Purpose
The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.
Design/methodology/approach
Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.
Findings
The electricity costs of the bus route can be reduced by applying the optimal charging plans.
Originality/value
This paper produces a viable option for transit agencies to reduce their operation costs.
Purpose
This paper aims to optimize the charging schedule for battery electric buses (BEBs) to minimize the charging cost considering the time-of-use electricity price.
Design/methodology/approach
The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model. The objective is to minimize the total charging cost of the BEB fleet. The charge decision of each BEB at the end of each trip is to be determined. Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.
Findings
This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line. The results show that the total charge cost with the optimized charging schedule is 15.56% lower than the actual total charge cost under given conditions. The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent, which can provide a reference for planning the number of charging piles.
Originality/value
Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.
With the introduction of emission peak and carbon neutrality strategy, green shipping has already become an urgent need for the development of water transportation industry. Using liquified natural gas (LNG)-powered ships is an effective way to deal with carbon emission, and more and more shipping companies are willing to choose it as the main ship type in the future. As the open cooperation portal to the Asia-Pacific region, the Bohai Rim region occupies an important position in China national shipping system. Focusing on the bunkering needs of LNG-powered ships in the future, ports in the Bohai Rim region should add LNG bunkering stations with reasonable layout and complete facilities. This paper constructs a comprehensive evaluation system for the site selection of LNG bunkering stations in coastal ports from five aspects: natural factors, infrastructure factors, economic factors, safety factors and policy factors, and conducts the evaluation on the alternative port areas. After comprehensive analysis, the Caofeidian Port Area of Tangshan Port is recommended as the location of LNG bunkering station.
Taxi sharing is becoming a trendy travel mode in an increasing number of cities. By encouraging two or more passengers to share a taxi, taxi sharing can cut down travel costs, reduce traffic emissions, and relieve traffic congestion. To promote taxi sharing, this paper first reviews the existing taxi sharing practices in cities. The entire process of taxi sharing can be broken up into the following: matching, route choice, and pricing. The current situation is revealed, as well as prospects for future developments. Building on the review, new approaches are proposed to taxi sharing matching, route choice modelling, and pricing schemes to improve taxi sharing practices and broaden their application.
Purpose
The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes.
Design/methodology/approach
The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model.
Findings
This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically.
Originality/value
The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses.
Owing to stricter environmental regulations of the International Maritime Organization (IMO) 2020, the demand of liquefied natural gas (LNG) bunkering is expected to grow by approximately 15% during 2020–2025 along with increased investments in eco-friendly ships by global shipping companies. Thus, determining optimal methods for LNG bunkering using existing ports that lack LNG bunkering infrastructure is necessary. Here, a method is proposed to determine the optimal LNG bunkering method for existing ports. Analyzing previous studies, we selected four evaluation factors: assessment of LNG supply for ships, suitability of fuel supply, risk of spillage, and domestic and international standards, which were used to calculate a geometric aggregation score via normalization, weight, and aggregation for selecting an appropriate LNG bunkering method. The analytical results indicated that the ship to ship (STS) method, evaluated based on the size and type of ships, is optimal for the Busan port. This is expected to contribute to the competitiveness of ports and their safety and economic feasibility by serving as a basis for determining the optimal LNG bunkering implemented in existing ports. It is necessary to expand the follow-up research to improve the evaluation method by aggregating more improved data through real cases.
From January 2020, the International Maritime Organization has regulated ship emissions to reduce sulfur content. As an alternative to this, LNG bunkering was proposed, and infrastructure and ships were deployed. Therefore, we used analytic hierarchy process AHP techniques to determine optimal methods of LNG bunkering for shipyard safety. First, we conducted a literature survey on the concept and type of LNG bunkering, global LNG bunkering trends, and features of Japan and South Korea cases and compared them. Thereafter, an expert survey was conducted, and survey data was analyzed using AHP techniques. Finally, we derived optimal methods applicable to shipyard industry. The analytical results revealed that the derived priority of the optimal LNG bunkering method of shipyard was in the order of the STS method, TTS method, and the PTS method. The result of this study can serve as a theoretical basis to make LNG bunkering safer and more economical in shipyards to prepare for the expansion of demand of LNG-fueled ships and LNG. However, this study inevitably has limitations of ranking reversals paradox as it was conducted by experts, assuming no weights to STS, TTS, or PTS.
Growing awareness of the environment and new regulations of the International Maritime Organization and the European Union are forcing ship-owners to reduce pollution. The use of liquefied natural gas (LNG) is one of the most promising options for achieving a reduction in pollution for inland shipping and short sea shipping. However, the infrastructure to facilitate the broad use of LNG is yet to be developed. We advance and analyze models that suggest LNG infrastructure development plans for refueling stations that support pipeline-to-ship and truck-to-ship bunkering, specifying locations, types, and capacities, and that take into account the characteristics of LNG, such as boil-off during storage and loading. We develop an effective primal heuristic, based on Lagrangian relaxation, for the solution of the models. We validate our approach by performing a computational study for the waterway network in the Arnhem-Nijmegen region in the West-European river network, including, among others, multi-year scenarios in which capacity expansion and reduction are possible.
This paper focuses on sustainable transportation of prefab products from factories to construction sites by ship. Since the transportation cost for all the prefab products of a construction site is mainly dependent on the number of cargo holds used on ships, a loading plan for prefab products that minimizes the number of holds required is highly desirable. This paper is therefore devoted to the development of an optimal loading plan that decides which prefab products are loaded into each cargo hold and how to pack these prefab products into the holds so that as few holds as possible are used. We formulate the problem as a large-scale integer optimization model whose objective function is to minimize the total number of cargo holds used and whose constraints represent the cargo hold capacity limits. We develop a heuristic to solve the problem and obtain a high-quality solution. We have tested the model and algorithm on a case study that includes 20 prefab products. We find that different cargo holds carry prefab products that have quite different densities. Moreover, the orientations of many prefab products are different from their default orientations. The results demonstrate the applicability of the proposed model and algorithm.
In this paper, we investigate the foldable container slot planning problem with loading and unloading operations that include shifting containers in a shipping line. We use the global optimal perspective in which a terminal operator generates an optimal stowage plan created on the basis of demand at subsequent ports. State-of-the-art foldable containers have been recently used in commercial maritime transport systems because they confer space-saving advantages when folded. We investigate container use through mixed-integer programming and shift cost-sharing methods as means to prevent conflicts between ports over inessential shifts and to provide guidelines for distributing shift costs among all ports in a logical and fair way. Through the proposed model, we found that most inessential shifts, considered inevitable from the local optimal perspective, can be eliminated, and the inevitable shift costs can be fairly distributed.
This article is a revised and expanded version of a paper entitled ‘Shift Minimization with Loading and Unloading Operations using Foldable Containers’ presented at the first Conference of the Yangtze-River Research and Innovation Belt(Y-RIB), Zhoushan, China; 2–5 December 2018.
This paper addresses an in-port multi-ship routing and scheduling problem in maritime transportation. The aim is to find an optimized schedule for a number of ships in a port that is going to pick up or deliver some cargos located in the various terminals with different draft limits. We develop a mathematical model based on multiple traveling salesman problem (mTSP) with draft limits and time windows. To solve the presented model, a two-stage solution method based on dynamic programming and branch-and-bound algorithms is developed. Finally, the solution approach is applied to solve 41 real-sized instance problems. The experiments show the superiority of the proposed method compared to CPLEX and the capability of solving the instances in a reasonable time. The formulated problem and its solution methodology are also applicable to fleet operations of autonomous ships in the future smart shipping.
The substantial adverse effects of risk factors on container shipping and logistics promoted a deep integration of risk analysis into the decision-making process. This paper aims to develop a well-grounded quantitative model to operational risk in a container shipping context. Considering uncertainty as a primary component of the risk concept, methods were employed in an inter-complementary manner to enable not only a sense of foreseeability but also a deeper look into the weaknesses of the knowledge base. The intersubjectivity of the input extraction process was supported by the Evidential Reasoning (ER) algorithm. Risks are then assessed based on a Fuzzy Rules Bayesian Network (FRBN) model with a 2-level parameter structure before meaningful interpretations can be derived through a new risk mapping approach. Besides an illustrative case study, the model was tested by sensitivity analysis and an examination of multiple validity claims.
Since 2020, the International Maritime Organization (IMO) has tightened regulations on the emissions of sulfur oxides from ships from less than 3.5% to less than 0.5%. As a countermeasure, shipping companies can adopt one of three potential solutions: using low sulfur fuel (LSFO), installing scrubbers, or using liquefied natural gas (LNG) fuel. However, considering the environmental aspects such as the UN greenhouse gas (GHG) emission reduction program and the reduction of fine dust generation in port areas, LNG fuel is ultimately considered to be the most ideal method in the marine industry. In line with this international trend, major port authorities are considering building LNG bunkering stations, but the proper methods and criteria for estimating the size of LNG bunkering infrastructure are not clear. This study proposes a method of estimating the size of LNG infrastructure required with consideration for the operational status of ports according to the estimated amount of bunkering demand at a future time with the case study of Busan Port in Korea. In order to estimate the detailed demand amount by inbound vessels, a simulation modeling technique is applied as a tool of research.
Recent studies in maritime shipping have concentrated on environmental and economic impacts of ships. In this regard, fuel is considered as one of the important factors for such impacts. In particular, the sailing speed of the vessels affects the fuel consumption directly. In this study, we consider a speed optimization problem in liner shipping, which is characterized by stochastic port times and time windows. The objective is to minimize the total fuel consumption while maintaining the schedule reliability. We develop a dynamic programming model by discretizing the port arrival times to provide approximate solutions. A deterministic model is presented to provide a lower bound on the optimal expected cost of the dynamic model. We also work on the effect of bunker prices on the liner service schedule. We propose a dynamic programming model for bunkering problem. Our numerical study using real data from a European liner shipping company indicates that the speed policy obtained by proposed dynamic model performs significantly better than the ones obtained by benchmark methods. Moreover, our results show that making speed decisions considering the uncertainty of port times will noticeably decrease fuel consumption cost.
We consider a project selection problem where each project has an uncertain return with partially char- acterized probability distribution. The decision maker selects a feasible subset of projects so that the risk of the portfolio return not meeting a specified target is minimized. To model and evaluate this risk, we propose and justify a general performance measure, the Underperformance Riskiness Index (URI). We define a special case of the URI, the Entropic Underperformance Riskiness Index (EURI), for the project selection problem. We minimize the EURI of the project portfolio, which is the reciprocal of the absolute risk aversion (ARA) of an ambiguity averse individual with constant ARA who is indifferent between the target return with certainty and the uncertain portfolio return. The EURI extends the riskiness index of Aumann and Serrano (2008) by incorporating the target and distributional ambiguity, and controls the underperformance probability for any target level. Our model includes correlation and interaction effects such as synergies. Since the model is a discrete nonlinear optimization problem, we derive the optimal solution using Benders decomposition techniques. We show that computationally efficient solution of the model is possible. Further- more, the project portfolios generated by minimizing the underperformance risk are more than competitive in achieving the target with those found by benchmark approaches, including maximization of expected return, minimization of underperformance probability, mean-variance analysis, and maximization of Roy’s safety-first ratio (1952). When there is only a single constraint for budget, we describe a heuristic which routinely provides project portfolios with near optimal underperformance risk.
Liquefied natural gas (LNG) bunkering stations are areas for bunkering LNG-powered ships via a flexible hose from either a shoreside facility, shore-based/pontoon tank, or an LNG truck. The operation management of LNG bunkering stations is complex because many factors affect the operational performance, including station layout, bunkering technology, and frequent interactions among trucks, tanks, and ships. In this study, we consider the bunkering operation problem (BOP) of an LNG bunkering station in the inland waterways. The problem involves decisions of assigning ships to tanks, managing the inventory of the tanks, and scheduling LNG trucks for bunkering ships and replenishing tanks. We first formulate the problem as a mixed-integer linear programming (MILP) model that aims to minimize the cost incurred by the bunkering operations. As the BOP is NP-hard, the MILP model for the problem with practical size is generally difficult to solve. We thus reformulate it into an equivalent task-based model and develop a tailored branch-and-price heuristic (BPH) algorithm to solve the new model. Several enhancement techniques are also presented to improve the solution efficiency of the BPH algorithm. Numerical experiments demonstrate the satisfactory performance of the solution algorithm. Some managerial implications are also obtained to provide scientific guidance for station operators to make operational decisions. In particular, our model can help determine the best combination of bunkering modes and replenishment modes as well as the optimal truck fleet.
This paper proposes a coordinated charging scheduling approach for battery electric buses (BEBs) in a hybrid charging scheme, i.e., both plug-in fast charging and battery-swapping charging modes are incorporated in a single charging station. To accommodate the uncertain battery energy consumption during bus operation, a two-stage stochastic program is formulated, where the first stage decision determines the battery inventory level of each station and the second stage determines the charging mode and designs when, where, and how long each bus should be charged. Future uncertainties associated with energy consumption are captured by a set of possible discrete scenarios from historical data. A progressive hedging algorithm is developed to decompose the two-stage stochastic program into sub-problems. A case study is conducted to verify the proposed models and solution algorithms.
Prediction and optimization are the foundation of many real-world analytics problems in various disciplines. As both can be challenging, they are usually treated sequentially in existing studies, where the prediction problem is dealt with in the first stage, followed by the optimization problem in the second stage, which is called the predict-then-optimize paradigm. Specifically, the unknown parameters in the optimization problem are first predicted by the prediction model and are then input to the optimization model to generate the optimal decisions. However, prediction models in the first stage are intended to minimize the prediction error, while ignoring the structure and property of the downstream optimization problem and how the predictions will be used. Consequently, suboptimal decisions might be generated. This editorial piece discusses current popular frameworks to integrate prediction with optimization, namely the smart “predict, then optimize” framework and the predictive prescription framework with examples in the transportation area provided. The article ends with proposing several promising research directions for future research.
Recommendation system has recently experienced widespread applications in fields like advertising and streaming platforms. Its ability of extracting valuable information from complex data makes it a promising tool for multi-modal transportation system. In this paper, we propose a conceptual framework for proactive travel mode recommendation combining recommendation system and transportation engineering. The proposed framework works by learning from historical user behavioral preferences and ranking the candidate travel modes. In this framework, an incremental scanning method with multiple time windows is designed to acquire multi-scale features from user behaviors. In addition, to alleviate the computational burden brought by the large data size, a hierarchical behavior structure is developed. To further allow for social benefits, the proposed framework proposes to adjust the candidate modes according to real-time traffic states, which is potential in promoting the use of public transport, alleviating traffic congestion, and reducing environmental pollution.
The paper focuses on the impacts of the inclusion of the maritime sector in the EU Emissions Trading System (ETS). The enforcement of a regional Market-Based Measure (MBM) such as the EU ETS may provide financial incentives to shipping operators to reconfigure their networks and avoid voyages inside the European Economic Area (EEA). This paper investigates the risk of container vessels engaging in evasive port calls by replacing EEA transshipment hubs with nearby non-EEA competitors. We perform a cost–benefit analysis that calculates the cost of EU Allowances (EUAs) for several international services and compares it with a relocation scenario. Our case studies focus on the Piraeus–Izmir and the Algeciras–Tanger Med scenarios and identify the EU carbon price turning point that will render the switch of the transshipment hubs a cost-effective choice for the operator. The results show that the preference of a non-EEA hub will become attractive for carbon prices well below 25 EUR per metric ton of CO2. Further, in all cases, the hub switch results in a rise in the overall carbon emissions attributed to the service which amplifies the risk of carbon leakage. Our results show that the relocation would lead to revenue loss for the EU ETS and penalization of the EEA transshipment hubs in close proximity with hubs outside the EEA, thus posing a threat to their economic activity and development.
The pressure on shipping to reduce its carbon footprint is increasing. Various measures are being proposed at the International Maritime Organization (IMO), including Market-Based Measures (MBMs). This paper investigates the potential of a bunker levy in achieving short-term CO2 emissions reductions. The analysis focuses on the tanker market and uses data from the latest IMO GHG studies and a variety of other sources. The connection between fuel prices and freight rates on the one hand and vessel speeds on the other is investigated for the period 2010–2018. A model to find a tanker’s optimal laden and ballast speeds is also developed and applied to a variety of scenarios. Results show that a bunker levy, depending on the scenario, can lead to short-term CO2 emissions reductions of up to 43%. Policy implications are also discussed, particularly vis-à-vis recent IMO and European Union (EU) action on MBMs.
This paper is a sequel to the authors’ earlier article, which addressed the safe layout design of liquified natural gas (LNG) fueled facilities for preventing LNG or natural gas (NG) leaks. This paper aims to develop a new method for designing the safety zone of LNG-fueled ships during truck-to-ship LNG bunkering. While the deterministic approach has limitations in practical application, a hybrid method is suggested as an advanced design solution. In this regard, this paper develops a hybrid method as an alternative solution to the deterministic approach based current industry practice. The applicability of the proposed solution is demonstrated with an illustrative example of the safety zone layout of a truck-to-ship LNG bunkering. The insights and findings of the paper are summarized in association with a new design method for the safety zone, contributing to the safety engineering of LNG bunkering.
Uncertainty is usually perceived as having negative effects on transportation systems, such as increasing operation cost, decreasing resource utility, and reducing customer satisfaction. However, it is unclear whether this perception is universally true or is true only under certain conditions. This research compares the performance of transportation systems with uncertain parameters with the performance of the same systems in which the uncertain parameters are replaced by their expectations. The analyses prove that uncertainty can have negative, negligible, and positive impact on the performance of transportation systems under different conditions.
Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.
An Integrated Approach to Managing Vessel Service in Seaports
Efficient vessel service is of utmost importance in the maritime supply chain. When serving a group of incoming vessels, berth allocation and pilotage planning are the two most important decisions made by a seaport. Although they are closely correlated, the berth allocation problem and pilotage planning problem are often solved sequentially, leading to suboptimal or even infeasible solutions for vessel services. In “Vessel Service Planning in Seaports,” Wu, Adulyasak, Cordeau, and Wang focus on a vessel service planning problem that optimizes berth allocation and pilotage planning in combination. To solve the joint problem, the authors develop an exact solution method that combines Benders decomposition and column generation within an efficient branch-and-bound framework. They also propose acceleration strategies that significantly improve the performance of the algorithm. Test instances from one of the world's largest seaports are used to validate the effectiveness of the approach and demonstrate the value of integrated planning.
The shipping industry is associated with approximately three quarters of all world trade. In recent years, the sustainability of shipping has become a public concern, and various emissions control regulations to reduce pollutants and greenhouse gas (GHG) emissions from ships have been proposed and implemented globally. These regulations aim to drive the shipping industry in a low-carbon and low-pollutant direction by motivating it to switch to more efficient fuel types and reduce energy consumption. At the same time, the cyclical downturn of the world economy and high bunker prices make it necessary and urgent for the shipping industry to operate in a more cost-effective way while still satisfying global trade demand. As bunker fuel bunker (e.g., heavy fuel oil [HFO], liquified natural gas [LNG]) consumption is the main source of emissions and bunker fuel costs account for a large proportion of operating costs, shipping companies are making unprecedented efforts to optimize ship energy efficiency. It is widely accepted that the key to improving the energy efficiency of ships is the development of accurate models to predict ship fuel consumption rates under different scenarios. In this study, ship fuel consumption prediction models presented in the literature (including the academic literature and technical reports as a typical type of “grey literature”) are reviewed and compared, and models that optimize ship operations based on fuel consumption prediction results are also presented and discussed. Current research challenges and promising research questions on ship performance monitoring and operational optimization are identified.
With the construction of on-dock rails, the terminal has become an interface between the maritime transport network and the railway transport network. Terminal operators are facing some new challenges like more complicated terminal operations and the scarcity of storage spaces. To address this issue, the managers should improve the operation efficiency of terminals by adjusting the yard templates and equipment deployment plan, which has not been well studied. To fill this gap, we study the transfer flow template planning problem in seaport railway terminals, and a multi-objective model that integrates the decisions on flow volume, yard template, and equipment deployment plan is proposed. Then, a group of numerical experiments is conducted using Ningbo Beilun Container Terminal as an example to analyze the effect of different management objectives, the pattern of yard template, and the influence of handling capacities. The results show that the optimized flow template performs well when using maximizing throughput as the primal objective. It also reports that stacking imported containers in the blocks near the seaside could help to reduce the operation cost and time consumption of the terminal. Moreover, the location of on-dock rails shows a significant influence on the yard template.
To reduce CO 2 emissions from shipping activities to, from, and within the European Union (EU) area, a system of monitoring, reporting, and verification (MRV) of CO 2 emissions from ships are implemented in 2015 by the EU. Although the MRV records in 2018 and 2019 have been published, there are scarce studies on the MRV system especially from a quantitative perspective, which restrains the potential of the MRV. To bridge this gap, this paper first analyzes and compares MRV records in 2018 and 2019, and then develops machine learning models for annual average fuel consumption prediction for each ship type combining ship features from an external database. The performance of the prediction models is accurate, with the mean absolute percentage error (MAPE) on the test set no more than 12% and the average R-squared of all the models at 0.78. Based on the analysis and prediction results, model meanings, implications, and extensions are thoroughly discussed. This study is a pioneer to analyze the emission reports in the MRV system from a quantitative perspective. It also develops the first fuel consumption prediction models from a macro perspective using the MRV data. It can contribute to the promotion of green shipping strategies.
Port state control (PSC) inspection contributes a lot to improving maritime safety and protecting the marine environment. After selecting the ships coming to a port for inspection, one critical challenge faced by the PSC authorities is deciding what deficiency items should be inspected and what the inspection sequence of these items is. To address this problem, two innovative and high-efficient PSC inspection schemes describing specific PSC inspection items and sequence are proposed for the inspectors’ reference when time and resources are limited, especially when there are difficulties in estimating the possible deficiencies in advance. Both schemes take the occurrence probability, inspection cost, and ignoring loss of each deficiency item into account. More specifically, the first inspection scheme is based on the occurrence probabilities of the deficiency items in the whole data set, while the second scheme further considers the correlations among the deficiency items extracted by association rules. The results of numerical experiments show that the efficiency of the two proposed inspection schemes is 1.5 times higher than that of the currently used inspection scheme. In addition, the second inspection scheme performs better than the first inspection scheme, especially with inspecting ships with no less than five deficiency items and limited inspection resources.
Purpose
As of January 1, 2020, the upper limit of sulfur emissions outside emission control areas decreased from 3.5% to 0.5%. This paper aims to present some of the challenges associated with the implementation of the sulfur cap and investigates its possible side effects as regard the drive of the International Maritime Organization (IMO) to reduce carbon dioxide (CO 2 ) emissions. Even though it would appear that the two issues (desulfurization and decarbonization) are unrelated, it turns out that there are important cross-linkages between them, which have not been examined, at least by the regulators.
Design/methodology/approach
A literature review and a qualitative risk assessment of possible CO 2 contributors are presented first. A cost-benefit analysis is then conducted on a specific case study, so as to assess the financial, as well as the environmental impact of two main compliance choices, in terms of CO 2 and sulfur oxide.
Findings
From a financial perspective, the choice of a scrubber ranks better comparing to a marine gas oil (MGO) choice because of the price difference between MGO and heavy fuel oil. However, and under different price scenarios, the scrubber choice remains sustainable only for big vessels. It is noticed that small containerships cannot outweigh the capital cost of a scrubber investment and are more sensitive in different fuel price scenarios. From an environmental perspective, scrubber ranks better than MGO in the assessment of overall emissions.
Research limitations/implications
Fuel price data in this paper was based on 2019 data. As this paper was being written, the COVID-19 pandemic created a significant upheaval in global trade flows, cargo demand and fuel prices. This made any attempt to perform even a rudimentary ex-post evaluation of the 2020 sulfur cap virtually impossible. Due to limited data, such an evaluation would be extremely difficult even under normal circumstances. This paper nevertheless made a brief analysis to investigate possible COVID-19 impacts.
Practical implications
The main implication is that the global sulfur cap will increase CO 2 emissions. In that sense, this should be factored in the IMO greenhouse gas discussion.
Originality/value
According to the knowledge of the authors, no analysis examining the impact of the 2020 sulfur cap on CO 2 emissions has yet been conducted in the scientific literature.
We consider a significant problem that arises in the planning of many projects. Project companies often use outsourced providers that require capacity reservations that must be contracted before task durations are realized. We model these decisions for a company that, given partially characterized distributional information, assumes the worst-case distribution for task durations. Once task durations are realized, the project company makes decisions about fast tracking and outsourced crashing, to minimize the total capacity reservation, fast tracking, crashing, and makespan penalty costs. We model the company’s objective using the target-based measure of minimizing an underperformance riskiness index. We allow for correlation in task performance, and for piecewise linear costs of crashing and makespan penalties. An optimal solution of the discrete, nonlinear model is possible for small to medium size projects. We compare the performance of our model against the best available benchmarks from the robust optimization literature, and show that it provides lower risk and greater robustness to distributional information. Our work thus enables more effective risk minimization in projects, and provides insights about how to make more robust capacity reservation decisions.
Summary of Contribution: This work studies a financially significant planning problem that arises in project management. Companies that face uncertainties in project execution may need to reserve capacity with outsourced providers. Given that decision, they further need to plan their operational decisions to protect against a bad outcome. We model and solve this problem via adjustable distributionally robust optimization. While this problem involves two-stage decision making, which is computationally challenging in general, we develop a computationally efficient algorithm to find the exact optimal solution for instances of practical size.
Purpose
This paper aims to examine containership routing and speed optimization for maritime liner services. It focuses on a realistic case in which the transport demand, and consequently the collected revenue from the visited ports depend on the sailing speed.
Design/methodology/approach
The authors present an integer non-linear programming model for the containership routing and fleet sizing problem, in which the sailing speed of every leg, the ports to be included in the service and their sequence are optimized based on the net line's profit. The authors present a heuristic approach that is based on speed discretization and a genetic algorithm to solve the problem for large size instances. They present an application on a line provided by COSCO in 2017 between Asia and Europe.
Findings
The numerical results show that the proposed heuristic approach provides good quality solutions after a reasonable computation time. In addition, the demand sensitivity has a great impact on the selected route and therefore the profit function. Moreover, the more the demand is sensitive to the sailing speed, the higher the sailing speed value.
Research limitations/implications
The vessel carrying capacity is not considered in an explicit way.
Originality/value
This paper focuses on an important aspect in liner shipping, i.e. demand sensitivity to sailing speed. It brings a novel approach that is important in a context in which sailing speed strategies and market volatility are to be considered together in network design. This perspective has not been addressed previously.
The purpose of this paper is to design a route and speed optimization method to simultaneously reduce sailing cost and time, considering Emission Control Areas (ECAs) regulations and weather conditions. To solve it, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was used to find the Pareto-optimal set, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to find the trade-off solution from the Pareto-optimal set. The proposed model was then applied to a service route around the US Coast, based on different fuel prices in ECAs. The results show that the proposed method can reduce the total sailing cost and time compared with other methods, and can help shipping companies effectively cope with fuel price fluctuations. Moreover, we also determined the best time interval for changing ship route and speed in terms of minimizing the sailing cost.
This article investigates a low carbon-oriented berth allocation and quay crane assignment problem considering vessels’ uncertain arrival time and loading/unloading workload for vessels. A two-stage stochastic programming model is formulated based on a set of scenarios. The first stage designs a baseline schedule and the second stage adjusts the schedule in each scenario. A solution method is developed by using column generation techniques. Numerical experiments are conducted to validate the efficiency of our column generation-based solution method and the effectiveness of the proposed decision model. Some sensitivity analysis is also performed to draw some managerial implications.
Collision avoidance for unmanned surface vehicles (USVs) is significant for the fulfillment of autonomous navigation. Generally, classical collision-avoidance algorithms are proposed for relatively simple encounter situation, in this scenario only two USVs are stressed. Furthermore, to generate the rational manoeuvre operations, it is necessary that USVs should abide by International Regulations for Preventing Collision at Sea (COLREGS). However, COLREGS has not paid attention to rules for multi-USV collision-avoidance problem. Furthermore, those collision-avoidance rules in COLREGS have not been quantified for USVs. Following that, this paper utilizes deep reinforcement learning (DRL) algorithm to resolve collision-avoidance for USVs even in complex encounter situations. Within our DRL algorithm, related COLREGS is quantified properly and integrated into the DRL model, and then encounter situation of USVs is formulated as environmental observation value, accordingly a set of decision making is reached by decision-making neural network, and the reward function is designed for updating network parameters iteratively. Consequently, collision avoidance for USVs can be achieved eventually. By employing our DRL algorithm, collision avoidance for USVs under generous complex scenarios are resolved with the aid of corresponding intelligent decision-making operations. Simulation results verify the effectiveness of our DRL algorithm.
Given environment regulations on emissions from ships, shipping companies have sought alternative fuel ships, such as LNG-powered vessels, which may give rise to growth in liquefied natural gas (LNG) bunkering ports. Because demand for LNG-powered vessels is expected to increase, it is worth assessing the factors that lead to the selection of LNG bunkering ports in LNG bunkering industries. However, a lack of academic research exists in the field of LNG bunkering port selection. This paper employs a second-stage empirical analysis approach that selects criteria for shipping companies' selection of a LNG bunkering port through a literature review and interviews, and then adopts a fuzzy-AHP methodology to reveal the priority of the LNG bunkering port selection criteria in LNG bunkering decision making. The results indicate that most shipping companies decide on a LNG bunkering port with a stronger emphasis on safety/security or port services rather than port reputation. This paper offers invaluable policy implications for governments and port authorities that plan to build and operate LNG bunkering ports in the near future.
This paper investigates a stowage planning problem, in which a liner ship will visit a sequence of ports, the number of available quay cranes in ports and the numbers of loading/unloading containers in ports are uncertain. This stowage planning problem is about how to assign the loaded containers to the bays of the ship considering uncertain information in the future, so as to minimise the sum of the expected quay crane handling time at the ports. Based on stochastic programming, a two-stage decision model is proposed for this problem. A particle swarm optimisation based solution method is developed to solve the model for large-scale problem instances. Numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.
The objective of this paper is to examine the characteristics of leaked-gas dispersion in ship-to-ship liquefied natural gas (LNG) bunkering, thereby providing an insight towards determining the appropriate level of safety zones. For this purpose, parametric studies are undertaken in various operational and environmental conditions, with varying geometry of the ships, gas leak rate, wind speed and wind direction. The study applies computational fluid dynamics (CFD) simulations for case-specific scenarios where a hypothetical LNG bunkering ship with a capacity of 5100 m³ in tank space is considered to refuel two typical types of large ocean-going vessels: an 18,000 TEU container ship and a 319,000 DWT very large crude oil carrier. It is found that wind speed, wind direction, ship geometry and loading condition are important parameters affecting the extent of safety zones in addition to gas leak rate and leak duration. Details of the computations and discussions are presented.
With strong environmental and economic driving forces for using LNG as a marine fuel over the last decade, an increasing number of local/international ports, mainly in Europe, have initiated LNG fuel providing service to LNG-fuelled ships. This trend is now spreading throughout the world.
The LNG bunkering methods currently in use are truck-to-ship (TTS), ship-to-ship (STS) and pipeline-to-ship (PTS). This paper describes a study conducted to identify potential risks associated with LNG bunkering with particular emphasis on the fuel-supplying side. A series of parametric analyses were also carried out to identify the sensitivity to some parameters with the aid of a purpose-built computer program, Integrated Quantitative Risk Assessment (IQRA). Through the parametric analyses, general relationships between the risk and various parameters could be established from which the importance of the selected parameters might be evaluated.
This paper also proposes a new approach of establishing realistic safety exclusion zones in LNG bunkering process. Research findings demonstrate that the implied hypothesis that the current practice of the probabilistic risk assessment focused on the population-independent analysis only is somewhat inadequate when applied to determining the safety exclusion zones as showing that the extent of safety exclusion zones tends to be set up unpractically wide. Instead, the proposed approach designed with the combination of population-dependant and independent analyses is proven to be useful in determining the zones more realistically. It may form a basis on which more useful safety-related standards and regulations on LNG bunkering can be built.
This study proposes a pile-guided floater, a new mooring concept, for large offshore floating structures such as an offshore liquefied natural gas (LNG) bunkering terminal. The economic feasibility of the new mooring system was demonstrated through a cost–benefit analysis. The environmental loads acting on the floaters were computed using wave data at the target location. The mooring system was designed using finite element analysis to estimate the additional investment. An LNG ship-to-ship bunkering operation that included an LNG bunkering terminal, LNG carrier, LNG bunkering shuttle, and LNG receiving ship was adopted. To estimate the technical feasibility and economic benefit of the proposed mooring system, the availabilities of two types of LNG bunkering terminals were compared considering the acceptance criteria for LNG ship-to-ship transfers. One LNG bunkering terminal was a typical barge-type floater and the other was the pile-guided floater. The relative motion of the terminal with the LNG carrier and the LNG bunkering shuttle was analyzed. The limiting wave height was determined from the maximum relative vertical motion between the floaters at the position of the LNG loading arms. The availability of the pile-guided LNG bunkering terminal was significantly improved owing to the reduced vertical motion. Finally, a cost–benefit analysis verified that the new mooring concept for an offshore LNG bunkering terminal was economically feasible.
Objective:
This study aims to evaluate the effectiveness of a newly designed hybrid cooling vest for construction workers in alleviating heat stress.
Method:
Two types of cooling vests, namely, a commonly worn Vest A and a newly designed Vest B, were tested in a climatic chamber environment (34.0?C temperature, 60% relative humidity, and 0.4 m s-1 air velocity) using a sweating thermal manikin. Four test scenarios were included: fan off with no phase change materials (PCMs) (Fan-off), fan on with no PCMs (Fan-on), fan off with completely solidified PCMs (PCM + Fan-off), and fan on with completely solidified PCMs (PCM + Fan-on).
Result:
Test results showed that Vests A and B provided a continuous cooling effect during the 3-h test. The average cooling power for the torso region of Vest B was 67 W, which was higher than that of Vest A (56 W). The addition of PCMs offered a cooling effect of approximately 60 min. Ventilation fans considerably improved the evaporative heat loss compared with the Fan-off condition.
Conclusion:
The newly designed hybrid cooling vest (Vest B) may be an effective means to reduce heat strain and enhance work performance in a hot and humid environment.
Purpose
Extreme hot environments are prevalent in many occupational settings, and facilities management workers are no exception. Wearing suitable cooling garment is a useful means to alleviate heat strain and improving performance at heat exposure. This paper aims to evaluate the effectiveness and applicability of the cooling vest across four selected fields (i.e. construction, outdoor cleaning and horticulture, kitchen work and work involved manual handling at the airport) and identify the shortcomings of the cooling vest used by the participating workers.
Design/methodology/approach
This study adopted a two-phase design: a quantitative questionnaire survey followed by qualitative in-depth interviews.
Findings
A remarkable physical strain alleviation (PSA) of 21.1 per cent (14.8 per cent in construction, 18.8 per cent in horticulture and cleaning, 27.4 per cent in kitchen and catering and 26.5 per cent in airport apron service) is achieved by the use of cooling vest in four industries. Despite the success of PSA, several shortcomings of the cooling vest were identified: easily stained color, heavy weight, short cooling time, inflexibility that presents a hazard around moving equipment, lack of industry-specific design, nondurable and thick fabric with poor permeability.
Originality/value
The findings of the current study do not only confirm the effectiveness of the cooling vest in alleviating heat strain and physical strain but also identify the major shortcomings upon which further improvements can be made.
We study the discrete optimization problem under the distributionally robust framework. We optimize the Entropic Value-at-Risk, which is a coherent risk measure and is also known as Bernstein approximation for the chance constraint. We propose an efficient approximation algorithm to resolve the problem via solving a sequence of nominal problems. The computational results show that the number of nominal problems required to be solved is small under various distributional information sets.
Containerized transport by liner shipping companies is a multi billion dollar industry carrying a major part of the world trade between suppliers and customers. The liner shipping industry has come under stress in the last few years due to the economic crisis, increasing fuel costs, and capacity outgrowing demand. The push to reduce CO2 emissions and costs have increasingly committed liner shipping to slow-steaming policies. This increased focus on fuel consumption, has illuminated the huge impacts of operational disruptions in liner shipping on both costs and delayed cargo. Disruptions can occur due to adverse weather conditions, port contingencies, and many other issues. A common scenario for recovering a schedule is to either increase the speed at the cost of a significant increase in the fuel consumption or delaying cargo. Advanced recovery options might exist by swapping two port calls or even omitting one. We present the Vessel Schedule Recovery Problem (VSRP) to evaluate a given disruption scenario and to select a recovery action balancing the trade off between increased bunker consumption and the impact on cargo in the remaining network and the customer service level. It is proven that the VSRP is NPNP-hard. The model is applied to four real life cases from Maersk Line and results are achieved in less than 5 seconds with solutions comparable or superior to those chosen by operations managers in real life. Cost savings of up to 58% may be achieved by the suggested solutions compared to realized recoveries of the real life cases.
Inland river LNG bunkering demand and the location and layout of bunkering station
Jan 2019
13
Dai
Shipping decarbonisation: overcoming the obstacles
Jan 2022
Psaraftis
International guidelines for Bunkering LNG as a Marine fuel
E Skramstad
Research on the development forecast of Chongqing marine LNG bunkering terminal layout planning