Fleet Productivity Research Articles

Development of a Fleet IoT-Based System Management in Institutions

IoT-based systems are increasingly important for managing assets and activities, and implementing such a system can greatly benefit institutions like WASAC. The purpose of this system is to effectively manage WASAC's fleet, improving vehicle tracking, dispatching, fuel management, maintenance planning, driver behavior and safety, accident reduction, vehicle lifespan, customer service, unauthorized vehicle usage control, and data analysis and reporting. Currently, WASAC relies on paper-based methods for data recording and fuel management, resulting in financial losses and inadequate driver monitoring. By developing an IoT-based system, several sensors and devices such as a CAN adapter FMB140, automatic alcohol test sensor, fuel sensor, relay, I-button, and buzzer will be employed. These devices and sensors will be connected to the FMB140 IoT device, which will capture data and store them in a server database. This data will then be used to create an interactive web-based application using socket programming for fleet managers and the monitoring team. The application will be developed using PHP and React technologies. By implementing this IoT in fleet management will enhance safety and security measures for institutions specifically within the WASAC fleet management system, Real-time tracking, geofencing, prevention of fuel theft, driver behavior monitoring, and remote engine cut-off will significantly improve driver and vehicle safety while boosting fleet productivity. Overall, the IoTbased system will streamline operations, reduce costs, and enhance efficiency for WASAC's fleet management.

Economic of Small-Scale Hand Line Fishery Fleet Production Based On Driving Power in Every Season in The Small Island Park Conservation Area, Southeast Maluku Regency

The economic benefits of hand line production activities carried out by small fishermen utilizing some inputs to produce high economic value output each season in the Kei Kecil small island park (TPK) conservation area differ according to the driving power. Production cost efficiency, effectiveness of production results, and business profits are very important for the sustainability and development of small-scale hand-line fisheries. This study aims to analyze the economics of small-scale hand line production based on the driving power in each season in the TPK Kei conservation area. The type of data needed is primary and secondary data, and the collection method used is triangulation. The analysis used includes analysis of business profits, production cost efficiency, and effectiveness of production results. The results of the analysis show that the production of hand line carried out in the TPK Kei Kecil conservation area in each season is more efficient and effective in the hand line fishing fleet that uses hand line rowing and the highest business profit is the hand line fishing fleet that uses ketinting driving power. The production of small-scale hand lines in the TPK Kei conservation area is small in each season efficiently, effectively, and profitably.

THE HARMONIZING MINE PLANNING IN THE EAST KALIMANTAN COKING COAL MINE: THE CHALLENGES AND IMPROVEMENTS

The coking coal mine at the research site is in the Pamaluan formation East Kalimantan. The condition of the mine has many complexities and challenges, such as natural slope condition (30º-35º), narrow pit area and low out pit dump capacity limited by land compensation issue, overburden material, geological structure, coal with very thin layers (<1 m) and consist multiple products in a layer, small number of strips and large number of blocks and high SR pit and SR balance in short range. In mine planning at the research location, many improvements need to be made so that all aspects, starting with mine planning and operations, can run smoothly and profits can be as expected. Some of the improvements that have been made at the mine such as build mine planning concepts, build fleet allocation rules, structural and stratigraphical geology modelling and improve drill & blast plan, implementation dynamic mine planning and optimize mining digital product. By implementing these improvements, significant positive impacts have been achieved, such as Consistently maintain mine planning compliance (accuracy) >97%, Maintain fleet productivity performance in range of 95% - 110%, Consistently achieving production targets >97%, Able to maintain SR balance by achieving weekly coal plan with the range of 94 - 103%, Achieving blasting performance results with a value of >97% and Maintain work safety aspects with no accidents and loss time injury.

Forecasting shipbuilding demand using shipping market modeling: A case study of LNGC

Volatile and cyclical shipbuilding demand has consistently been a risk for the shipbuilding industry. This study analyzes the shipping market from a macro perspective and proposes a model capable of long-term shipbuilding demand forecasting. Initially, a system dynamics model representing the shipping market based on maritime economy theory is presented, comprising five main components: external variables, the freight market, shipyards, fleet productivity, and demolition. Based on this system dynamics model, a case study was conducted using LNG carrier market data. The prediction results of the model were compared with seven other time series forecasting models, demonstrating its validity. Finally, scenario analyses evaluated the impact of changes in cargo transport demand, shipyard supply capacity, and carbon regulations. The findings indicated that while increases in transport demand and stricter regulations enhance the amplitude of the shipbuilding demand cycle, increased shipyard supply capacity mitigates these cycles, raising both the period and trough of the cycle.

Multilevel analysis of bulk shipping fleet productivity: a big data approach

ABSTRACT Fleet productivity is an important element in shipping economics, a defining factor in the asset’s profitability, and a key driver of the emission footprint of the industry. This paper analyzes the productivity of bulk vessels in a multi-dimensional framework by utilizing over 800,000 voyages data which were carried out by 17,764 vessels over a 7-year horizon and was derived from the Automated Identification System. An examination of the underlying factors driving transportation output provides further understanding of how the operational behaviours impact vessel productivity. The insights from this research have important implications for policy making in investment, operational enhancement, and emission reduction.

Reinforcement Learning-Based Fleet Dispatching for Greenhouse Gas Emission Reduction in Open-Pit Mining Operations

In typical mining operations, more than half of the direct greenhouse gas (GHG) emissions come from haulage fuel consumption. Smarter truck fleet dispatching is a feasible and manageable solution to reduce direct emissions with existing equipment. Conventional scheduling-based and human-led dispatching solutions often cause lower efficiency that wastes resources and elevates emissions. In this study, a simulated environment is developed to enable testing smarter real-time dispatching systems, Q-learning as a model-free reinforcement learning algorithm is used to improve fleet productivity, decrease waiting time and, consequently, reduce GHG emissions. The proposed algorithm trains the fleet to make better decisions based on payload, traffic, queueing, and maintenance conditions. Results show that this solution can reduce GHG emissions from haulage fuel consumption by over 30% while achieving the same production levels as compared to fixed scheduling. The proposed solution also shows advantages in handling operational randomness and balancing fleet size, productivity, and emissions.

Engineering Poster E8: Dynamic Risk Advisor – real time bowtie risk assessment monitoring system

Poster E8 This paper describes the development of a Dynamic Risk Advisor to improve safety of operations in fleet marine construction and production oil and gas vessels, well, and subsea equipment in process safety management, providing rig management onshore and offshore tools to assess health, safety, and environment (HSE) performance in real time. This tool uses the bowtie methodology, and provides a useful framework to present a risk ‘picture’ and to satisfy management of health and safety at work regulations. The focus of the methodology is the relationship between safeguards (control systems/equipment, process controls, and human factors) and an overview of the safety process monitoring in a simple and visual form. As facilities and equipment age, those safeguards may become less effective or degraded (different from the condition at the moment of risk assessment creation/revision), risks may increase and that is why they must be monitored using an effective, continuous, and temporal-aware method. Ocyan and Viasat/Intelie created a solution to identify and analyse the hazards dynamically (in real time) using the LIVE streaming data platform, providing an interface that allows the client to manage the current risk profile of their assets using a bowtie diagram. Additionally, this also provides a way for the user to assess the effect of degraded safeguards on complex operations in real time, by monitoring the actual residual risk. The study started in 2019 and was developed together with the Ocyan HSE team and was extended to all fleet due to the positive results. The tool can be accessed from mobile phones and tablets. To access the poster click the link on the right. To read the full paper click here

Dynamic Risk Advisor – real time bowtie risk assessment monitoring system

This paper describes the development of a Dynamic Risk Advisor to improve safety of operations in fleet marine construction and production oil and gas vessels, well, and subsea equipment in process safety management, providing rig management onshore and offshore tools to assess health, safety, and environment (HSE) performance in real time. This tool uses the bowtie methodology, and provides a useful framework to present a risk ‘picture’ and to satisfy management of health and safety at work regulations. The focus of the methodology is the relationship between safeguards (control systems/equipment, process controls, and human factors) and an overview of the safety process monitoring in a simple and visual form. As facilities and equipment age, those safeguards may become less effective or degraded (different from the condition at the moment of risk assessment creation/revision), risks may increase and that is why they must be monitored using an effective, continuous, and temporal-aware method. Ocyan and Viasat/Intelie created a solution to identify and analyse the hazards dynamically (in real time) using the LIVE streaming data platform, providing an interface that allows the client to manage the current risk profile of their assets using a bowtie diagram. Additionally, this also provides a way for the user to assess the effect of degraded safeguards on complex operations in real time, by monitoring the actual residual risk. The study started in 2019 and was developed together with the Ocyan HSE team and was extended to all fleet due to the positive results. The tool can be accessed from mobile phones and tablets.

The Effect of Freight Rates on Fleet Productivity: An Empirical Research on Dry Bulk Market

Fleet productivity increases in two directions. First one is achieved by increasing the speed of the vessels in the market conditions where high freight rates are observed, this increases the amount of cargo per unit capacity they carry at the unit time. The other one is related to the short run inelastic supply curve in shipping because of the time to build effect. When the demand increases occur, the amount of cargo carried per unit capacity increases since the increase in supply is limited in the short run. In this context, it is determined the relationship between freight rates and the amount of cargo carried per unit capacity in this study. The Baltic Dry Index (BDI) was selected as a measure of the freight rates, and the tonnage carried per DWT from the portion of the total cargo tonnage carried by the sea to the dry cargo fleet capacity during that year was selected as an indicator of the fleet productivity. The dataset used in the study consists of annual observations covering the period from 1985 to 2020. Correlation and regression methods were used to determine the econometric relationship between the variables. As a result of the study, a significant strong relationship was found between freight rates and productivity in the positive direction. According to the developed model, a 10% increase in the freight rate causes an increase of about 1.3% in fleet productivity.

The Rise and the Fall of the Shipping Industry in 2003-2008 and 2009-2018: The Interrelationship of 7 Maritime Markets

The main objective of this work was to present the role of 7 maritime markets together, and especially as they stood during the recent exceptional boom (2003-2008) and the long depression (2009-2018). These markets were: shipbuilding, seaborne trade, freight rate, 2nd hand ship market, demolition, ships laid-up and ports & canals. Usually, maritime economists dealt with one market, or at a maximum with three or four but separately, and so the reader had no opportunity to understand the whole picture. We considered also ourselves lucky to analyze a period where a boom has been followed immediately by a depression! This finding, we believe, will be useful for future as we showed—that the industry is unpredictable as well as cyclical… The world’s fleet productivity presented for the first time, and we used it to predict the seaborne trade in 2023. One market which has not received the full attention it deserved, was demolition one—where here it has been analyzed as fully as possible. Finally, we presented the work done by Ports and Canals1 usually treated separately by the maritime economists! Our method was that of the economic analysis of the Supply and Demand determining whatever price is involved.

Strengthening European Union fisheries by removing harmful subsidies

Harmful fisheries subsidies have historically contributed to fleet overcapacity and continue to be allocated to the fishing industry to artificially maintain its profitability. However, in this contribution we show that removing harmful subsidies and reducing overfishing will help to recover the resource biomass, subsequently leading to increased levels of sustainable catches, income and well-being of fishers, and reduces inequities in income and consumption when fish stocks are not effectively managed. Maintaining harmful fisheries subsidies is socially and economically inefficient. Taking the example of the EU fishing fleet, one of the largest fishing fleets in the world, we use the total factor productivity to show that small-scale fishing fleet's productivity is almost two-fold in the North Atlantic and 16% higher in the Mediterranean and Black seas compared to large-scale vessels. This result is explained because the harmful fisheries subsidies disproportionately allocated to large-scale vessels introduce distortions in the efficient allocation of inputs. With critical WTO negotiations ongoing regarding the global rules on fisheries subsidies, the EU must take advantage of the opportunity to lead a desirable transformative change while also supporting developing nations to truly achieve global sustainable and equitable fisheries.

Integration of simulation and dispatch modelling to predict fleet productivity: an open-pit mining case

ABSTRACT Predicting the fleet requirement based on fleet productivity estimation is one of the critical parts of a robust long-term mine plan. The dispatch logic that determines the return destination of the empty trucks is significantly important in the overall full and empty travel distances and trucks’ productivity. In this paper, a Monte-Carlo simulation model is presented to mimic the real truck-and-shovel operations and measure trucks’ productivity in terms of Tonne Per Gross Operating Hour (TPGOH). A linear programming model is integrated into the simulation model to optimize the dispatch decision through distance minimization subject to the mine’s production schedule. The historical data records of oil sands mining operations are used to validate model’s performance. The results show significant improvement over the existing mine site’s method with closely matching the real TPGOH and better estimation of the total empty travel distance, as a result of new dispatch model implementation.

Car e-Talk: An IoT-Enabled Cloud-Assisted Smart Fleet Maintenance System

Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affect fleet productivity and performance. In this article, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle's health. Data from sensors are received on the driver's smartphone through a microcontroller and, after processing, useful information is displayed on the driver's mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.

Site Operation Strategy for Wheel Loader/Truck Loading and Transportation Cycle

The fuel consumption, productivity and operability are three important performance criteria for construction machinery. Better fuel economy sometimes means reduced productivity while higher productivity requires more powerful powertrain and thus consumes more fuel. It is challenging to compromise these performances in a single machine. However for working sites with multiple machines running together, it is possible to balance fleet fuel consumption and productivity with proper site operation strategy. In this study a site operation strategy for wheel loader/truck loading and transportation cycle is developed. A comprehensive criterion balancing the trade-off between fleet fuel consumption and fleet productivity - the fleet operating cost, is proposed. The strategy takes both fleet configuration parameter (number of wheel loaders and trucks) and operating parameter (wheel loader cycle time) into considerations. The fleet fuel consumption is calculated based on vehicle powertrain models instead of empirical estimation. The fleet fuel consumption, productivity and fleet operating cost with different numbers of wheel loader/truck and wheel loader cycle times are obtained. The optimal number of wheel loader/truck and wheel loader cycle time to achieve minimum fleet operating cost are identified.

A simulation-based algorithm for solving surface mines’ equipment selection and sizing problem under uncertainty

ABSTRACT Delivering a robust long-term mine plan depends on the precise prediction of mining fleet productivity and the estimation of the required fleet’s size over the mine life. In open-pit mining, fleet productivity and the required fleet’s size are directly related to the tonnage of moved material and the time and distance it takes for the trucks to move the mined material. All the above-mentioned parameters are associated with inherent uncertainties. Thus, incorporating these uncertainties is required in the prediction and estimation procedure to predict fleet productivity and subsequently estimate the required fleet size. This article presents a new simulation-based algorithm to predict fleet productivity and estimate the required fleet size in open-pit mines. Implementation of the developed simulation-based algorithm at an operating open-pit mine provided a considerable improvement over the existing method, with a slight (negative 2%) deviation in predicting fleet productivity compared to historical data.

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

Real-time monitoring and assessment of construction resources have always been a challenge due to the unique, dynamic, and complex nature of each construction site and operation. The ability to automatically classify activities performed by various equipment in real time can aid in making timely tactical operational decisions that can lead to increased fleet productivity, reduced time and cost of operations, and minimized idle times. Such endeavors have traditionally been performed manually through human observation, making it extremely labor and time-intensive. Meanwhile, the development of low-cost micro-electro-mechanical systems (MEMS) with rapidly evolving computing, networking, and storage capabilities, along with the advances in computational techniques such as machine learning present new opportunities in the real-time activity identification domain. Even though previous studies have shown promising results for equipment activity identification at limited levels of detail, they have a fundamental limitation in their reliance on the equipment vibration. Equipment vibration is highly dependent on factors that are extrinsic to the performance of an activity itself such as ground conditions, age and condition of equipment, and operator skill. This aspect of current methods necessitates the collection of training data from the specific equipment of interest and requires manual labeling of training data, thereby limiting its application across different types of equipment and operating conditions. This paper investigates the use of activity-specific equipment motions instead of vibration for activity identification. This approach is the first step towards the larger goal of generating training data automatically from virtual kinematic models of equipment in the future. This paper also provides an array of sensitivity analyses in order to determine the most appropriate parameters for implementing machine learning algorithms for equipment activity identification. A case study was performed using an excavator working on an earthmoving site that demonstrated a significant improvement in equipment activity identification results by utilizing IMU data of different articulated elements over previous efforts. The results of this paper indicate that more accurate results for activity identification can be obtained by using articulated equipment motion over vibration, which paves the way for the automatic generation of labeled training data in the future.

Composite Criticality in Machinery Fleet Management of Construction Projects

During construction operations, fleet management aims at maximizing the uptime and efficiency of construction machinery while also minimizing the cost of ownership through lifecycle planning and management. In the deterministic approach, the theory suggests that one type of machinery is considered to be critical. However, taking into account the real circumstances under which projects are performed with issues such as machine reliability, worker performance, and/or errors in estimating the scope of work, it is evident that there are significant limitations to the existing approach. Hence, to address this issue, uncertainty in fleet productivity is modelled with fuzzy set theory. In this context, the notion of composite criticality under which the productivity of a fleet depends on more than one type of machinery because of the fluctuations of the individual productivities is introduced. To this purpose, a simple case study is presented to illustrate this concept. It is concluded that this approach leads to a better understanding of the estimation of activity duration and cost estimation which in turn means better project scheduling and financial planning.

Robust Aircraft Maintenance Routing Problem Using a Turn-Around Time Reduction Approach

This article discusses the problem of how to efficiently build aircraft routes that better withstand potential disruptions, such as bad weather, technical problems, and passenger delays. This optimization problem is called robust aircraft maintenance routing problem (RAMRP). There are three approaches in the literature to deal with the RAMRP, such as the buffer time allocation approach (BT), the departure retiming approach (DR), and the scenario-based stochastic programming approach (SSP). Most of the previous approaches have some shortcomings in terms of fleet productivity and delay absorption. In addition, the majority of the RAMRP models overlook maintenance regulations, which result in the generation of infeasible routes. In this article, RAMRP is investigated with two main objectives. First, a novel robustness approach, called the turn-around time reduction approach (TRTR), that avoids the shortcomings of the existing approaches, is incorporated into RAMRP. The second objective is to develop an RAMRP model that simultaneously considers all maintenance regulations. The effectiveness of the proposed RAMRP model along with the TRTR is demonstrated using real data from a major Middle Eastern airline. The results reveal an improved performance of the TRTR over the BT by about 3.43%–12.20% and 2.5%–13.58%, while handling the expected propagated delay costs and fleet productivity, respectively. In addition, the results show that the TRTR is better than the SSP by about 2.07%–18.82%, while minimizing the propagated delay costs. Therefore, the TRTR has a great potential to be implemented in the actual industry.

Use of AIS data for the environmental characterization of world cruise ship traffic

Cruise tourism industry has experienced significant global growth in recent years in terms of new passengers, fleet size, ports visited, and economic value. Therefore, the main environmental impacts resulting from this activity such as exhaust emissions, wastewater discharges, and solid waste should be researched since most of them can be released directly into the ocean and atmosphere. This study develops a methodology that combines automatic identification system (AIS) data as well as reports belonging to different companies and environmental agencies, in order to assess environmental pressures related to global cruises’ traffic along their paths. Three innovative indexes are proposed to help assess environmental impacts of world cruise fleet (equivalent fulltime navigation vessel, areal cruise traffic pressure, and route cruise traffic pressure), displaying the most crowded routes by unit of length and water masses by unit of area. This methodology can allocate hot spots of activity and quantify the production of waste and emissions worldwide. Moreover, the results from the proposed method allow calculating the world cruise fleet production (2015–2017), resulting in the last year studied in 23 Mt·year−1 of CO2 and 34 hm3·year−1 of wastewater, in addition to many other calculated wastes.

Produkcja taboru tramwajowego i trolejbusowego na Ukrainie na tle sytuacji taborowej przedsiębiorstw

Początki produkcji tramwajów na obszarze Ukrainy to okres międzywojenny, a trolejbusów – koniec lat pięćdziesiątych XX wieku. Jednakże wraz z masowymi dostawami importowanego taboru z Rosji i Czechosłowacji produkcję tych środków transportu na Ukrainie wstrzymano. Na początku lat 90. XX wieku – z powodu kryzysu ekonomicznego i problemów finansowych – dostawy zagraniczne uległy znacznemu zmniejszeniu. Sytuacja przedsiębiorstw tramwajowych i trolejbusowych pogorszyła się wraz z rozpadem ZSRR, kiedy to uległy rozerwaniu więzi gospodarcze nie tylko pomiędzy państwami Europy Środkowej i Wschodniej, ale również pomiędzy byłymi sowieckimi republikami. W tych trudnych ekonomicznie warunkach podjęto decyzję o wznowieniu na Ukrainie produkcji tramwajów i trolejbusów. Artykuł ma na celu analizę sytuacji taborowej komunikacji tramwajowej i trolejbusowej na Ukrainie po 25 latach od momentu odzyskania niepodległości, poprzez porównanie dwóch aspektów jej funkcjonowania: ukraińskiego rynku producentów taboru tramwajowego i trolejbusowego oraz sytuacji taborowej przedsiębiorstw. Jako obszar badań przyjęto granice Ukrainy sprzed 2014 roku. Badania mają charakter podstawowy i opierają się w dużej mierze na materiale zebranym przez autorów. W artykule przeanalizowano produkcję taboru na Ukrainie po 1991 roku – nie tylko opisano produkowane marki i modele, ale również przygotowano zestawienia statystyczne dotyczące wielkości produkcji i jej odbiorców. Sytuację na rynku producentów taboru porównano z sytuacją taborową tamtejszych przedsiębiorstw. Badania potwierdziły, że mamy do czynienia z kryzysem systemowym. Komunalne przedsiębiorstwa tramwajowe i trolejbusowe każdego roku generują straty i coraz większe zadłużenie. Nowy tabor kupowany jest nieregularnie i w niewielkich ilościach, co ogranicza liczbę potencjalnych dostawców, a w dodatku przy wyborze najkorzystniejszej oferty preferowany jest rodzimy producent. Tym samym nie musi on być konkurencyjny cenowo, jakościowo i technologicznie, co tylko zwiększa jego dystans rozwojowy w stosunku do czołowych producentów taboru w Europie. A z uwagi na relatywnie niską jakość produktów ze swojego portfolio musi utrzymywać się wyłącznie ze skromnych zleceń krajowych.