EURO Journal on Transportation and Logistics最新文献

Establishing innovative fulfillment options for online orders has become a key challenge for bricks-and-mortar retailers. A mere focus on store sales is no longer affordable due to the competition by pure online players. Retailers are continuously developing new approaches for online order fulfillment and last-mile logistics. Further shortening lead times is becoming even more important in this context. One recently developed concept is the omnichannel approach where existing structures are utilized and distribution centers (DCs) and local stores are integrated into a holistic fulfillment concept. This concept is especially relevant when retailers are providing fast delivery services (e.g., same-hour delivery). It resembles a multi-depot vehicle routing problem where all facilities act as depots and orders are assigned based on processing and transportation costs as well as available delivery capacity.

We address this new concept and present the novel problem for rapid integrated order fulfillment in grocery retailing. We empirically identify decision-relevant costs for order processing in stores and develop an approach for the evaluation of overall fulfillment costs. Our work considers the order assignment to heterogeneous depots and vehicle routing for each depot depending on depot-specific fulfillment costs using a tailored cluster-first-route-second heuristic. We show that integrated rapid order fulfillment can reduce costs by an average of 7.4% compared to order fulfillment from DCs. However, as order processing costs in stores remain a significant cost factor, DCs will always have some relevance and cannot entirely be replaced by delivery from stores. Our results highlight the importance of modeling order processing costs in stores for actual order fulfillment decisions in a heterogeneous network.

To relieve human order pickers from unproductive walking through a warehouse, parts-to-picker systems deliver demanded stock keeping units (SKUs) toward picking workstations. In a wide-spread parts-to-picker setup, a crane-operated automated storage and retrieval system (ASRS) delivers bins with demanded SKUs toward an end-of-aisle picking workstation and returns them back into the rack once the picks are completed. We consider the scheduling of the crane that operates subsequent dual commands. Each dual command combines a retrieval request for another SKU bin demanded at the picking workstation with a storage request, where a bin that has already been processed and passed through the bin buffer is returned to its dedicated storage position in the ASRS. This system setup in general and the resulting crane scheduling problem in particular have been an active field of research for more than 30 years. We add the following contributions to this stream of research: We finally prove that the crane scheduling problem is strongly NP-hard. Furthermore, we show that, although only a single vehicle (namely, the crane) is applied, the problem is equivalent to the traditional vehicle routing problem (VRP). This opens the rich arsenal of very efficient VRP solvers, which substantially outperform existing tailor-made algorithms from the literature.

Although optimization techniques have been successfully applied for a number of airline operations, aircraft recovery remains a challenge for both practitioners and researchers due to its complexity and the usual need of a quick response in practical settings. In this paper, we consider the case of a Brazilian oil and gas company that uses a heterogeneous fleet of helicopters for passenger transportation from a few mainland aerodromes to maritime units. The major difficulties in rescheduling delayed flights are resource limitations and realistic features of the company combined with safety and management practices. The problem consists of determining joint daily flight reschedules for all aerodromes that satisfy operational constraints and recovers all pending flights, while minimizing flight transfers among aerodromes, usage of helicopters and overall flight delays. We propose a mixed integer programming (MIP) model, aiming to appropriately represent the problem, and MIP-based local search and two-phase heuristics to cope with larger realistic problem instances. Computational results obtained with instances collected in the case study show the potential of these approaches to deal with this real-life aircraft recovery problem.

With the current advances in aircraft design and Lithium-Ion batteries, electric aircraft are expected to serve as a replacement for conventional, short-range aircraft. This paper addresses the main operational challenges for short-range flights operated with electric aircraft: determining the investment needs for a fleet of electric aircraft, and the logistics of charging stations and swap batteries required to support these flights. A mixed-integer linear program with two phases is proposed. In the first phase, a schedule for flight and battery recharge is developed for a fleet of electric aircraft. In the second phase, optimal times for battery charging are determined, together with an optimal sizing of the number of charging stations and swap batteries. We illustrate our model for short-range flights to and from an European airport and for an electric aircraft designed based on the operational characteristics of a conventional, narrow-body aircraft.

A concept of relevance to spot markets for truck-based freight transportation services is the price of anarchy (PoA), which defines the performance inferiority of decentralized market mechanisms for consummating transactions (vis-à-vis centralized mechanisms). To examine this concept in the context of transportation spot markets, this study uses a combination of behavioral experiments and mathematical optimization. The paper's contributions to the research literature for that context involve (1) addressing the case of freight transportation service providers having service requests information spanning multiple periods; (2) analyzing multiple performance measures; (2) accounting for the performance effects of human behavior; and (3) introducing new behavioral experiments as well as novel and effective analytical procedures to tackle truck-to-load assignment problems. Among the paper's most salient findings is that when performance is measured as the market participants' financial outcome, human behavior in buyer-seller interactions could result in a doubling of the PoA.

Due to the increasing price pressure in the less-than-truckload (LTL) market, horizontal cooperation is an effective and efficient way for small- and medium-sized LTL carriers to enhance their profits by exchanging requests. For this purpose, a decentralized auction-based collaboration framework has proven to provide a good approach. In this paper, such a collaboration framework is adopted and extended by applying it to a practical-oriented routing problem, the Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets. Particularly, we analyse the bundle selection process made by the auctioneers, which is a stochastic problem and specify which requests are supposed to be offered together in a bundle to the cooperating carriers. For the purpose of solving the selection problem properly, we implement a new procedure required due to the characteristics of the considered routing problem: a scenario-based bundle selection approach. In order to make this approach applicable, two pre-selection techniques (cluster- and neural network-based) are developed. Our approach is evaluated on 240 collaboration network instances created from well-known pickup and delivery research data sets generated by Li and Lim (2001). The collaboration framework results are compared with respect to the profit to individual transportation planning of each carrier (lower threshold) as well as centralized transportation planning of all carriers (upper threshold). It can be shown that the auction-based collaboration approach is up to 43.49% better than the individual planning as well as exhausts at least 53.5% of the centralized transportation planning potential on average.

Evacuation drills are critical to evaluate emergency preparedness and infrastructure capacity. Before conducting drills, it is necessary to design the evacuation routes that people are likely to follow in a real evacuation. In this paper, we present a path-oriented optimization model for evacuation planning. To solve this model, we propose a column-oriented approach in which a master problem assigns people to evacuation paths, while an auxiliary problem generates feasible evacuation paths. To recreate possible evacuation drills, we embed the column generation method in an optimization-based simulation procedure that mimics the evacuation dynamics and unveils critical evacuation routes that are likely to become congested. To illustrate the applicability of our method, we recreated a real evacuation drill conducted in a university campus. Our results support evacuation planners that often make infrastructure decisions with the goal of enhancing evacuation dynamics with the commendable goal of saving lives.

Motivated by the need for transportation infrastructure and incident management planning, we study traffic density under non-recurrent congestion. This paper provides an analytical solution approximating the stationary distribution of traffic density in roadways where deterioration of service occurs unpredictably. The proposed solution generalizes a queuing model discussed in the literature to long segments that are not space-homogeneous. We compare single and tandem queuing approaches to segments of different lengths and verify whether each model is appropriate. A single-queue approach works sufficiently well in segments with similar traffic behavior across space. In contrast, a tandem-queue approach more appropriately describes the density behavior for long segments with sections having distinct traffic characteristics. These models have a comparable fit to the ones generated using a lognormal distribution. However, they also have interpretable parameters, directly connecting the distribution of congestion to the dynamics of roadway behavior. The proposed models are general, adaptable, and tractable, thus being instrumental in infrastructure and incident management.

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock.