Networks and Spatial Economics最新文献

We investigate the Meal Delivery Routing Problem (MDRP), managing courier assignments between restaurants and customers. Our proposed variant considers uncertainties in meal preparation times and future order numbers with their locations, mirroring real challenges meal delivery providers face. Employing a rolling-horizon framework integrating Sample Average Approximation (SAA) and the Adaptive Large Neighborhood Search (ALNS) algorithm, we analyze modified Grubhub MDRP instances. Considering route planning uncertainties, our approach identifies routes at least 25% more profitable than deterministic methods reliant on expected values. Our study underscores the pivotal role of efficient meal preparation time management, impacting order rejections, customer satisfaction, and operational efficiency.

Autonomous Mobility-on Demand (AMoD) combines self-driving and Mobility-on-Demand (MoD) services, allowing passengers to enjoy the last mile service. Due to the success of Uber, Lift, and other ride-sourcing companies, previous research has discussed pricing strategies in the ride-sourcing market. This study combines ridesharing and dynamic pricing strategy in the AMoD system, building models to maximize social welfare. Shared Autonomous Vehicles (SAV) ridesharing and dispatching models are constructed, and numerical experiments are conducted on a real road network using different factors. Linear regression models based on the simulation data from the dispatching model are established to predict the average waiting time and meeting rate. The regression models are applied to the ride-sourcing market model to conduct dynamic pricing experiments. We use approximate dynamic programming (ADP) to solve the dynamic pricing multiplier for each time interval. The experimental results show that ridesharing can improve the service rate of rides, and the dynamic pricing strategy achieves higher social welfare by balancing supply and demand compared to the fixed pricing strategy.

The study investigates if the advancements in the domestic servicification of manufacturing reflect the rise in Chinese servicification within the manufacturing sectors of its trade partners. In a broader context, the article verifies whether China is following global trends related to servicification and digitalization in GVCs with Europe. The research reveals a growing role for Chinese services in both developed and developing European economies, with a higher absorption observed in Western European countries due to their level of development. However, the study did not fully confirm that China is strengthening its role in European digital GVCs. The Chinese strategy of joining advanced GVCs is two-pronged, with heavy servicification with traditional services and considerable variation in the servicification with digital services.

With the emergence of e-commerce and the growing trend for online shopping, many retailers are adding online sales channel to their traditional brick-and-mortar stores. In this paper, we study the problem of fulfillment center location and network design for a retailer that seeks to integrate online sales channel into its traditional brick-and-mortar retail channel. The proposed analytical model seeks to locate fulfillment centers, select stores for online demand fulfillment, assign stores to fulfillment centers, and allocate safety stocks at fulfillment centers and stores to deal with variability in demand and lead time at minimum total cost. The problem is modelled as a mixed-integer nonlinear program, which is then reformulated as a mixed-integer second-order conic program. We present an exact branch-and-cut algorithm for the reformulated problem, in which extended polymatroid inequalities are separated to improve the convergence of the algorithm. Extensive computations under different cost scenarios and parameter settings confirm the efficiency of our exact algorithm. Through computational experiments, we showed that our algorithm reduces computation time, provides lower optimality gap, and outperforms the state-of-the art commercial solver on large instances. We conduct sensitivity analyses to understand the impact of variation in cost and model parameters and present managerial insights. The model also determines if the safety stock inventory should be pooled at the distribution centers (i.e. centralized inventory) and/or held at the stores (i.e. decentralized inventory). Using the model, we show the benefits of integrating fulfillment centers in the dual-channel network to serve in-store demand as well as online demand and its impact on facility location and network design decisions.

The minisum multifacility location problem is viewed as finding an embedding of a graph in a metric space under additional constraints such as a number of fixed vertex locations, minimising the sum of weighted lengths of all edges. We show that certain nontrivial minimal cuts in the graph are sets of nodes that will necessarily coincide at any or at some optimal solution, irrespective of the fixed locations and the metric. This new property strongly generalises all coincidence conditions known in literature. In fact we show that it is best possible for coincidence with a fixed vertex at any position in arbitrary metric spaces. For coincidence among free vertices a different property of graph symmetry is also sufficient, and we conjecture its best possibility in conjunction with the minimal cut condition. All such instance-independent coincidences (both in at least one and in all optimal solutions) may be determined efficiently.

Mobility patterns in an urban area can be defined as the trip making behavior of an urban population. Traditionally, the origin-destination matrix representation of travel demand, where trip ends are agglomerated toward zone centroids that are decided a priori, has historically been used to identify trip making behavior. In this paper, different agglomeration methods are explored to extract the trip making behavior and their performances are analyzed. First, a variant of the zone-based agglomeration method is proposed, in which zones are optimally located rather than having their locations determined beforehand. Then a trip-based agglomeration method is proposed, where each trip is represented as an ordered pair of origin and destination in the form of a line segment and agglomeration of these line segments is performed. The proposed line-based agglomeration method serves a two-fold purpose, (a) the proposed trip-based agglomeration method helps in identifying the corridors carrying the majority of the flow in a single step, as opposed to trip-end based agglomeration methods where several post-processing steps may be required to identify the corridors, and (b) this method performs better than the existing trip-end based agglomeration methods in terms of the number of corridors that are required to cover the given trips. Efficient algorithms are also developed to solve the proposed trip-based agglomeration method, their performance on real-world trip datasets is tested and finally, the properties of the proposed algorithms are explored.

We present a spatial and time-continuous Ramsey-type equilibrium model for households and firms that interact on a spatial domain to model labor mobility in the presence of commuting costs. After discretization in space and time, we obtain a mixed complementarity problem that represents the spatial equilibrium model. We prove existence of equilibria using the theory of finite-dimensional variational inequalities and derive a tailored diagonalization method to solve the resulting large-scale instances. Finally, we present a case study that highlights the influence of commuting costs and show that the model allows to analyze transitory effects of industrial agglomeration that emerge and vanish over time as in the real economy.

Emergency Mobility Facilities (EMFs) possess the capability to relocate dynamically, providing adequate responses to fluctuations in emergent demand patterns across temporal and spatial dimensions. This study proposes a two-stage stochastic programming model that integrates the EMF allocation problem and the road network design problem for disaster preparedness. The model takes into account uncertainties arising from emergency demand and road network congestion levels under various sizes and timings of disaster occurrences. The first-stage decision involves determining the fleet size of EMFs and identifying which road links’ travel time should be reduced. The second-stage decision pertains to the routing and schedule of each EMF for each disaster scenario. Due to considering various sources of uncertainty, the resulting model takes the form of a non-convex mixed-integer nonlinear program (MINLP). This poses computational challenges due to the inclusion of bilinear terms, implicit expressions, and the double-layered structure in the second-stage model, along with integer decision variables. A comprehensive set of techniques is applied to solve the model efficiently. This includes employing linearization techniques, converting the second-stage model into a single-level equivalent, transforming an integer variable into multiple binary variables, and utilizing other methods to equivalently reformulate the model into a mixed-integer linear programming problem (MILP). These transformations render the model amenable to solutions using the integer L-shaped method. A simplified example clarifies the solution procedures of the model and algorithm, establishing the theoretical foundation for their practical implementation. Subsequently, to empirically demonstrate the practicality of the proposed model and algorithm, a real-world case study is conducted, effectively validating their utility.

Transporting passengers to maritime units is one of the most critical factors in the oil industry. Optimizing costs and reducing workforce exposure to flight hours is paramount. The target scenario for this study is Petrobras’ operations in the Campos Basin, where more than 500 thousand passengers are transported annually. A model inspired by Murthy (Operations research (linear programming). bohem press, 2005) transportation problems is proposed, which aims to answer which helicopters from each base will have to fly how many flights per week to meet the demand of each of the maritime units. Current scenarios are analyzed with changes in the configuration of marine units, helicopter fleets, and airport bases. The results showed that the model represents very well the characteristics taken into account by the company, which presented financial gains with the optimization of the scenario today. Also, the scenarios with changes showed even more significant financial gains than the scenario currently adopted, indicating that the model helps support decisions to improve helicopter transport operations.

The economic development of cities is significantly influenced by multinational corporations (MNCs), whose presence and interconnections with their subsidiaries across various regions and continents shape their global operations and potential expansions. North America serves as a pivotal hub for numerous leading MNCs, and cities within this region play a vital role in establishing both local and global economic relationships. This research aims to investigate the tightly coupled interdependent networks of cities facilitated by MNCs and their subsidiaries, with a specific focus on North American cities and urban centers. This study examines empirical data from four distinct time periods (2010, 2013, 2016, and 2019) at various scales, aiming to empirically support the hypothesis that robust intra-regional ties within North America foster the economic self-reliance of cities and urban centers, concurrently stimulating sustainable economic growth throughout the studied period. The analysis corroborates this hypothesis, as evident from the research findings.