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This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

The optimisation of forest fuels supply chain involves several entities actors, and particularities. To successfully manage these supply chains, efficient tools must be devised with the ability to deal with stakeholders dynamic interactions and to optimize the supply chain performance as a whole while being stable and robust, even in the presence of uncertainties. This work proposes a framework to coordinate different planning levels and event-based models to manage the forest-based supply chain. In particular, with the new methodology, the resilience and flexibility of the biomass supply chain is increased through a closed-loop system based on the system forecasts provided by a discrete-event model. The developed event-based predictive model will be described in detail, explaining its link with the remaining elements. The implemented models and their links within the proposed framework are presented in a case study in Finland and results are shown to illustrate the advantage of the proposed architecture.

This paper aims to provide decision support for precast concrete contractors about both precast concrete supply chain strategies and construction configurations.

This paper proposes a simulation-based optimisation for supply chain and construction (SOSC) during the planning phase of PC building projects. The discrete event simulation is used to capture the characteristics of supply chain and construction processes, and calculate construction objectives under different plans. Particle swarm optimisation is combined with simulation to find optimal supply chain strategies and construction configurations.

The efficiency of SOSC is compared with the parametric simulation approach. Over 70 per cent of time and effort used to simulate and compare alternative plans is saved owing to SOSC.

Building simulation model costs a lot of time and effort. The data requirement of the proposed method is high.

The proposed SOSC approach can provide decision support for PC contractors by optimising supply chain strategies and construction configurations.

This paper has two contributions: one is in providing a decision support tool SOSC to optimise both supply chain strategies and construction configurations, while the other is in building a prototype of SOSC and testing it in a case study.

Supply chain (SC) configuration has gained increased acceptance as an important issue when evaluating new customization possibilities and this evidence has contributed to the strengthening of the debate between global vs local production locations. This work contributes in enrichment of this topic by studying how local or global SC location decisions influence performances by considering a SC point of view, in terms of cost and time, in traditional and customized productions.

A discrete event simulation approach, based on experimentation through executable configurations, was used to evaluate different SC scenarios for customized as well as traditional products within the footwear industry.

The results indicated that to identify proper SC locations, existing trade-offs between the time and cost performances should be studied, avoiding the evaluation of a single performance independently and, instead, adopting a complete SC point of view while considering these performances.

This evidence has contributed to the reinforcement of the discussion between far-shore destinations vs near-shore production locations. Further studies are encouraged to adopt the present model, in which addition of other variables such as specific manufacturing competences to differentiate suppliers, both local and global suppliers, or the possibility of realizing special types of product customization required by final consumers can be done.

The paper contributes to the academic and practitioners' debate by proposing a systemic approach to assess SCs’ performances in customized contexts and to compare them to traditional collections. Results indicate that cost and time performance must find a balance that does not necessarily correspond to an exclusively local or global production.

This work contributes to the SC configuration issue by considering the trade-off between efficiency and effectiveness (i.e. SC costs and SC times) for customized productions by reviving and enriching it with an SC perspective in customization contexts.

The purpose of this paper is to investigate the benefit of pre-emptive disruption management measures for assembly systems towards the target dimension adherence to delivery times.

The research was conducted by creating simulation models for typical assembly systems and measuring its varying throughput times due to changes in their disruption profiles. Due to the variability of assembly systems, key influence factors were investigated and used as a foundation for the simulation setup. Additionally, a disruption profile for each simulated process was developed, using the established disruption categories material, information and capacity. The categories are described by statistical distributions, defining the interval between the disruptions and the disruption duration. By a statistical experiment plan, the effect of a reduced disruption potential onto the throughput time was investigated.

Pre-emptive disruption management is beneficial, but its benefit depends on the operated assembly system and its organisation form, such as line or group assembly. Measures have on average a higher beneficial impact on group assemblies than on line assemblies. Furthermore, it was proven that the benefit, in form of better adherence to delivery times, per reduced disruption potential has a declining character and approximates a distinct maximum.

Characterising the benefit of pre-emptive disruption management measures enables managers to use this concept in their daily production to minimise overall costs. Despite the hardly predictable influence of pre-emptive disruption measures, these research results can be implemented into a heuristic for efficiently choosing these measures.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

This paper evaluates an alternative queuing concept for marine container terminals that utilize a truck appointment system (TAS). Instead of having all lanes providing service to trucks with appointments, this study considers the case where walk-in lanes are provided to serve those trucks with no appointments or trucks with appointments but arrived late due to traffic congestion.

To enable the analysis of the proposed alternative queuing strategy, the queuing system is shown mathematically to be stationary. Due to the complexity of the model, a discrete event simulation (DES) model is used to obtain the average waiting number of trucks per lane for both types of service lanes: TAS-lanes and walk-in lanes.

The numerical experiment results indicated that the considered queuing strategy is most beneficial when the utilization of the TAS lanes is expected to be much higher than that of the walk-in lanes.

The novelty of this study is that it examines the scenario where trucks with appointments switch to the walk-in lanes upon arrival if the TAS-lane server is occupied and the walk-in lane server is not occupied. This queuing strategy/policy could reduce the average waiting time of trucks at marine container terminals. Approximation equations are provided to assist practitioners calculate the average truck queue length and the average truck queuing time for this type of queuing system.

This modeling facilitates the determination of control responses (or possibly reconfiguration) upon such events and the identification of which segments of the pipeline can continue to function uninterrupted. Based on this modeling, an algorithm is presented to implement the control responses and to establish this determination. In this work, the authors propose using Message Queuing Telemetry Transport (MQTT), which is an integrated method to perform the system-wide control based on message exchanging among local node controllers (agents) and the global controller (broker).

Complex manufacturing lines in industrial plants are designed to accomplish an overall task in an incremental mode. This typically consists of a sequence of smaller tasks organized as cascaded processing nodes with local controls, which must be coordinated and aided by a system-wide (global) controller. This work presents a logic modeling technique for such pipelines and a method for using its logic to determine the consequent effects of events where a node halts/fails on the overall operation.

The method uses a protocol for establishing communication of node events and the algorithm to determine the consequences of node events in order to produce global control directives, which are communicated back to node controllers over MQTT. The algorithm is simulated using a complex manufacturing line with arbitrary events to illustrate the sequence of events and the agents–broker message exchanging.

This approach (MQTT) is a relatively new concept in Cyber-Physical Systems. The proposed example of feed-forward is not new; however, for illustration purposes, it was suggested that a feed-forward be used. Future works will consider practical examples that are at the core of the manufacturing processes.

Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts.

The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production.

The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product.

The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.