Locomotive Assignment Under Consist Busting and Maintenance Constraints

2014 ◽  
Author(s):  
Brigitte Jaumard ◽  
Huaining Tian ◽  
Peter Finnie
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
Optimization Techniques ◽  
Large Scale Optimization ◽  
Railway Network ◽  
Time Period ◽  
Scale Optimization ◽  
Solution Accuracy ◽  
Locomotive Assignment ◽  
The Impact

We propose a new optimization model with column generation (CG) decomposition for the locomotive assignment problem (LAP). Although several algorithms have been developed for LAP, including exact mathematics models, approximate dynamic programming and heuristics, previously published optimization algorithms all suffer from scalability or solution accuracy issues. In addition, each of the optimization models lacks part of the constraints that are necessary in real-world train/locomotive assignments, e.g., maintenance shop constraints or consist-busting avoidance. We propose a train string based LAP model, which includes those constraints and which can efficiently be solved using large scale optimization techniques, namely column generation techniques. Numerical results are conducted on the railway network infrastructure of Canada Pacific Railway, with up to 1,394 trains and 4 types of locomotives over a two-week time period. We investigate the impact of the size of the locomotive fleet on the consist busting.

Applying Large-Scale Optimization to Evaluate Pavement Maintenance Alternatives for Low-Volume Roads using Genetic Algorithms

2018 ◽  
Vol 2672 (52) ◽  
pp. 205-215 ◽  
Author(s):  
Marwan Hafez ◽  
Khaled Ksaibati ◽  
Rebecca A. Atadero
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Pavement Performance ◽  
Large Scale Optimization ◽  
Maintenance Policy ◽  
Low Volume Roads ◽  
Low Volume ◽  
Scale Optimization

Over the last decade, significant progress has been made to customize the maintenance policies of low-volume roads (LVRs) to local needs and available resources. Low-cost treatments and surface repairs are extensively employed to reduce annual maintenance costs. Colorado Department of Transportation (CDOT) uses chip seals and thin overlays as the available treatment options applied to LVRs. However, the effectiveness of these treatments differs depending on the existing condition of pavements. Some surface treatments and light rehabilitations provide only short-term effectiveness. Multi-year optimization techniques can support decision makers with a set of optimal maintenance activities to achieve specific pavement performance targets. This study applies large-scale optimization to compare the current CDOT maintenance policy with an alternative strategy recommended for low-volume paved roads in Colorado. Genetic algorithms were applied in the optimization models because they are capable of resolving the computational complexity of optimization problems in a timely fashion. The optimized maintenance alternatives were comprehensively investigated for a LVR network in Colorado over a specific planning horizon. The specific optimization constraints and limitations prevailing in LVRs are addressed and introduced in the problem formulation of the optimization process. The results of both performance and cost analysis emphasize the effectiveness of the proposed maintenance strategy compared with the existing one. The alternative policy provides much more benefit-cost saving while preserving the overall pavement performance of the network. This approach is expected to be efficient to quantify the mid- and long-term financial impact of different treatment policies applied to LVRs within modest resources.

scholarly journals Large-scale optimization with the primal-dual column generation method

2015 ◽  
Vol 8 (1) ◽  
pp. 47-82 ◽  
Author(s):  
Jacek Gondzio ◽  
Pablo González-Brevis ◽  
Pedro Munari
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Primal Dual ◽  
Scale Optimization ◽  
Dual Column

scholarly journals Item Aggregation and Column Generation for Online-Retail Inventory Placement

2020 ◽  
Author(s):  
Annie I. Chen ◽  
Stephen C. Graves
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
A Priori ◽  
Mixed Integer ◽  
Large Scale Optimization ◽  
Fixed Costs ◽  
Online Retail ◽  
Numerical Example ◽  
A Priori Bound ◽  
Scale Optimization

Problem definition: This paper studies an online retailer’s problem of choosing fulfillment centers in which to place items. We formulate the problem as a mixed-integer program that models thousands or millions of items to be placed in dozens of fulfillment centers and shipped to dozens of customer regions. The objective is to minimize the sum of shipping and fixed costs over one planning period. Academic/practical relevance: A good placement plan can significantly reduce the operational cost, which is crucial for online-retail businesses because they often have a low profit margin. The placement problem can be difficult to solve with existing techniques or off-the-shelf software because of the large number of items and the fulfillment center fixed costs and capacity constraints. Methodology: We propose a large-scale optimization framework that aggregates items into clusters, solves the cluster-level problem with column generation, and disaggregates the solution into item-level placement plans. We develop an a priori bound on the optimality gap, and we also apply the framework to a numerical example that consists of 1,000,000 items. Results: The a priori bound provides insights on how to select the appropriate aggregation criteria. For the numerical example, our framework produces a near-optimal solution in a few hours, significantly outperforming a sequential placement heuristic that approximates the status quo. Managerial implications: Our study provides a computationally efficient approach for solving online-retail inventory placement as well as similar large-scale optimization problems in practice.

scholarly journals Large-scale optimization of households with photovoltaic-battery system and demand response

2020 ◽  
Vol 53 (2) ◽  
pp. 12572-12577
Author(s):  
Fernando Lezama ◽  
Ricardo Faia ◽  
Omid Abrishambaf ◽  
Pedro Faria ◽  
Zita Vale
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Battery System ◽  
Scale Optimization
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