EURO Journal on Transportation and Logistics最新文献

This paper proposes a two-stage model to tackle a problem arising in Waste Management. The decision-maker (a regional authority) is interested in locating sorting facilities in a regional area and defining the corresponding capacities. The decision-maker is aware that waste will be collected and brought to the installed facilities by independent private companies. Therefore, the authority wants to foresee the behaviour of these companies in order to avoid shortsighted decisions. In the first stage, the regional authority divides the clients into clusters, further assigning facilities to these clusters. In the second stage, an effective route is defined to serve client pickup demand. The main idea behind the model is that the authority aims to find the best location–allocation solution by clustering clients and assigning facilities to these clusters without generating overlaps. In doing so, the authority tries to (i) assign the demand of clients to the facilities by considering a safety stock within their capacities to avoid shortages during the operational phase, (ii) minimise Greenhouse Gases emissions, (iii) be as compliant as possible with the solution found by the second stage problem, the latter aiming at optimising vehicle tour lengths. After properly modelling the problem, we propose a matheuristic solution algorithm and conduct extensive computational analysis on a real-case scenario of an Italian region. Validation of the approach is achieved with promising results.

The availability of various services for individual mobility is increasing, especially in urban areas. Dynamic ride-hailing services address these aspects and are gaining market share with providers such as MOIA, UberX Share, Sprinti or BerlKönig. To be able to offer competitive pricing for such a service and at the same time provide a high service quality (e.g. fast response times), effective capacity management is needed. In order to reach this goal, two challenges have to be met by the service provider. On the one hand, a proper demand control has to be installed, which optimizes the responses to transportation requests from customers. On the other hand, suitable fleet control needs to be set in place to optimize the route of the fleet so that the demand can be met. Papers in the literature do solve both but typically focus on one of these two challenges. As an example, value function approximation (VFA) can be used to learn a service offering decision while anticipating future incoming requests. A typical example of a routing-focused method is the multiple scenario approach (MSA) creating a routing which anticipates future requests using a sampling method. In this paper, we combine VFA and MSA to address the two challenges in an effective way. The resulting method is called anticipatory-routing-and-service-offering (ARS). We find that the combined method significantly outperforms the individual components, improving not only the total reward but also the accepted requests. It is found that this performance is particularly high with a heavy workload and thus resources are relatively scarce. We analyse how and under which conditions the components together or individually are particularly important.

Panic-buying and shortages of essential commodities is common during early phases of a disaster or an epidemic. The goal of this paper is develop a methodology which includes social media information in optimization models of searching essential commodities during disasters and improves the efficiency of search. Specific contributions in the data processing of social media posts include the development of an event localizer that probabilistically infers the location and time of shortage of commodity based on social media information. Contributions in the mathematical model development include an integer programming formulation of the resultant search problem on a graph, with the two objective different objective functions: (a) Maximizing probability of finding the commodity (b) Minimizing expected time to find the commodity given the commodity is found. The methodology is validated using a case study on gasoline search during the Hurricane Irma evacuations. We found that social media posts can predict shortage at gas station for four major cities of Florida accurately with a MAPE of 12%. We also found that addition of social media information to the search process improved the average search time by 41.74%.

In this paper, we study an extension of the orienteering problem where travel times are random and time-dependent and service times are random. Additionally, the service at each selected customer is subject to a soft time window; that is, violation of the window is allowed but subject to a penalty that increases in the delay. A solution is a tour determined before the vehicle departs from the depot. The objective is to maximize the sum of the collected prizes net of the expected penalty. The randomness of the travel and service times is modeled by a set of scenarios based on historical data that can be collected from public geographical information services. We present an exact solution method for the problem based on a branch-and-bound algorithm enhanced by a local search procedure at the nodes. A numerical experiment demonstrates the merits of the proposed solution approach. This study is the first to consider an orienteering problem with stochastic travel times and soft time windows, which are more relevant than hard time windows in stochastic settings.

We introduce a novel multimodal (ground and air transportation) network design model with transshipments for the transport of express cargo with heterogeneous service classes (i.e., next morning delivery, and next day delivery). We formulate this problem using a novel path-based mixed-integer program which seeks to maximize the demand (weight) served. We investigate the value of the proposed transshipment network under various operational conditions and by benchmarking against a direct shipment network and a network with a single transshipment point which mimics a classical star-shaped hub-and-spoke network. Our extensive computational study with real-world data from ShunFeng (SF) Express reveals that the integration of ground and air transportation improves the coverage and that transshipment enables serving a large number of origin–destination pairs with a small number of cargo planes. Importantly, we show that by simplifying handling, i.e., employing cross-docking rather than time-consuming sortation, a transshipment network can transport express cargo fast enough to meet demanding delivery deadlines. Finally, we find that increasing the efficiency of intra-city operations and extending the nightly operating time window are the most effective operational adjustments for further improving the performance of the proposed transshipment network.

The routing of commodities is a tactical problem in supply chain management that aims to synchronise transportation services connecting a network of warehouses and consolidation locations. This paper considers the routing of commodities in a transportation network that is flexible in response to demand through changes to regional warehouse clustering and the designation of consolidation locations. Traditionally, warehouse clustering and consolidation locations are determined as part of strategic planning that is performed months to years in advance of operations—limiting the flexibility in transportation networks to respond to changes in demand. A mathematical programming-based algorithmic framework is proposed to integrate the strategic decisions of location planning with tactical decisions of vehicle routing and synchronisation. A multi-armed bandit problem is developed to explore warehouse clustering decisions and exploit those that lead to small transportation costs. An extensive computational study will show that the proposed algorithmic framework effectively integrates strategic and tactical planning decisions to reduce the overall transportation costs.

This study tries to find the contribution of the fleet standardization index of airline companies through the financial values of airlines in the COVID-19 pandemic crisis. One of the driving forces of this research is the stagnation in the aviation industry, along with the cessation of all international flights to prevent worse pandemic conditions. The main contribution of the study is clarifying the effect of fleet variety and its financial inferences about fleet standardization and crisis management relationship under inoperative service considerations. Data Envelopment Analysis (DEA) is used as a complementary method for standardization index calculations. According to the results, while the positive contribution of the fleet standardization index to operational revenues was observed, there was no sign of the effects of fleet standardization on the accounting performance of companies. Besides, fleet standardization enabled us to measure the response of Low-Cost Carrier (LCC) and Full-Service Carrier (FSC) airlines in crises. In this study, nine airline companies were selected from both trend representatives, and the results were presented for the U.S. It is found that fleet standardization is related to the adopted flight network type and profitability of each company directly. Results obtained revealed the relationship between fleet standardization and financial relations under inoperative conditions positively. In addition, inferences were made about the effects of the operational policies adopted by the companies.

Weather affects the work of air traffic controllers, however, for staff scheduling in Remote Tower Centers (RTCs) it has not been taken into account. We study the impact of various weather phenomena on air traffic controller (ATCO) taskload through structured interviews with ATCOs. We deduce taskload-driven impact factors and the corresponding thresholds for the intensity of the weather phenomena at several Swedish airports. To account for the uncertainty in the weather prediction, we obtain probabilistic weather data from Ensemble Prediction Systems (EPSs). Then we adjust our prior Mixed Integer Programming (MIP) model for RTC staff scheduling to account for uncertain impactful weather occurrences and yield a distribution for the necessary number of ATCOs for RTC staff scheduling. Our framework can be used for conventional towers as well.

We quantify the impact of weather by comparing the number of controllers necessary to operate at five Swedish airports from a remote tower during two example days in 2020, with and without taking weather events into account. In our calculations we use historical weather and flight data to show that ignoring weather impact may lead to significant understaffing at a RTC.

Current environmental issues that have been made unavoidable by environmental regulations have become new constraints for industrial companies. In this paper, we consider a joint production-location problem for supply chains under a carbon tax policy on transport-related carbon emissions. We characterize the relationship that links the production level to the input quantities by considering a production function, namely, constant elasticity of substitution (CES) function. Our study focuses on the potential impact of increased transportation costs due to carbon taxation on the joint production-location decision. We find that the location-production configuration differs according to the degree of substitutability among the raw material quantities. More importantly, we observe that a higher carbon tax is more likely to cause a significant jump in firm location choice and a considerable change in production decisions when a firm has high flexibility in its ability to substitute among input quantities.