with less. It focuses on reducing/eliminating waste in order to increase productivity and maximize customer values (Belekoukias et al., 2014).

With the origin of the Toyota Production System, several LM techniques and tools were developed and used to achieve lean. Some of them are TPM, JIT, TQM, kaizen, kanban, production smoothing, cellular manufacturing, one-piece flow, Value Stream Mapping (VSM), and standardized work. Most of them are adopted in discrete manufacturers (Kumar & Parameshwaran, 2018). LM techniques and tools where effectively implemented in Automotive (Vamsi Krishna Jasti & Sharma, 2014), chemical (Jilcha & Kitaw, 2015), textile industry (Hodge, Goforth Ross, Joines, & Thoney, 2011), construction (Ko, 2010; Aka, Danladi Isah, Eze, & Timileyin, 2020), and healthcare industries (Barberato, Freitas, Godinho, & Francisco, 2016).

Abstract

     In the present time of innovation, conveyor is an exceptionally indispensable part and has huge significance for material handling in various process and manufacturing industries. It is made with precisions, and the expenses are relatively high and should work with better productivity. The selection of the best conveyor is a crucial task, and the designers need to recognize different attributes that will influence the functionalities of the conveyor system to limit bottlenecks in the system. Researchers have worked on strategies for finding the best optimum solution for various selection problems, but conveyor selection using selected methods is not reported. Therefore, the present work aims to the selection process of the best option for conveyor by using four selected decision-making methods such as analytical hierarchy process, technique of order preference by similarity to ideal solution, compromise ranking method and Deng’s similarity-based method. The selection is done among four alternatives based on six attributes, viz. fixed cost each hour, variable cost each hour, conveyor speed, product width, product weight and flexibility. The analytical hierarchy process is used to determine weights of the attributes based on relative importance of each attribute. It is observed that A3 conveyor is best suitable conveyor. Hence, the proposed strategies help manufacturers to examine and choose the best conveyor.

Introduction

     Nowadays, the material handling conveyors are the backbone of many industries. A conveyor system is a mechanical device that moves items from one location to another. It is utilized in a variety of processing and manufacturing industries. The different conveyor systems have been established for conveying materials or products from one location to another, depending on the speed of handling, nature, amount, size and weight of commodities to be carried. The determination of the best option of conveyor for developing a dependable and efficient conveyor system will save money and increase production and lowering the risk of workers operating it. The appropriate planning of material handling is also required for minimizing the delivery time, and it leads to lower overall expenses of the manufacturing and improves customer service and reduces inventory cost. In India, 80% of material handling is done by conveyors in most of the industries and the worldwide transport framework market size is projected to arrive at USD 10.6 billion by 2025, from an expected worth of USD 8.8 billion in 2021. Thus, various processing and manufacturing plants are improving the performance of conveyor systems. Therefore, best conveyor must be chosen to reduce impact and wear of the conveyor and thereby increase durability and strength.

Conclusions and future work

     The evaluation of available conveyor and selection of best suitable conveyor is very crucial and important decision for any process industry. The several more conveyors with different properties and specifications are also available. The selection of improper conveyor can directly or indirectly affect the productivity and efficiency of any process industry. Thus, it is important to select the best conveyor for better material handling. The present work shows the easy and logical scientific study to guide any decisionmaker for selecting any best alternative. The methodology done in the present work helps decision-maker to take qualitative decision. The four selected decision-making methods, viz. AHP, TOPSIS, VIKOR and Deng’s similarity-based methods, are used for solving the proposed problem. The AHP method is used for obtaining weights of all attributes and is applicable for the remaining methods as well. It is found from the ranks obtained using selected methods that A3 conveyor is the best suitable choice for material handling among the four alternatives. It is observed that ranking of conveyor has some deviation in the rankings due to different mathematical approaches used in the four methods. Finally, it is concluded that these methods are very helpful for making decision in complex problems.

Abstract

     In order to solve the problem of portfolio optimization, this paper proposes a method that combines multi-objective optimization and multi-attribute decision-making to solve the dual-objective portfolio optimization model with conditional value-at-risk (CVaR) measuring risk and including transaction costs. First, in the multi-objective optimization stage, a multi-population parallel NSGA-II based on sparsity strategy (SMP-NSGA-II) is proposed to obtain multiple Pareto optimal solutions of the model. Second, in the multi-attribute decision-making stage, in order to reflect different investment preferences, the Pareto optimal set obtained is clustered through the fuzzy C-means, and then the grey relational projection method is used to evaluate the solutions belonging to the same cluster to select the optimal compromise solution. Finally, a case study of 9 semiconductor stocks in China’s Shanghai and Shenzhen stock markets is carried out, and the optimal compromise portfolio under different investment preferences is given. At the same time, the proposed algorithm is compared with the other six multi-objective evolutionary algorithms (MOEAs), which verifies that the algorithm in this paper has certain competitiveness.

Introduction

     Nowadays, in the field of securities investment, the indicators used to quantify risk mainly include value-atrisk (VaR) and conditional value-at-risk (CVaR). Among them, VaR is represented by nonlinear, non-convex and nondifferentiable function with multiple local optima, making it difficult to calculate. To solve these problems, Rockafellar et al. [1] introduced the CVaR, which is a coherent risk measure that considers risk as the most serious loss in a given scenario, taking into account a certain degree of confidence. Since CVaR is a convex function, it can effectively solve the optimization problem that uses CVaR as a minimization goal or constraint [2, 3]. At the same time, Yu et al. [4] compared five different risk models and verified through experiments that using CVaR to measure risk is a good choice.

     As the complexity of practical applications continues to increase, scholars have developed various heuristic algorithms to solve portfolio optimization problems. The application of heuristic algorithms in portfolio optimization problems is divided into two categories. The first category simplifies portfolio objectives through the setting of weight coefficients [5–7], and obtains a risk-return curve by continuously changing the risk avoidance parameters of representative investors. This method has a certain degree of subjectivity. The second type uses multi-objective evolutionary algorithm (MOEA) to directly optimize risks and benefits simultaneously [8–11], and can obtain a complete effective frontier in one operation. Obviously, it is more convenient to use MOEAs to solve portfolio optimization problems.

Conclusion

     In order to solve the dual-objective portfolio optimization model with conditional value-at-risk (CVaR) as a measure of risk and including transaction costs, this paper proposes a method combining multi-objective optimization and multi-attribute decision-making. In the multi-objective optimization stage, this paper proposes a multi-population parallel NSGA-II based on sparsity strategy (SMP-NSGAII). In the case studies of 9 stocks in the semiconductor industry, we compared SMP-NSGA-II with the other six MOEAs through two performance evaluation indicators (HV and SP) and running time, then verified the feasibility of the SMP-NSGA-II algorithm. In the multi-attribute decision-making stage, this paper adopts the FCM-GRP hybrid method to give the optimal compromise investment portfolio under different DM preferences.

     There are a couple of drawbacks in the present study. Firstly, the model studied in this paper is relatively simple and does not take into account the many unstable factors of the real securities market; Secondly, the space complexity of the proposed SMP-NSGA-II algorithm is also high.

1- Introduction

2- Literature review

3- Problem formulation

4- Solving method

5- Model implementation and numerical results

6- Conclusion

References

Abstract

Medical Supply chain network design is one of the crucial planning problems that if solved efficiently and effectively, can alleviate the dangers arising from the accumulation of wastes. Furthermore, the harmful effects of medical waste, with their infectious nature in the environment, have increased justifies the significance of this research. In this article, forward and reverse supply chain network design for consumer medical products is considered taking into account the biological risks. The proposed model has two objectives: first, maximizing the profit obtained from subtracting the costs from the revenues of the supply chain. Revenues are gained from selling medical supplies to hospitals and medical wastes to recycling centers, as well as getting the permission to use sterilize services from clinics lacking a sterilization center. The costs include activation cost of inventory, sterilization and collection centers, transportation cost, purchasing cost of medical supplies, municipality cost for destruction of medical waste, etc. The second objective is to reduce biological risks. The primary source of infectious medical supplies is from disposable supplies and equipment. Thus, the second objective function is to minimize the distance and travel time between the clinic and the sterilization center. The proposed model is implemented in hospitals in Tehran’s 4th municipal district using the Bounded De Novo Programming approach which initially attempts to redesign rare sources of the problem. The issue has two types of resource and policy constraints. The policy constraints of the problem cannot be changed, which is why the bounded De Novo Programming approach is used to solve the problem. Redesigning the capacity of the sterilization center leads to the decrease of biological risks, and in addition to can causes to increasing profitability. Also, by raising the productivity of medical waste in the recycling process, the distribution center can receive more profits.

Introduction

In the past, producers and consumers were not responsible for the products used. In other words, they were careless of the post consumption process. Many of the consumed and expired products were out of place, regardless of the extent of their return. They saw a fate of including burial, burning, or destruction ahead of them. Each of the above operations would undoubtedly impose destructive effects and damage to the surrounding environment. Now, the point of view of the advancement and development of countries, people, and the government are expecting to pay attention to their funds (Guo et al. 2019). Therefore, the producers plan to reduce the production of their defective goods. Thus, organizations are working to prevent more of the waste by devising an effective and efficient system in distribution network management. In turn, it includes determining the role of existing or potential supply chain resources as well as the interactions between these entities to manage the physical flows related to the recycling of products (Rajendran and Ravindran, 2019).

1- Introduction

2- WL/SL properties

3- Background: CDFs for link failure time, link property value at link failure, and time at which LOAS occurs

4- CDFs for failure time margins

5- CDFs for system property values at LOAS

6- CDFs for margins based on WL and SL property values

7- CDFs for margins involving only SL property values

8- Summary discussion

References

Abstract

Representations for margins associated with loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems involving multiple time-dependent failure modes are developed. The following topics are described: (i) defining properties for WLs and SLs, (ii) background on cumulative distribution functions (CDFs) for link failure time, link property value at link failure, and time at which LOAS occurs, (iii) CDFs for failure time margins defined by (time at which SL system fails) − (time at which WL system fails), (iv) CDFs for SL system property values at LOAS, (v) CDFs for WL/SL property value margins defined by (property value at which SL system fails) − (property value at which WL system fails), and (vi) CDFs for SL property value margins defined by (property value of failing SL at time of SL system failure) − (property value of this SL at time of WL system failure). Included in this presentation is a demonstration of a verification strategy based on defining and approximating the indicated margin results with (i) procedures based on formal integral representations and associated quadrature approximations and (ii) procedures based on algorithms for sampling-based approximations.

Introduction

Representations for margins associated with loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems [1-6] involving multiple time-dependent failure modes are developed. As described in Ref. [7], the descriptor “loss of assured safety” for the performance under accident conditions (e.g., a fire) of a high consequence system with multiple WLs and SLs is used for the situation in which (i) a necessary set of SL failures places the system in a potentially operational condition before (ii) an appropriate set of WL failures places the system in an inoperable state. In contrast, safety is “assured” if (i) an appropriate set of WL failures places the system in an inoperable state before (ii) a necessary set of SL failures places the system in a potentially operational condition. Consistent with the preceding description, LOAS does not mean that the accident will progress to operation of the high consequence system; rather, it only signifies that certain conditions exist that could contribute to this progression.

1- Introduction

2- Literature review

3- Problem description and model formulation

4- Case study

5- Results and discussion

6- Conclusions and suggestions

References

Abstract

In recent years, coal power has obvious advantages in terms of safety, economy and stability. As people gradually realize the importance of the environment, low carbon has become one of the core indicators of power system evaluation, so it is particularly important to guide the coal enterprises to achieve a win-win situation of economic and environmental benefits. Combined with the carbon emission policies, this paper takes the coal supply chain network as the research object and aims to develop a comprehensive decision model for sustainable design of coal supply chain. First, this paper extracts the influencing factors of low-carbonization in coal supply chain and constructs an interpretative structural model (ISM). The results show that low-carbon green technology, low-carbon production cost, utilization level of green energy and energy efficiency are the most basic manifestations of low-carbonization of coal supply chain and interact with each other, therefore this paper builds an optimization model with the objective of minimizing the low-carbon production costs in the coal supply chain. Concurrently, this paper introduces four policies (emission cap, carbon tax, carbon trade and carbon offset) as constraint condition. Then, in the aspect of model solving, this paper combines the differential evolution (DE) strategy to mutate, cross and select the leaders generated in each iteration of the salp swarm algorithm (SSA), further increases the diversity of the salp swarm, avoids the algorithm falling into a local optimum, and proposes the salp swarm algorithm based on differential evolution (DE-SSA). Finally, this paper compares the effects of four different carbon emission policies on the optimization of coal supply chain network through empirical analysis, and finds that coal enterprises have the best emission reduction effect under the carbon trade policy. The development and implementation of this paper not only enriches the related research of efficient supply chain, but also provides scientific and quantifiable decision-making technology for coal enterprises.

Introduction

The ecological concept of “low energy-consumption, lowpollution and low-emission” has reached a consensus in the world. Since the concept of “low-carbon economy” was first put forward by the British government in the energy white paper in 2003, carbon emissions in economic activities have attracted wide attention in the world, especially in China (Yang et al., 2016). In 2009, the Chinese government promised that carbon intensity would be reduced 40% – 50% of the total volume in 2005 before 2020. In 2015, China proposed to achieve emission peaks around 2030 and strive for early realization in the Intended Nationally Determined Contribution (Su and Sun, 2019a). In 2016, China’s “the 13th Five-Year Plan of Work on Controlling Greenhouse Gas Emissions” stipulated that by 2020, carbon intensity would be 18% lower than that in 2015, and carbon emissions would be effectively controlled (Liu et al., 2017). Currently, carbon emission policies implemented in the world include the United Nations Framework Convention on Climate Change, European Union’s Emissions Trading Scheme (EU-ETS), American Clean Energy and Security Act (ACESA) and China’s “the Guidance on the Development of LowCarbon Economy”, etc. At the same time, due to the influence of resource environment, industrial policy and technological development, thermal power has been playing a leading role in China’s power supply structure.

1- Introduction

2- Methodology

3- Application of the model to design of RAF

4- Results and discussion

5- Conclusion

References

Abstract

In this article, a novel hybridized Multi-Attribute Decision Model (MADM) is developed to identify an optimal design of a Reconfigurable Assembly Fixture (RAF) from a set of alternative design concepts. The model combines the comparative advantage of Fuzzy Analytic Hierarchy Process (FAHP) and the computational strength of the Fuzzy Weighted Average (FWA) based on left and right scores in order to obtain aggregates for the design alternatives considering the relative importance of the design criteria as needed in the optimal design. The model was applied to evaluate four design concepts of a RAF with six design features having numerous sub-features. Results obtained from the evaluation process shows that there are differences in final values of the design alternatives. However, a close variation exists between these values. These differences can be accrued to the interrelationships between the design features and sub-features obtained from the Fuzzy Synthetic Extent (FSE) of the FAHP and an unambiguity judgment of the FWA when aggregating availability of the design features and sub-features in the design alternatives.

Introduction

Robust design of Products and industrial machineries is important from conceptualization to manufacturing and up till usage in order for manufacturers to obtain a share of the competitive market flooded with changeable designs (Olabanji, 2018). The need for these robust designs calls for development of different design concepts of a particular product or machine before a detail design analysis can be done (Song et al., 2013). The availability of alternative design concepts necessitates the need for selection of optimal design concept (Wei and Chang, 2008). Concept selection in engineering design has attracted importance in recent times because it has a direct implication on the quality of the final design. Problems that arises when it is not properly done includes; repetitive alterations and modifications of drafted designs, prolonged developmental time and amplified cost of actualization (Tiwari et al., 2017). In order to arrive at robust design of a new product or engineering component, identification of design attributes and sub features viz a viz the numerous functional requirements from the customers or intended end users becomes the first task (Ayag and Ozdem, 2007; Brackea et al., 2017). The engineering design process attempts to give a holistic approach to identification of the design attributes, sub features and functional requirements. It follows an established design standard by proposing four phases (product planning and clarification of task, conceptual design, embodiment design and detail design). These phases are usually applied to arrive at a detail design of the new product (Yeo et al., 2004; Olabanji and Mpofu, 2014). Also, the engineering design process can be imagined to have a set of eleven steps as described by (Ayag and Ozdem, 2007). The relationship between these two analogies is described in Figure 1 where these four phases are disintegrated into the eleven steps. It may be assumed that the information needed in each step and phase will also follow the same manner. However, since the concept selection step is a decision-making process, adequate information is needed for successful selection process. Concept selection in engineering design can be modelled as a multicriteria decision-making (MCDM) problem since it involves multiple design attributes that are having different sub features. Considering the steps in the engineering design process as an all-inclusive approach, it is possible to develop a relationship between the constraints, design attributes and sub features for determining optimal design concept using the multi-criteria analysis as presented in Figure 2.

1- Introduction

2- Literature review

3- An integrated maintenance model

4- An improved PSO

5- Computational experiments

6- Conclusions

References

Abstract

To decrease the over-maintenance and under-maintenance phenomenon, this paper examined the relationship between facility reliability and lifespan so as to generate the optimal maintenance plan which includes the proper combination of maintenance time and maintenance modes. An age reduction maintenance model is built to describe the deterioration situation of the port facility, and a particle swarm optimization (PSO) based integrated approach is used to solve the model. The sensitivity analysis helps to determine the maintenance mode combination from which the total maintenance cost is minimized. The result shows that taking multiple maintenance modes into account would help optimize the total maintenance cost. In addition, the obvious difference between the reliability thresholds would assist in providing discrepant maintenance service. The results have indicated that when the thresholds of different maintenance mode are overlapped, the total maintenance cost is the highest.

Introduction

In recent years, the port economy gains a prosperous development, and the life cycle of port devices are designed to be longer, making facility management a critical part among the port management stream. The environmental and economic concerns become high, among which, the facility maintenance plays an important role. Most of the port facilities are exposed to a mixed environment of the windy, sunny, salty and high load operation. Any facility is a necessary link in the port operation chain, and the interruption of any link will cause a great damage to the port operation efficiency. Every year, about 11% of operating cost is used for maintenance, and this number has been increasing at an annual rate of 4% in recent years with the increase of port throughput [7]. Furthermore, according to the report on China port development [2], around twelve percent of the port machine OEM’s revenue is coming from the maintenance activities, where the maintenance costs for crane are growing at double-digit rate annually [13]. To deal with these growing cost, an appropriate facility maintenance scheduling becomes essential both for port operators and port machine manufacturers. Normal overcapacity often characterizes port operation due to the high facility cost. This causes port facilities are always operated either far beyond or below their full capacity subjected to the resilient loading plan. Such overcapacity or under-capacity usually results from various facility type and fluctuating workload. The variable facility type results in the wear and tear at varying degrees even when they are assigned the same workload. The workload fluctuation, either due to field dynamics or demand dynamics, leads to strong fluctuation in tear mix and deterioration extent. Different types of facilities support smooth port operation, while outdoor environments intensifies serious wear and tear on the facility. A common practice is, therefore, to apply maintenance whenever possible to postpone or avoid excessive wear and tear [8]. Since maintenance recovers the reliability in some measures, their consideration has attracted increased attention with highlighted focus on reasonable maintenance mode. Various maintenance modes influence aging rates and thus impacts the next maintenance activities, such as maintenance interval, technique, and cost.