No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Simulation of an AIS System for the Port of Hamburg
Abstract
This paper shows that the prediction of vessel arrival times with AIS (Automatic Identification System) is increasing the number of vessels a port can handle without additional superstructure. The Port of Hamburg is used as a case study to show the difference between the as-is situation and one with the integrated information system. The simulation shows improvements with two different risk levels to prove the concept. The simulation uses simplified versions of an algorithm that assigns vessels to free berths without disrupting the normal terminal usage. It was possible to clear up to 44% more ships each day just with an additional system that utilises already existing data for achieving more efficiency within the port.
... The arrival of a ship triggers activities at all these parties, who then determine the performance of the port as a whole by each contributing their specialist activity [19]. The need for reliable tools to verify and ensure the accuracy of the estimated time of arrival (ETA) information provided by ships as they approach ports has never been more critical than it is today [3,6]. This paper establishes a groundwork for future research by demonstrating the advantages of using neural networks (NN) with simple input parameters for ETA prediction in inland waterway transportation. ...
... Previous studies have emphasised the significance of enhancing ETA prediction accuracy for efficient terminal and inland port operations [3,6]. Xie [22] has showcased the effectiveness of using LSTM models for ETA prediction. ...
Ensuring the accuracy of the estimated time of arrival (ETA) information for ships approaching ports and inland terminals is increasingly critical today. Waterway transportation plays a vital role in freight transportation and has a significant ecological impact. Improving the accuracy of ETA predictions can enhance the reliability of inland waterway shipping, increasing the acceptance of this eco-friendly mode of transportation. This study compares the industry-standard approach for predicting the ETA based on average travel times with a neural network (NN) trained using real-world historical data. This study generates and trains two NN models using historical ship position data. These models are then assessed and contrasted with the conventional method of calculating average travel times for two specific areas in the Netherlands and Germany. The results indicate by using specific input features, the quality of ETA predictions can improve by an average of 20.6% for short trips, 4.8% for medium-length trips, and 13.4% for long-haul journeys when compared to the average calculation.
In the context of port communities, one of the most commonly used technological developments are Port Community Systems (PCS). The typical service offer of a PCS includes information exchange, electronic exchange of customs declarations and responses, control, tracking and tracing of the goods, and statistics. However, it has been argued that it is necessary to evolve the existing PCS into a renewed version, more suitable to the novel requirements posed by both developed and emerging economies, also incorporating the surge of new technologies. Such renovation should care for integrating new value-added services to the aforementioned typical PCS service offer. Therefore, we propose a new hinterland intermodal routing service to be included to the regular PCS functionality. Such new service is based on the development of a built-in optimization model, delivering a sustainable and cost-effective intermodal transport network. The proposed hinterland intermodal routing service could help mitigating the environmental impact of the Colombian hinterland transport and the national transportation costs, increasing the nation’s competitiveness and sustainability through a value-added service of a PCS.
Given automated order systems, detailed characteristics of items and vehicles enable the detailed planning of deliveries including more efficient and safer loading of distribution vehicles. Many vehicle routing approaches ignore complex loading constraints. This paper focuses on the comprehensive evaluation of loading constraints in the context of combined Capacitated Vehicle Routing Problem and 3D Loading (3L-CVRP) and its extension with time windows (3L-VRPTW). To the best of our knowledge, this paper considers the currently largest number of loading constraints meeting real-world requirements and reducing unnecessary loading efforts for both problem variants. We introduce an approach for the load bearing strength of items ensuring a realistic load distribution between items. Moreover, we provide a new variant for the robust stability constraint enabling better performance and higher stability. In addition, we consider axle weights of vehicles to prevent overloaded axles for the first time for the 3L-VRPTW. Additionally, the reachability of items, balanced loading and manual unloading of items are taken into account. All loading constraints are implemented in a deepest-bottom-left-fill algorithm, which is embedded in an outer adaptive large neighbourhood search tackling the Vehicle Routing Problem. A new set of 600 instances is created, published and used to evaluate all loading constraints in terms of solution quality and performance. The efficiency of the hybrid algorithm is evaluated by three well-known instance sets. We outperform the benchmarks for most instance sets from the literature. Detailed results and the implementation of loading constraints are published online.
In this paper, we investigate the Multi-Depot Heterogeneous VRP with Backhauls. Though the problem is a generalisation of three existing routing problems, this is the first time this combined routing problem is investigated. A mathematical formulation is first presented followed by some tightening. A powerful and novel hybridisation of Variable Neighbourhood Search (VNS) with the Greedy Randomized Adaptive Memory Programming Search is proposed. As there are no problem instances available for bench-marking and evaluation purposes, we generated data sets by combining those from existing vehicle routing problems. The proposed meta-heuristic obtains a number of optimal solutions for small instances and yields about gap from the lower bounds compared to nearly and average gap values for our CPLEX implementation and the VNS without hybridisation, respectively.
The service time of the vessels is one of the main indicators of ports’ competitiveness. This, together with the increasing volume of bulk transportation, make the efficient management of scarce resources such as berths a crucial option for enhancing the productivity of the overall terminal. In real scenarios, the information available to port operators may vary once the planning has been elaborated. Unforeseen events, errors, or modifications in the available information can lead to inefficient terminal management and the initial scheduling might become unfeasible. This implies that the use of deterministic approaches may not be enough to maximize productivity. Therefore, in this work, proactive simulation–optimization approaches that utilize the information collected during the simulation for guiding the optimization search to provide robust solutions are proposed. Moreover, a multi-objective approach based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for jointly tackling the problem objective as well as the deviations because of stochastic changes is developed. Finally, we also investigate the contribution of time management strategies such as buffers to absorb stochastic modifications and hence increase solutions’ robustness. The computational results indicate, on the one hand, the benefit of integrating both types of objectives (i.e., deterministic and stochastic) to guide the simulation–optimization process, and on the other hand, the benefit of using the multi-objective approaches like NSGA-II. Finally, the incorporation of buffers leads to better performance in terms of reducing penalty costs due to disruptions, shortening the planning risks related to only considering deterministic planning.
On-demand microtransit services are frequently seen as an important tool in supporting first and last mile operations surrounding fixed route high frequency transit facilities, but questions remain surrounding who will use these novel services and for what purposes. In November 2019, the Utah Transit Authority launched an on-demand microtransit service in south Salt Lake County in partnership with a private mobility operator. This paper reports the results of an expressed preferences survey of 130 transit riders in the microtransit service area that was collected before and immediately after the service launched. There is not a clear relationship between current transit access mode and expressed willingness to use microtransit, although some responses from new riders indicate the novel service competes most directly with commercial transportation network company operations. The survey responses also reveal younger passengers express a more than expected willingness to use microtransit, middle-aged passengers a less than expected willingness, and older passengers neutral or no expressed opinion. The results suggest additional relationships between household size and transit use frequency, but further research is necessary. The effect of other user characteristics, including income and automobile availability, is less statistically clear and requires further research.
Connected and Automated Mobility will disrupt the road transport sector which has remained substantially unchanged since the vehicle became mass produced in the first half of the twentieth century. Given the central role of mobility for our society and economy, the implications of a transformation in the transport sector will not be limited to transport but will regard many other aspects of our society. The aim of this paper is to analyse the possible future socio-economic implications of such a disruption in the road transport sector. It builds upon a set of future road transport scenarios and aims to identify the main impacts in different economic sectors mostly from a qualitative perspective. Results show that the deployment of Connected and Automated Vehicles could provide profitable opportunities for sectors like automotive, electronics and software, telecommunication, data services, digital media and freight transport; but other sectors including insurance and maintenance and repair are identified as businesses that might suffer revenue decreases in the future. In all scenarios, the policy implications of the transformation can be strong, which implies that the evolution of the transport system has to be carefully monitored in order to promptly cope with possible future effects.
Long-haul continental freight flows still heavily rely on unimodal road transport. Intermodal transport, combining road transport with other transport modes, has the potential to have lower operating costs and to be more environmentally sustainable. However, road transport benefits from its better flexibility and adaptability to sudden disruptions and uncertainties. To facilitate a modal shift towards intermodal transport, it is crucial to improve its resilience (i.e., capability to resist and recover from sudden disruptions). Synchromodality is an extension of intermodality in which decisions on modal choice and routing are not predefined long in advance but are taken based on real-time information and may provide a step in that direction. The conducted literature review investigates how uncertainty can be handled in intermodal and synchromodal freight transport networks. The literature is classified based on the planning level, which is either strategic, tactical or operational. The main focus is on the studied types of uncertainty and the proposed solution approaches. This work contributes to the research field by reviewing the literature on intermodal and synchromodal transport with uncertainty, presenting measures to mitigate the effects of uncertainty and proposing future research directions.
The Multi-Depot Green Vehicle Routing Problem (MDGVRP) is an extension of the well-known Green Vehicle Routing Problem (GVRP) where a fleet of alternative fuel-powered vehicles (AFVs) are used to serve the customers. GVRP consists of determining AFV tours such that the total distance travelled is minimum. The AFVs depart from the depot, serve a set of customers, and complete their tours at the depot without exceeding their driving range and the maximum tour duration. AFVs may refuel en-route at public refueling stations. In MDGVRP, the AFVs are dispatched from different depot locations and may refuel during the day at any depot or refueling station. We formulate MDGVRP as a mixed integer linear programming model and develop a hybrid General Variable Neighborhood Search and Tabu Search approach by proposing new problem-specific neighborhood structures to solve the problem effectively. We assess the performance of our method using the GVRP dataset from the literature. Our results show that the proposed method can provide high quality solutions in short computation times. Then, we extend these instances to the multi-depot case and compare our solutions for small-size instances with the optimal solutions. We also report our results for large-size problems and investigate the trade-offs associated with operating multiple depots and adopting different refueling policies to provide further insights for both academicians and practitioners.
The reduction of greenhouse gas emissions is one of the greatest global challenges through 2050. Besides greenhouse gas emissions, air pollution, such as nitrogen oxide and particulate matter emissions, has gained increasing attention in agglomerated areas with transport vehicles being one of the main sources thereof. Alternative fuels that fulfill the greenhouse gas reduction goals also offer the possibility of solving the challenge of rising urban pollution. This work focuses on the electric drive option for heavy and light duty vehicle freight transport. In this study, fuel cell-electric vehicles, battery-electric vehicles and overhead catenary line trucks were investigated, taking a closer look at their potential to reduce greenhouse gas emissions and air pollution and also considering the investment and operating costs of the required infrastructure. This work was conducted using a bottom-up transport model for the federal state of North Rhine-Westphalia in Germany. Two scenarios for reducing these emissions were analyzed at a spatial level. In the first of these, selected federal highways with the highest traffic volume were equipped with overhead catenary lines for the operation of diesel-hybrid overhead trucks on them. For the second spatial scenario, the representative urban area of the city of Cologne was investigated in terms of air pollution, shifting articulated trucks to diesel-hybrid overhead trucks and rigid trucks, trailer trucks and light duty vehicles to battery-electric or fuel cell-electric drives. For the economic analysis, the building up of a hydrogen infrastructure in the cases of articulated trucks and all heavy duty vehicles were also taken into account. The results showed that diesel-hybrid overhead trucks are only a cost-efficient solution for highways with high traffic volume, whereas battery overhead trucks have a high uncertainty in terms of costs and technical feasibility. In general, the broad range of costs for battery overhead trucks makes them competitive with fuel cell-electric trucks. Articulated trucks have the highest potential to be operated as overhead trucks. However, the results indicated that air pollution is only partially reduced by switching conventional articulated trucks to electric drive models. The overall results show that a comprehensive approach such as fuel cell-electric drives for all trucks would most likely be more beneficial.
A transit trip involves travel to and from transit stops or stations. The quality of what are commonly known as first and last mile connections (regardless of their length) can have an important impact on transit ridership. Transit agencies throughout the world are developing innovative approaches to improving first and last mile connections, for example, by partnering with ride-hailing and other emerging mobility services. A small but growing number of transit agencies in the U.S. have adopted first and last mile (FLM) plans with the goal of increasing ridership. As this is a relatively new practice by transit agencies, a review of these plans can inform other transit agencies and assist them in preparing their own. Four FLM plans were selected from diverse geographic contexts for review: Los Angeles County Metropolitan Transportation Authority (LA Metro), Riverside (CA) Transit Agency (RTA), and Denver Regional Transit District (RTD), and City of Richmond, CA. Based on the literature, we developed a framework with an emphasis on transportation equity to examine these plans. We identified five common approaches to addressing the FLM issue: spatial gap analysis with a focus on socio-demographics and locational characteristics, incorporation of emerging mobility services, innovative funding approaches for plan implementation, equity and transportation remedies for marginalized communities, and development of pedestrian and bicycle infrastructures surrounding transit stations. Strategies in three of the plans are aligned with regional goals for emissions reductions. LA Metro and Riverside Transit incorporate detailed design guidelines for the improvement of transit stations. As these plans are still relatively new, it will take time to evaluate their impact on ridership and their communities' overall transit experience.
Achieving the CO2 reduction targets for 2050 requires extensive measures being undertaken in all sectors. In contrast to energy generation, the transport sector has not yet been able to achieve a substantive reduction in CO2 emissions. Measures for the ever more pressing reduction in CO2 emissions from transportation include the increased use of electric vehicles powered by batteries or fuel cells. The use of fuel cells requires the production of hydrogen and the establishment of a corresponding hydrogen production system and associated infrastructure. Synthetic fuels made using carbon dioxide and sustainably-produced hydrogen can be used in the existing infrastructure and will reach the extant vehicle fleet in the medium term. All three options require a major expansion of the generation capacities for renewable electricity. Moreover, various options for road freight transport with light duty vehicles (LDVs) and heavy duty vehicles (HDVs) are analyzed and compared. In addition to efficiency throughout the entire value chain, well-to-wheel efficiency and also other aspects play an important role in this comparison. These include: (a) the possibility of large-scale energy storage in the sense of so-called ‘sector coupling’, which is offered only by hydrogen and synthetic energy sources; (b) the use of the existing fueling station infrastructure and the applicability of the new technology on the existing fleet; (c) fulfilling the power and range requirements of the long-distance road transport.
A growing scholarship argues that decarbonization cannot be achieved with single instruments like carbon pricing alone. A broader mix of reinforcing policies is required. This literature focuses on how policies can accelerate technological innovation, restrict polluting activities, promote green growth, and ensure social justice. Applying the policy mix literature to the European Union (EU), this article examines the development of climate and energy policies from separate and narrow initiatives to coordinated policy packages to achieve increasingly ambitious climate targets, culminating with the European Green Deal. The starting point to explain this policy development is that EU policies will reflect the positions of the ‘least ambitious’ actors when unanimity is required. Examination of different policy phases shows that EU policy mixes are not only needed to fulfil different transition functions—they also provide opportunities to combine different actor interests to raise climate ambitions. The EU institutions have been instrumental in crafting policy packages that exempt and compensate the least climate-ambitious actors. The Paris Agreement has also provided an enabling context for higher EU ambitions. Looking towards the future, the corona-induced recession has so far mainly been used by the EU as an opportunity to strengthen climate ambitions and the European Green Deal.
The present article outlines the core technologies needed to realize the Hyperloop transportation system (HTS). Currently, the HTS vacuum tube train concept is viewed as the fastest way to cross the earth’s surface. However, the concept has not yet been demonstrated for subsonic or near-sonic speeds in large-scale implementations. Among the challenging technical areas are the tube’s depressurization, the capsule’s air resistance, and choked flows occurring around the capsule. There is also a need for effective levitation and propulsion solutions compatible with the velocities being proposed. Moreover, several conflicting objectives have been identified in the HTS design. It is highlighted that the tubes should be wide enough to lower the capsule’s drag forces but that a large-sized tube comes at the expense of higher operational energy costs and infrastructure investments. Another struggle is aiming at a low-cost passive track design and, and at the same time, a lightweight vehicle. One technical path is to turn the whole guideway into an electric propulsor, arriving at a ’lightweight capsule solution’ (LCS). Alternatively, the vehicle could be transformed into a ’modified airplane’ that stores massive amounts of onboard energy, yielding an energy-autonomous ’low infrastructure solution’ (LIS). In a case study, it is shown that even LIS technology is compatible with short-haul flights (1500km) but that it requires an energy reservoir of about 30% of the capsule’s overall mass. Results and discussions presented in this paper are supported by analytical predictions with parameters or input data either supported by referred simulations or experiments. In general, the paper aims to open up the discussion, provide a sufficient understanding of the Hyperloop field’s multidisciplinary aspects, and establish a foundation for further investigations. In the end, new research tasks have been defined, which have the potential to go beyond the state of current knowledge.
Efficiently handling last-mile deliveries becomes more and more important nowadays. Using drones to support classical vehicles allows improving delivery schedules as long as efficient solution methods to plan last-mile deliveries with drones are available. We study exact solution approaches for some variants of the traveling salesman problem with drone (TSP-D) in which a truck and a drone are teamed up to serve a set of customers. This combination of truck and drone can exploit the benefits of both vehicle types: the truck has a large capacity but usually low travel speed in urban areas; the drone is faster and not restricted to street networks, but its range and carrying capacity are limited. We propose a compact mixed-integer linear program (MILP) for several TSP-D variants that is based on timely synchronizing truck and drone flows; such an MILP is easy to implement but nevertheless leads to competitive results compared with the state-of-the-art MILPs. Furthermore, we introduce dynamic programming recursions to model several TSP-D variants. We show how these dynamic programming recursions can be exploited in an exact branch-and-price approach based on a set partitioning formulation using ng-route relaxation and a three-level hierarchical branching. The proposed branch-and-price can solve instances with up to 39 customers to optimality outperforming the state-of-the-art by more than doubling the manageable instance size. Finally, we analyze different scenarios and show that even a single drone can significantly reduce a route’s completion time when the drone is sufficiently fast.
Medium and heavy-duty battery electric trucks (BETs) may play a key role in mitigating greenhouse gas (GHG) emissions from road freight transport. However, technological challenges such as limited range and cargo carrying capacity as well as the required charging time need to be efficiently addressed before the large-scale adoption of BETs. In this study, we apply a geospatial data analysis approach by using a battery electric vehicle potential (BEVPO) model with the datasets of road freight transport surveys for analyzing the potential of large-scale BET adoption in Finland and Switzerland for trucks with gross vehicle weight (GVW) of over 3.5 t. Our results show that trucks with payload capacities up to 30 t have the most potential for electrification by relying on the currently available battery and plug-in charging technology, with 93% (55% tkm) and 89% (84% tkm) trip coverage in Finland and Switzerland, respectively. Electric road systems (ERSs) would be essential for covering 51% trips (41% tkm) of heavy-duty trucks heavier than 30 t in Finland. Furthermore, range-extender technology could improve the trip electrification potential by 3–10 percentage points (4–12 percentage points of tkm).
The logistic network design is an abstract optimization problem that, under the assumption of minimal cost, seeks the optimal configuration of the supply chain’s infrastructures and facilities based on customer demand. Key economic decisions are taken about the location, number, and size of manufacturing facilities and warehouses based on the optimal solution. Therefore, improvements in the methods to address this question, which is known to be in the NP-hard complexity class, would have relevant financial consequences. Here, we implement in the D-Wave quantum annealer a hybrid classical-quantum annealing algorithm. The cost function with constraints is translated to a spin Hamiltonian, whose ground state encodes the searched result. As a benchmark, we measure the accuracy of results for a set of paradigmatic problems against the optimal published solutions (the error is on average below ), and the performance is compared against the classical algorithm, showing a remarkable reduction in the number of iterations. This work shows that state-of-the-art quantum annealers may codify and solve relevant supply-chain problems even still far from useful quantum supremacy.
A car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. The data preprocessing is presented in order to automatically remove fake demands caused by search failures and application errors. Then, the real demand is forecasted using a deep learning approach, Bidirectional Gated Recurrent Unit. Finally, the Minimum Cost Maximum Flow algorithm is deployed to maximize forecasted demands, while minimizing the amount of relocations. Furthermore, the system is deployed in the real use case, entitled “CU Toyota Ha:mo,” which is a car-sharing system in Chulalongkorn University. It is based on a web application along with rule-based notification via Line. The experiment was conducted based on the real vehicle usage data in 2019. By comparing in real environment in November of 2019, the results show that our model even outperforms the manual relocation by experienced staff. It achieved a 3% opportunity loss reduction and 3% less relocation trips, reducing human effort by 17 man-hours/week.
Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Due to supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately since service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles, however, the characteristics of mobility services change and new opportunities to overcome the prevailing limitations arise. In this paper, we consider an autonomous ridesharing problem in which idle vehicles are hired on-demand in order to meet the service level requirements of a heterogeneous user base. In the face of uncertain demand and idle vehicle supply, we propose a learning-based optimization approach that uses the dual variables of the underlying assignment problem to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. These approximations are used in the objective function of the optimization problem to dispatch, rebalance, and occasionally hire idle third-party vehicles in a high-resolution transportation network of Manhattan. The results show that the proposed policy outperforms a reactive optimization approach in a variety of vehicle availability scenarios while hiring fewer vehicles. Moreover, we demonstrate that mobility services can offer strict service level contracts (SLCs) to different user groups featuring both delay and rejection penalties.
The purpose of the work was to provide an overview on craft beer. Details and issues concerning history and legal definition market, fiscal policy, innovation, safety, healthiness, consumer profile, and sustainability are supplied. The term “craft brewery” generally refers to a brewery able to produce low volumes of beer, often made with traditional ingredients—for emulating historic styles—but also with the addition of nontraditional ingredients as a distinctiveness sign of the master brewer. In many countries, the importance of the company size is related to the opportunity to take advantage of reduced excise rates for low production volumes. In several countries, another important requisite of a craft brewery is represented by its independence from other alcohol industry members. Even in the presence of a great heterogeneity of the size of craft breweries in the various countries, their number in the world is around 17,000. Craft beer is mainly consumed in restaurants and bars. Innovation of craft beer concerns aspects, such as ingredients, alcohol content, aging, and packaging, and the profile of the typical craft beer drinker is that of a young man, with a higher education and a medium‐high income. Craft beers are often not filtered/not pasteurized and, for these reasons, they are beverages rich in health compounds but with a reduced shelf life. As in the case of larger breweries, the environmental impact of craft breweries is mainly represented by water consumption and production of liquid and solid wastes.
In recent years, the development of new algorithms for multiobjective optimization has considerably grown. A large number of performance indicators has been introduced to measure the quality of Pareto front approximations produced by these algorithms. In this work, we propose a review of a total of 63 performance indicators partitioned into four groups according to their properties: cardinality, convergence, distribution and spread. Applications of these indicators are presented as well.
Barge transport suffers from a high degree of uncertainty resulting from unreliable container arrivals (e.g. due to deep sea vessel delays), and dynamism that leads to limited information availability during planning. This paper studies the impact of uncertainty and dynamism using real data of an inland terminal. Our method consists of iterating weekly operations for one year to evaluate the long term performance. Each iteration involves solving a stochastic program for planning barge calls, and then simulating actual events. We show that uncertainty has an impact of up to 53% and dynamism up to 20% on total costs.
In supply chain inventory management it is generally accepted that safety stocks are a suitable strategy to deal with demand and supply uncertainty aiming to prevent inventory stock-outs. Safety stocks have been the subject of intensive research, typically covering the problems of dimensioning, positioning, managing and placement. Here, we narrow the scope of the discussion to the safety stock dimensioning problem, consisting in determining the proper safety stock level for each product. This paper reports the results of a recent in-depth systematic literature review (SLR) of operations research (OR) models and methods for dimensioning safety stocks. To the best of our knowledge, this is the first systematic review of the application of OR-based approaches to investigate this problem. A set of 95 papers published from 1977 to 2019 has been reviewed to identify the type of model being employed, as well as the modeling techniques and main performance criteria used. At the end, we highlight current literature gaps and discuss potential research directions and trends that may help to guide researchers and practitioners interested in the development of new OR-based approaches for safety stock determination.
This paper addresses a real-life short-term rescheduling problem of helicopter flights from one onshore airport to several maritime units in the context of the oil industry. This is a complex and challenging problem to solve because of the particular characteristics observed in practice, such as pending flights transferred from previous days with different recovering priorities, changes in flight timetables and helicopter assignments previously planned for the current day, time windows and minimum time intervals between take-offs from the airport and the maritime units, mandatory flight precedence, maximum flight delays, among many others. The problem consists of determining a daily flight reschedule that satisfies operational constraints and recovers all pending flights, while minimizing flight delays and costs related to helicopter usage and reassignments. We propose two mixed integer programming models to formulate the problem with all relevant characteristics, one based on the extension of traditional network flow models and other that relies on a novel event-based representation of the problem. Additionally, we develop an effective heuristic approach based on constructive and improvement heuristics, able to produce high-quality solutions within acceptable computational times. The results of computational experiments with real-life data provided by an oil company highlight the potential of the proposed approaches to support decision making in this context.
Underground logistics systems (ULSs) has been an important topic and research hotspot for urban underground space use in China, especially for implementation in new cities and districts, as it is a necessary element to be considered for city underground space planning. Since these newly planned areas will face many challenges, including increasing logistics volume, spatial constraints etc. if we use the existing freight transport modes similar to old cities. Furthermore, the rapid developments of Chinese new cities provides an opportunity to implement innovative ULS freight transport systems and technology that are efficient and sustainable. Recently, ULS has been included officially in the planning and study for Beijing’s Sub-Center, the Yangtze River Demonstration District and other new cities and districts. However, the real value of ULS will not materialize unless it is successfully implemented. This paper presents the exploration, observations and findings of a few proposed ULS planning and research works in the new districts mentioned. The suitability of goods and freight types in ULS, logistic flow demand forecasting and ULS network systems integrated with underground metro or infrastructure are discussed. Both dedicated and integrated ULS network systems are also compared and analyzed. Despite the many advantages, many challenges and difficulties remain in the planning integration, technology adaptation and implementation of ULSs in new districts and cities, thus, interdisciplinary collaborations is one of the key success factors to ensure ULS can be brought to fruition.
Scheduling is a universal theme being conferred in technological areas like computing and strategic areas like operational management. The core idea behind scheduling is the distribution of shared resources across time for competitive tasks. Optimization, efficiency, productivity and performance are the major metrics evaluated in scheduling. Effective scheduling under uncertainty is tricky and unpredictable and its an interesting area to study. Environmental uncertainty is a challenging extent that effect scheduling based decision making in work environments where environment dynamics subject to numerous fluctuations frequently. Reinforcement Learning is an emerging field extensively research on environmental modelling under uncertainty. Optimization in dynamic scheduling can be effectively handled using Reinforcement learning. This paper presents a research that accompanies Multi-agent reinforcement learning (MARL) and extends Q-learning, which aims to provide a solution for the dynamic task scheduling in an uncertain environment. The proposed solution approaches as a model-based Deep Dyna-Q+ algorithm-based hybrid solution to solve the scheduling problem in an unexpected complex environment. The Dyna-Q+ based scheduler includes priority-indexing based task scheduler which allocates jobs to individual cooperative agents sequentially in adaptive mode. The proposed model-based solution is comparatively evaluated against benchmark methods; Q-Learning and Dyna-Q+ learning algorithms for dynamic task scheduling. The initial evaluation results verify that the approach would be an effective solution for scheduling optimization in a dynamic and complex environment.
As a promising sustainable mode of transportation, electric carsharing can relieve the urban traffic pressure and environmental pollution problems, which has been promoted all over the world to reduce or replace fossil-fueled private cars. However, the operation of electric carsharing is faced with difficulties in profitability and management. Vehicle relocation can alleviate the imbalance between supply and demand, and compared with operator-based relocation, user-based relocation is more sustainable and cost-efficient. In this paper, we develop a user-based relocation model with the optimization objectives of profit maximization and using failure rate minimization. Two station pricing schemes of pure preferential and preferential combined with fine are proposed as the constraints. For the input of the relocation model, the back propagation neural network of the quantity demand and the distribution fitting model of the energy demand are constructed to predict user demand. The demand prediction model and relocation model are validated through the operation data of EVCARD in Shanghai. The results demonstrate that the user-based relocation can effectively reduce the using failure rate and has the potential to increase the net profit. Compared with the situation without vehicle relocation, the user-based relocation strategy can reduce using failure rate by 13.6% and increase net profit by 76.9% in the best situation. Through sensitivity analysis of key parameters, it is found that the station pricing of pure preferential can reduce the using failure rate, but the profitability of the electric carsharing system is weakened. The introduction of fine can effectively slow down the erosion of user incentive on the system profit and increase the profit.
In the air cargo context, pallet loading faces substantial constraints and item heterogeneity. The stability constraint in the pallet loading problem is highly important due to its impact on the efficiency, security, and resulting costs of an air cargo company. In information systems that support pallet loading, physical simulations provide a realistic approximation of a pallet’s stability. However, current approaches neglect the opportunity to integrate physical simulations in underlying solvers. In this research, we propose and compare two approaches for integrating a physical simulation as a fixed component of the problem-solving heuristic and include irregular shapes. Our results achieve runtimes that meet air cargo requirements; therefore, assumptions about the cargo, e.g., shape assumptions, can be relaxed.
The aim of this research is to present an integer linear programming model for solving the school bus routing problem concerning the travel distance of all buses and the student assignment to each bus stop which depends on two views. Students’ satisfaction is more interested than the total travel distance of buses in the first model, and vice versa in the second model.
Purpose
This study aims to propose a framework for developing strategies for the supply chain of craft beer that can make the business efficient and profitable, and at the same time, generate sustainability benefits from reducing waste, conserving natural resources and reducing pollution.
Design/methodology/approach
Based on an extensive review of the literature of academic and industry publications, source material from craft brewers primarily situated in the USA and industry experience in craft brewing, the proposed framework describes strategies to establish sustainable craft beer supply chains.
Findings
The framework for craft beer supply chain consists of four categories that contribute to craft beer sustainability: ingredient procurement, recycling efforts, energy usage and distribution systems – some of these mimicking those used by macrobrewers. Each of the categories is further subdivided. Successful practices and examples are highlighted for each of the subcategories.
Research limitations/implications
This proposed framework was built upon current practices and available literature in the USA and focused on the environmental pillar of sustainability. Further, the proposed framework arises from the fact that current best practices in sustainability were available primarily from larger craft brewers, like Sierra Nevada and New Belgium.
Practical implications
By paying attention to operational changes in their supply chains, craft brewers can manage costs and improve their sustainability track record by reducing waste, conserving natural resources and improving upon their pollution footprint. Craft brewers can economize in the use of water, grains, hops and yeast by using practices discussed in this paper.
Originality/value
This is the first time that all aspects of supply chain and sustainability considerations in craft beer production are discussed in a comprehensive manner to propose a framework for analysis and enhancement of productivity and sustainability at the same time. The fact that the proposed framework can be used in future studies to empirically evaluate the utility of various sustainability strategies adds to the originality and value of this research.
A warehouse covers a wide spectrum of operations for the distribution of goods in a supply chain network. The advancement of technology and the changing global business environment have compelled the transformation of a warehouse. The present study attempts to revisit the warehouse transformation from 1990 to 2019 through an evolutionary lens. A systematic literature review is conducted to answer a few basic research questions: what were the issues that warehouses faced during the time period, and how did the academic world approach it? And what would be the research agenda for the warehousing in the era of Industry 4.0? The analysis of the literature shows that warehousing research has changed from a traditional storeroom to a more automated and integrated warehousing system characterised by better efficiency and effectiveness. This study contributes to the development of warehousing research by discussing the development trends, addressing the research gaps, and recommending future research directions. The study also reflects the dominance of developed countries in warehousing research and alludes to more opportunities for practitioners and academicians in developing countries. Based on the decade-wise analysis of literature, an evolutionary framework for warehouse research is proposed which is expected to ensure the supply chain resilience proactively.
The flying sidekick traveling salesman problem (FS-TSP) is a parcel delivery problem arising in the last-mile logistics, where the distribution plan of a driver-operated truck assisted by a drone (unmanned aerial vehicle, UAV) has to be defined. The FS-TSP is a variant of the TSP where routing decisions are integrated with customer-to-drone and customer-to-truck assignment decisions and truck-and-drone synchronization constraints. The objective is the minimization of the time required to serve all the customers, taking into account drone payload capacity and battery power constraints. In this work we provide a new representation of the FS-TSP based on the definition of an extended graph. This representation allows to model the problem by a new and compact integer linear programming formulation, where the synchronization issue is tackled in a column generation fashion, thus avoiding the usage of big-M constraints, representing one of the main drawbacks of the models present in literature. The proposed formulation has been solved by an exact approach which combines a Branch-and-Cut algorithm and a column generation procedure, strengthened by variable fixing strategies and new valid inequalities specifically defined for the problem. The proposed method has been experienced on a large set of benchmark instances. Computational results show that the proposed approach either is competitive or outperforms the best exact approach present in literature for the FS-TSP. Indeed, it is able to provide the optimal solution for all small size instances with 10 customers and for several medium size instances with 20 customers, some of them never solved before.
Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles. Since the number of request combinations increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such approaches have to employ ad hoc heuristics to identify a subset of request combinations for assignment. Our key contribution is in developing approaches that employ zone (abstraction of individual locations) paths instead of request combinations. Zone paths allow for generation of significantly more “relevant” combinations (in comparison to ad hoc heuristics) in real-time than competing approaches due to two reasons: (i) Each zone path can typically represent multiple request combinations; (ii) Zone paths are generated using a combination of offline and online methods. Specifically, we contribute both myopic (ridesharing assignment focussed on current requests only) and non-myopic (ridesharing assignment considers impact on expected future requests) approaches that employ zone paths. In our experimental results, we demonstrate that our myopic approach outperforms the current best myopic approach for ridesharing on both real-world and synthetic datasets (with respect to both objective and runtime). We also show that our non-myopic approach obtains 14.7% improvement over existing myopic approach. Our non-myopic approach gets improvements of up to 12.48% over a recent non-myopic approach, NeurADP. Even when NeurADP is allowed to optimize learning over test settings, results largely remain comparable except in a couple of cases, where NeurADP performs better.
This paper presents two hybrid metaheuristics to solve a multiproduct two‐stage capacitated facility location problem (MP‐TSCFLP). In this problem, a set of different products must be transported from a set of plants to a set of intermediate depots (first stage) and from these depots to a set of customers (second stage). The objective is to minimize the cost related to open plants and depots plus the cost for transporting the products from the plants to the customers satisfying demand and capacity constraints. Recently, the methods clustering search (CS) and biased random‐key genetic algorithm (BRKGA) were successfully applied to solve a single‐product problem (SP‐TSCFLP). Therefore, in this paper we propose adaptations and implementations of these methods for handling with a multiproduct approach. To the best of our knowledge, CS and BRKGA presented the best results for the SP‐TSCFLP and both have not yet been applied to solve the problem with multiple products. Four sets of large‐sized instances with different characteristics are proposed and computational experiments compare the obtained results to those from a commercial solver.
Mobility solutions like ride-sharing and carpooling are becoming popular in many urban and metropolitan areas around the globe. These solutions, however, create many operational challenges that need to be solved in order to make them more efficient and sustainable in time, e.g.: determining the number and location of parking slots, finding the optimal routes in terms of time or emissions, or developing synchronized schedules among ride-sharing users. This paper provides an updated review on car-sharing optimization studies (including ride-sharing and carpooling), compares different analytical approaches in this research area, and discusses the emerging concept of 'agile' algorithms as one of the approaches that might contribute to deal with the requirements of large-scale and dynamic car-sharing optimization problems.
This chapter aims to explore how the concept of sustainability is perceived and practiced in the craft beer industry in Italy. For this purpose, we present the case of a craft brewery located in the north of Italy. Based on an interview with the brewer about the meaning of sustainability and the brewery’s sustainability practices, this chapter discusses how sustainability may be conceptualized and operationalized by craft breweries in Italy and how a sustainability path may be further developed and supported in this type of business.
Nowadays, microgrids (MG) have attracted much attention, as a key technology of the internet of energy (IoE). A great deal of research have shown that the hierarchical microgrid is a more novel structure of IoE. Although the hierarchical microgrid model solves the problem of weak power scheduling capability across microgrids, it suffers from severe communications uncertainty, which can lead to communication delay and fluctuation. To obtain the accurate result of the renewable energy accommodation assessment capacity, a hierarchical microgrid model considering communication uncertainty is proposed in this paper. The solution to solve the problem of the assessment renewable energy accommodation capacity for hierarchical microgrids is a hybrid control based on distribution deep reinforcement learning. The temporal difference generation adversarial network (TD-GAN) is proposed as a value based method. Compared with the policy based method, it can better solve the distributed problem in hybrid control with a generation adversarial network (GAN). Moreover, the challenge that the method cannot handle a continuous action space is solved by using a normalized advantage function (NAF). The method similar with the temporal difference (TD) error method is employed to train the GAN network. Simulation results using real power grid data demonstrate the effectiveness and accuracy of the proposed method.
This paper addresses the Single Container Loading Problem. We present an exact approach that considers the resolution of integer linear programming and constraint programming models iteratively. A linear relaxation of the problem based on packing in planes is proposed. Moreover, a comprehensive set of mathematical formulations for twelve practical constraints that arise in this problem are discussed. These constraints include complete shipment, conflicting items, priorities, weight limit, cargo stability, load-bearing, multi-drop, load-balancing, manual loading, grouping, separation, and multiple orientations. Extensive computational experiments are carried out on instances from the literature to show the performance of the proposed approach and state how each practical constraint affects the container’s occupancy, the approach runtime, and the number of packing patterns evaluated. In general, the approach could optimally solve instances with around ten items types and a total of 110 items, besides obtaining the optimal solution for more than 70% of all instances.
Electronic products are an essential part of modern society, but their importance has perhaps never been as palpable as when the COVID-19 pandemic forced almost every aspect of human interaction to go online. However, the pandemic also revealed that the supply chains that provide crucial raw materials for manufacturing electronics are increasingly vulnerable to social, geopolitical, and technical disruptions. These vulnerabilities are likely to escalate in the future, due to global health crises, natural disasters, and global political instability, all of which will be magnified by looming climate change impacts. This study investigates potential supply chain disruption risks in the electronics sector by applying metrics that capture supply, demand, socio-political, and environmental risks in a multi-criteria framework to almost 40 metals and minerals that provide critical functionality to electronic products. Results illustrate that the material risks varied with the potential nature of the disruption. For example, in scenarios where disruptions led to price volatility or weakening of environmental regulations, highest risks were observed for precious metals such as gold, rhodium, platinum, and palladium. On the other hand, in scenarios where disruptions led to supply pressures or geopolitical tensions, cobalt, gallium, and key rare earth elements exhibited the highest risks. These metals are characterized by energy-intense manufacturing and highly concentrated geographic production, suggesting that recycling and supply chain diversification may alleviate some of the identified risks. The analysis also considers trade-offs that may occur across social, economic, and environmental dimensions. For example, cobalt, a critical component in lithium-ion batteries, has significant social impacts due to production concentration in the Democratic Republic of the Congo. Shifting production to other regions may alleviate these risks but introduce new concerns about economic and environmental impacts.
In dynamic ride-sharing systems, intelligent repositioning of idle vehicles enables service providers to maximize vehicle utilization and minimize request rejection rates as well as customer waiting times. In current practice, this task is often performed decentrally by individual drivers. We present a centralized approach to idle vehicle repositioning in the form of a forecast-driven repositioning algorithm. The core part of our approach is a novel mixed-integer programming model that aims to maximize coverage of forecasted demand while minimizing travel times for repositioning movements. This model is embedded into a planning service also encompassing other relevant tasks such as vehicle dispatching. We evaluate our approach through extensive simulation studies on real-world datasets from Hamburg, New York City, and Manhattan. We test our forecast-driven repositioning approach under a perfect demand forecast as well as a naive forecast and compare it to a reactive strategy. The results show that our algorithm is suitable for real-time usage even in large-scale scenarios. Compared to the reactive algorithm, rejection rates of trip requests are decreased by an average of 2.5% points and customer waiting times see an average reduction of 13.2%.
This paper deals with the problem of allocating berth positions for vessels in export bulk port terminals considering tidal constraints and was first formulated by Ernst et al., (2017). This study investigates the dynamic and continuous berth allocation problem (BAP) with respect to tidal constraints (BAP_TC), and seeks to minimize the total service time of berthed vessels. Since the BAP problem is NP-hard the BAP_TC is also NP-hard. A reduced variable neighborhood search (RVNS) based approach is developed to solve the problem. For parameters tuning a machine learning algorithm is developed and used. Problem instances are benchmarked with CPLEX and the numerical experiments proved that the proposed algorithm is capable of generating high-quality solutions in rather short time. Both small and large-scale instances in the literature are tested to evaluate the metaheuristic effectiveness using other solution approaches from the literature. The computational experiment proves that the proposed algorithm provides state of the art results.
Road freight transportation literature increasingly concentrates on environmental aspects to reduce logistics contribution to carbon dioxide emissions. Some literature reviews highlight specific mitigation and adaptation strategies, but the overarching research directions to identify emerging areas and general trends of the field have not yet been clustered or synthesized. This paper presents a systematic quantitative review of the road freight transportation decarbonization literature leveraging bibliographic coupling and network analysis techniques. It contributes to the understanding of road freight decarbonization and provides recommendations for further investigations of the field by systematically mapping the literature body. This way, key research clusters are outlined and visualized to understand the underlying knowledge structure. The findings reveal a diverse and fast-growing research field, which in large parts focuses on route optimizations, last-mile solutions, and alternative fuels, while offering future research opportunities that address organizational barriers currently hindering collaboration and technological or operational measures for long- haul transportations.
Twelve-qubit quantum computing for chemistry
Accurate electronic structure calculations are considered one of the most anticipated applications of quantum computing that will revolutionize theoretical chemistry and other related fields. Using the Google Sycamore quantum processor, Google AI Quantum and collaborators performed a variational quantum eigensolver (VQE) simulation of two intermediate-scale chemistry problems: the binding energy of hydrogen chains (as large as H 12 ) and the isomerization mechanism of diazene (see the Perspective by Yuan). The simulations were performed on up to 12 qubits, involving up to 72 two-qubit gates, and show that it is possible to achieve chemical accuracy when VQE is combined with error mitigation strategies. The key building blocks of the proposed VQE algorithm are potentially scalable to larger systems that cannot be simulated classically.
Science , this issue p. 1084 ; see also p. 1054