Search

The climate anomalies caused by La Niña events have caused some economic losses and human casualties in several regions around the world. In this paper, we first visualize and analyze the climate data of four representative countries to obtain the impact of La Niña on different countries. Then a BP neural network model is built to predict the probability of La Niña events in different regions. Finally, taking six different provinces in southern China as examples, an indicator system is established, a fuzzy comprehensive evaluation algorithm is used to summarize the impacts of different regions and indicators under a La Niña event, and practical response strategies are proposed.

In service systems with natural or anthropogenic barriers (e.g., rivers, railroads), customers who intend to visit facilities for service must first pass through certain network access points (e.g., bridges, railway crossings). Possible blockage or disruptions of these access points could change the customer-facility assignments or even affect reachability of various facilities, and thus introduce facility reliability and correlation issues. This paper incorporates network access points and their probabilistic failures into a joint optimization framework. A layer of network access points are added and connected to facilities to imply the real-world connections between facilities and access points. The access points are assumed to be subject to disruptions with site-dependent probabilities. We then develop a compact mixed-integer mathematical model to optimize the facility location and customer assignment decisions for the service systems design. Lagrangian relaxation based algorithms are designed to effectively solve the proposed model. Multiple case studies are constructed to test the model and the algorithm, and to demonstrate their performance and applicability.

Unconventional intersection designs have been used to increase the capacity of intersections that are over-saturated under conventional ones. However, existing unconventional designs typically require extra land space and their effectiveness often depends on drivers' familiarity with the uncommon operating rules. To overcome these challenges, we propose a new unconventional design, where movements that are mutually incompatible under the conventional design can be made compatible of each other by allocating exit lanes to them appropriately, thereby creating opportunities for capacity improvement. We develop a lane-based capacity optimization model that incorporates the allocation of exit lanes as decision variables. The model is formulated as a Binary Mixed Integer Linear Programming problem, which can be efficiently solved by standard branch-and-bound algorithms. Numerical experiments show that significant capacity improvement can be obtained under our design. Besides proposing a new unconventional design, we also contribute to the literature of lane-based signal optimization methods by providing a novel linear formulation for the latest, yet nonlinear, model described in Wong and Heydecker [Transportation Research Part B 45(4):667–681]. This improvement is methodologically beneficial as linear models are computationally more convenient than nonlinear ones. Copyright © 2017 John Wiley & Sons, Ltd.

Railroad companies rely on good call centers to reliably handle incoming crew/resource call demands so as to maintain efficient operations and customer services in their networks. This paper formulates a reliable caller districting problem which aims at partitioning an undirected network into a fixed number of districts. The demand of each district is assigned to crew caller desks (one primary desk, multiple backups) under possible desk disruption scenarios. We simultaneously take into account several operational criteria, such as district contiguity and compactness, workload balance, and caller desk service reliability. The resulted districting problem is modeled in the form of a challenging mixed-integer program, and we develop a customized heuristic algorithm (based on constructive heuristic and neighborhood search) to provide near-optimum solutions in a reasonable amount of time. Hypothetical and empirical numerical examples are presented to demonstrate the performance and effectiveness of our methodology for different network sizes and parameter settings. Managerial insights are also drawn.

In North America, thousands of locomotives operating in the railroad network require various types of mechanical work every day. This work may be unscheduled, periodic, or on demand and could be conducted at either a fixed facility or a movable facility. Each facility is characterized by the type of work it can provide. The long-term infrastructure planning of these facilities is vital to the efficiency of the railroad. In this paper, we develop a large-scale mixed-integer mathematical model for infrastructure planning. The model integrates and optimizes decisions about (1) Locations, capabilities, and capacities of fixed facilities, (2) Home locations and routing plans of movable facilities, and (3) Assignments of locomotive work demands to facilities. We propose a decomposition-based heuristic solution framework consisting of several underlying algorithms to solve this large-scale optimization model. Computational results on numerous scenario studies using field data from the railroad industry show that the proposed model and algorithms are capable of providing solutions that are significantly superior to the current practice. The successful application of our methodology to real-world railroad planning has clearly demonstrated the substantial cost-saving benefits of our models on infrastructure planning solutions.