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As the manufacturing activities in today's industries are getting more and more complex, it is required for the manufacturing firm to have a good shop floor production scheduling to plan and schedule their production orders. An accurate scheduling is essential to any manufacturing firm in order to be competitive in global market. The paper aims to discuss these issues.

Two types of shop floors, job shop and cellular layout, were developed by using WITNESS simulation package. Consequently, the performance of forward scheduling and backward scheduling in both job shop and cellular layout was compared using simulation method, and the results were analyzed by using analysis of variance (ANOVA). Through analysis, the best scheduling approach and layout to be used by manufacturing firm in order to achieve the make-to-order (MTO) production and inventory strategy were reported.

The results from simulation show that backward scheduling in job shop layout has the lowest average throughput time, lowest lateness, and highest labour productivity than forward scheduling. While in cellular layout, forward scheduling has the lowest average throughput time, lowest lateness, and highest labour productivity than backward scheduling in all conditions. It shows that the performance of scheduling approach is different in each production layout.

Suitable scheduling approach is needed in manufacturing industry as to maximize production rate and optimize machine and process capability. This paper presents an empirical study about the assembly process of radio cassette player of one manufacturing industry in order to investigate the impact of variety of orders and different number of two workers on the performance of production scheduling approach. Forward scheduling and backward scheduling are used to schedule the production orders.

Scheduling a manufacturing system can be one of the most complex tasks in managing an operation. Planning and control systems such as just in time (JIT) can aid scheduling. However, planning and control tools require a fairly stable shopfloor environment to get the best out of them. Many system designs and schedules only consider 100 per cent reliability in machines, and do not take into account random interruptions. In this paper, a simulation model was created to investigate machine and material handling system breakdown problems in a JIT‐driven flexible manufacturing system. Results show that compromises have to be made with JIT control in order to get the best system performance.

This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

Basic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cash flow, throughout project construction.

Upon reviewing the topic of DS, a worldwide internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.

The need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use and software integration with contemporary project management practices.

Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.

Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.

The paper illustrates the importance of DS in construction, identifies the user needs and overviews the development, verification and validation of a model that supports the generation of high-quality schedules beneficial to large-scale projects.

The paper discusses the design, implementation and validation of FLEAS, a flowline environment for automated supervision of clothing manufacturing systems. The paper argues for a mixed initiative approach to system control which incorporates both a scheduling component, based on local search, and a simulation component which dynamically tests the validity of the supervisory decisions made by the scheduler.

Maintenance, an essential element of facilities management and a fundamental requisite for increasing availability and sustaining stable processes, has been the focus of technical research for decades. However, there has not been a concrete and well‐structured classification of different maintenance types that is accepted globally. The need for such a widely acceptable classification is the main incentive to delve into and create a new maintenance taxonomy. This paper aims to address this issue.

The paper gathers and reviews several examples of maintenance classifications and viewpoints from different geographical regions in the world. Afterwards, it integrates various maintenance‐related terms and terminologies with the authors' systematic‐thinking approach, systems thinking, based on which strategic thinking is formed. Consequently, this combination results in a globally acceptable systematic classification of maintenance.

The outcome of this scientific endeavour is a newly developed maintenance taxonomy, which is established according to the correct and clear‐cut use of the terminologies of strategy, policy and tactic, which correspondingly connote the art and science of what, the plan and guideline of how, and the style and methodology of how.

This paper provides maintenance and facility stakeholders with a new maintenance taxonomy based on the available terminologies and practices taking into account the conception of strategy science. Such a classification cannot only ease the technical communication in this sector but also be accepted as a global standard due its lucid and logical structure.

Aside from the literature review and comparison of different maintenance terminologies and classifications, the paper offers a new methodical classification of maintenance, which has a strong scientific foundation and can be commonly accepted as a standard in the field of maintenance and facilities management.

The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, the purpose of this paper is to present a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.

The paper builds upon our previous survey of this field which was carried out for the ten‐year period 1995‐2004. Like the previous survey, it uses Elsevier's Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case‐based reasoning (CBR), fuzzy logic (FL), knowledge‐Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.

The survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.

This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

The purpose of this paper is to provide insights into the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in New Zealand (NZ).

This study adopted narrative inquiry methodology that involved semi-structured interviews (SSI)/expert opinion and systematic literature review (SLR) data to determine the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in NZ. The data were analysed by using descriptive analysis, Likert scale and thematic analysis techniques to understand the relationship of these factors to propose a system dynamic model.

This study concludes that weather, pandemic, material, geotechnical and disaster factors highly influence while other factors such as equipment shortage, breakdown, design error, labour and event had mixed impact on the dynamic scheduling (DS) that aids on-time project delivery. The proposed system dynamic model can enhance the understanding of factors affecting DS.

SLR is limited to English literature. The limitations of an SSI and a small sample size are acknowledged.

The proposed model can reduce the uncertainty and scheduling errors during the planning phase and aid in the lesser scheduling modification during the execution phase. In practice, this study will be helpful for road contractors to understand environment, surroundings and resource in-control and out-of-control factors, overcome road construction delays, reduce cost, aid in stakeholder management and sustainable development.

The inclusion of environment, resource and surroundings factors in force majeure clauses will bring an understanding between contracting parties and in turn reduce disputes and delays and help social causes such as on-time infrastructure delivery.

For the first time in a road construction, dynamic project scheduling model that collectively included and linked environment, resource, and surroundings factors to determine the in-control and out-of-control factors for an organisation is proposed. The novelty in the paper is provided by the inclusion of the events, disasters, and pandemics influence on DS in the NZ road construction industry for the first time.