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Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.

This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.

The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.

The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.

This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.

The purpose of this paper is to study the impact of bargaining powers of firms in supply chain coordination. It studies selected aspects of bargaining powers, namely, impatience, breakdown probability and outside options, and uses a bargaining-theoretic approach to analyze surplus allocation in a coordinated supply chain.

This paper proposes one-supplier one-buyer infinite horizon supply chain coordination game, where suppliers and buyers negotiate for the allocation of supply chain surplus arising out of supply chain coordination. Various aspects of the bargaining power of the negotiating parties are modeled and the paper studies impact of power levels on the results of the bargaining game.

A significance of impatience on the bargaining process and the surplus split has been established. This paper also demonstrates a rather counter-intuitive aspect of bargaining that the impatience (as perceived by the other party) can improve the bargaining position and therefore share of profits.

This paper has limited its analysis to three key components of bargaining power. Future works can study other aspects of bargaining power, namely information asymmetry, learning curve, inside options, etc. Further, the paper has considered an infinite horizon model – this assumption can be relaxed in future research.

Equations to derive optimal split of the surplus have been derived and can be leveraged to design an autonomous bargaining agent to discover equilibrium profit splits in a cloud or e-commerce setting. Further, insights from this paper can be leveraged by managers to understand their relative bargaining power and drive to obtain the best profit split.

This paper establishes that impatience (in terms of counter-offer probability) has a significant impact on the bargaining position and on the split of the surplus that the firm can get for themselves. It establishes the advantage of higher levels of impatience, provided the other party recognizes the impatience and factors it in their decision-making process.

The purpose of this paper is to properly plan the preventive maintenance schedule for multiple elevators and optimize the number of maintenance workers.The total amount of the maintenance cost consisting of the labor cost, the part cost, and the quality cost (the loss evaluated in terms of cost, to be incurred when an elevator breaks down) is to be minimized.The method is presented of setting up the optimal preventive maintenance schedule on a long‐term basis by rescheduling the contents of schedule dynamically and flexibly in accordance with the ever‐changing maintenance conditions, taking the possibility of the future occurrence of failure into consideration. From numerical experiments, the validity of the proposal procedure for planning the preventive maintenance schedule and the effectiveness of considering the possibility of the future occurrence of failure in planning the schedule are shown, and the optimal number of maintenance workers can be decided.

The purpose of this paper is to develop a stochastic detailed schedule for a preventive/scheduled/periodic maintenance program of a military aircraft, specifically a rotorcraft or helicopter.

The new model, entitled the military “periodic aviation maintenance stochastic schedule” (PAM-SS), develops a stochastic detailed schedule for a PUMA SA 330SM helicopter for the 50-h periodic inspection, using cyclic operation network (CYCLONE) and Monte Carlo simulation (MCS) techniques. The PAM-SS model identifies the different periodic inspection tasks of the maintenance schedule, allocates the resources required for each task, evaluates a stochastic duration of each inspection task, evaluates the probability of occurrence for each breakdown or repair, develops the CYCLONE model of the stochastic schedule and simulates the model using MCS.

The 50-h maintenance stochastic duration follows a normal probability distribution and has a mean value of 323 min and a standard deviation of 23.7 min. Also, the stochastic maintenance schedule lies between 299 and 306 min for a 99 per cent confidence level. Furthermore, except the pilot and the electrical team (approximately 90 per cent idle), all other teams are around 40 per cent idle. A sensitivity analysis is also performed and yielded that the PAM-SS model is not sensitive to the number of technicians in each team; however, it is highly sensitive to the probability of occurrence of the breakdowns/repairs.

The PAM-SS model is specifically developed for military rotorcrafts, to manage the different resources involved in the detailed planning and scheduling of the periodic/scheduled maintenance, mainly the 50-h inspection. It evaluates the resources utilization (idleness and queue), the stochastic maintenance duration and identifies backlogs and bottlenecks.

The PAM-SS tackles military aircraft planning and scheduling in a stochastic methodology, considering uncertainties in all inspection task durations and breakdown or repair durations. The PAM-SS, although developed for rotorcrafts can be further developed for any other type of military aircraft or any other scheduled maintenance program interval.

The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements.

The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns.

The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested.

This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems.

In real‐life applications maintenance managers often face complicated decision problems under uncertainty. This difficulty increases when they have to take conflicting objectives into account. A recent review of the literature shows that previous works consider repairable systems subject to random failures and analyse trade‐offs between the costs and the benefits of maintenance activities. The risk aversion of the maintenance decision maker may be not underlined enough. This paper aims to deal with a single component system that has to accomplish a series of missions of a given length.

The development of a maintenance strategy for this system is analysed from a risk aversion point of view. An attempt is made to highlight the attitude of a neutral decision maker versus a risk‐averse manager.

Presents a very simple framework to analyse the risk aversion effect on managers' decisions. The model confirms the observation that risk aversion implies no‐monotone relation between optimality frequencies of maintenance operations and the deformation rate of the breakdown probability.

Since the deformation rate is monotonic with time, the proposed model can be extended to derive optimal frequencies, which allow the implementation of the optimal deformation rates according to the probability law of the deformation rate δ.

Demonstrates the application of spreadsheets in simulating queuing systems with arrivals from a finite population. The problem is referred to as the machine repair problem where the members of the queue are machines that are breaking down and the servers are the technicians repairing the broken machines. The total number of machines are finite and pre‐specified. The technique for the development of the simulation is illustrated with six machines. Describes the approach for developing a generalized simulation model with any number of machines.

The purpose of this paper is to propose a hybrid-simulated annealing algorithm to address the lacunas in production logistics. The primary focus is laid on the basic understanding of the critical quandary occurring in production logistics, and subsequently research attempts are undertaken to resolve the issue by developing a hybrid algorithm. A logistics problem associated with a flow shop (FS) having a string of jobs which need to be scheduled on m number of machines is considered.

An attempt is made here to introduce and further establish a hybrid-simulated annealing algorithm (NEHSAO) with a new scheme for neighbourhood solutions generation, outside inverse (OINV). The competence in terms of performance of the proposed algorithm is enhanced by incorporating a fast polynomial algorithm, NEH, which provides the initial seed. Additionally, a new cooling scheme (Ex-Log) is employed to enhance the capacity of the algorithm. The algorithm is tested on the benchmark problems of Carlier and Reeves and subsequently validated against other algorithms reported in related literature.

It is clearly observed that the performance of the proposed algorithm is far superior in most of the cases when compared to the other conventionally used algorithms. The proposed algorithm is then employed to a FS under dynamic conditions of machine breakdown, followed by formulation of three cases and finally identification of the best condition for scheduling under dynamic conditions.

This paper proposes an hybrid algorithm to reduce makespan. Practical implementation of this algorithm in industries would lower the makespan and help the organisation to increse their profit