An exact second order cone programming approach for traffic assignment problems

Demographic changes, urbanization and increasing vehicle ownership at unprecedented rates put a lot of strain on cities particularly on urban mobility and transportation and overwhelm transportation network infrastructures and current transportation systems, which are not built to cope with such a fast increasing demand. Traffic congestion is considered as the most difficult challenge to tackle for sustainable urban mobility and is aggravated by the increased freight activity due to e-commerce and on-demand delivery and the explosive growth in transportation network companies and ride-hailing services. There is a need to implement a combination of policies to ensure that increased urban traffic congestion does not lower the quality of life and threaten global climate and human health and to prevent further economic losses. This study aims to contribute to the United Nations (UN) climate action and sustainable development goals in tackling recurring traffic congestion problem in urban areas to achieve a sustainable urban mobility in that it offers a solution methodology for traffic assignment problem. We introduce an exact generalized solution methodology based on reformulation of existing traffic assignment problems as a second order cone programming (SOCP) problem and propose column generation (CG) and cutting plane (CP) algorithms to solve the problem effectively for large scale road network instances. We conduct numerical experiments to test the performance of the proposed algorithms on realistic road networks

Line balancing optimization under robot location and worker-station assignment considerations: A case study of a dishwasher factory

The focus of this article is on the line balancing work planned on the assembly line of a Dishwasher Factory. The main motivation is to make the assembly line more stable by grouping similar jobs and to increase the number of dishwashers produced per shift. In addition to these basic objectives, this study aims to rearrange the number of operators, optimize work-stock area and balance the man power at the stations. We propose a novel integer programming model that takes into account the location selection of stations, elevators and robots and the decisions of assigning jobs and workers to stations and use a commercial solver to solve the problem exactly. In the light of the outputs obtained from the solution of the problem, the current system and the improved system results were compared. First, the increase of dishwasher production capacity under current operational guidelines was evaluated and then the effect of grouping jobs on cycle time was evaluated. Based on the results of the sensitivity analysis, different results were proposed to optimize the current tempo, cycle time and number of workers. The results indicate that the number of workers can be reduced by 36%, while the number of dishwashers produced per shift can be increased by up to 52%, when all other inputs of the problem are fixed. Compared to the current practice, in the solution proposed to the manufacturing firm, the number of stations opened with similar jobs grouped was reduced by 68%, the number of fields that could be used by the stations was kept the same, the number of workers was reduced by 10% and the cycle time was improved by 4.34 seconds and the number of machines produced per shift increased by 43%

Data driven storage location assignment problem considering order picking frequencies: A heuristic approach

Warehouses are crucial in supply chain management. They are used to distribute and store products. In this study, we optimize storage location assignment decisions in a warehouse managed by a manufacturing firm. A mathematical model is introduced to solve the nonlinear mixed integer optimization problem (NLMIP), i.e., the Storage Location Assignment Problem (SLAP) by using historical data from warehouse management system (WMS). Clustering and ABC analysis is conducted based on the number of times two items are picked together and the picking frequency of items, respectively and embed the results into our optimization model. Also, a greedy heuristic is developed to solve SLAP of the firm. According to obtained output, the distances between filled slots and the I/O point of the current system and our proposed solution are compared in order to see the improvement in the system, and an improvement of up to 49.99% is observed

EFFECT OF EIGHT WEEKS EXERCISE ON BODY COMPOSITION AND SOME BLOOD VALUES IN WOMEN

Purpose: In this study, it was aimed to investigate the effect of eight weeks exercise on body composition and some blood values in women. Methods: A total of 16 volunteer women with an age mean of 30,81 ± 9,44 years and an age mean of 159,44 ± 6,61 cm were participated in the study. Blood samples of the participants were taken at the health facility while they were hungry before starting the exercise program. After applying the eight-week and 3 days a week exercise program, blood samples of subjects were taken again. The results which obtained from the study were analyzed with the SPSS 23.0 package program. In the analysis of the data, independent samples t test was applied to determine the difference between the groups. Results: As a result of the analyzes, while no significant difference was found between glucose and urea, creatine, total crystallinity, pre-test and post-test values of direct crystallization (p>0,05), body weight, BMI, chest circumference, waist circumference, waist circumference / height ratio, baseline area, and uric acid were significantly different between the pretest and posttest measurements (p<0,05). Conclusion: As a result of the study, it was observed that the eight-week exercise had differences in body weight, BMI, chest circumference, waist circumference, waist circumference, and baseline region and uric acid levels. It can be said that the exercise played an important role in the formation of this difference

Hub Network Design Problem with Capacity, Congestion and Stochastic Demand Considerations

Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach’s performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system

An exact second order cone programming approach for traffic assignment problems

Demographic changes, urbanization and increasing vehicle ownership at unprecedented rates put a lot of strain on cities particularly on urban mobility and transportation and overwhelm transportation network infrastructures and current transportation systems, which are not built to cope with such a fast increasing demand. Traffic congestion is considered as the most difficult challenge to tackle for sustainable urban mobility and is aggravated by the increased freight activity due to e-commerce and on-demand delivery and the explosive growth in transportation network companies and ride-hailing services. There is a need to implement a combination of policies to ensure that increased urban traffic congestion does not lower the quality of life and threaten global climate and human health and to prevent further economic losses. This study aims to contribute to the United Nations (UN) climate action and sustainable development goals in tackling recurring traffic congestion problem in urban areas to achieve a sustainable urban mobility in that it offers a solution methodology for traffic assignment problem. We introduce an exact generalized solution methodology based on reformulation of existing traffic assignment problems as a second order cone programming (SOCP) problem and propose column generation (CG) and cutting plane (CP) algorithms to solve the problem effectively for large scale road network instances. We conduct numerical experiments to test the performance of the proposed algorithms on realistic road networks

Optimizing the capacity and operation of U.S. Army ammunition production facilities

As the executive agent for ammunition, the Army manages the arsenals and plants that produce conventional ammunition for the Department of Defense. This industrial base must be able to manufacture a wide range of ammunition and ordnance items. In peacetime, the Army tests new rounds, makes training rounds, and manufactures rounds or components for war reserves, stockpile maintenance and upgrades. The Army must also manage and maintain capacity to replenish ammunition consumed by major theater wars without expanding the industrial base. The combined organic and inorganic industrial base can meet current requirements, but parts are becoming obsolete, and are expensive to operate. To improve efficiency and reduce per-unit costs while maintaining strategic control of this key defense capability, the Army is seeking to reconfigure facilities, and stabilize production rates. The Army realizes that the industrial base structure has to change. This thesis provides a prototypic decision support model that captures the essence of their problem by optimizing transition actions while satisfying complicated long-term constraints on resources, management, and capacity. The model suggests yearly decisions for a planning horizon of a decade or more, and is demonstrated with 16 organic installations, structures located therein, and process centers housed in those.http://archive.org/details/optimizingcapaci109455966First Lieutenant, Turkish ArmyApproved for public release; distribution is unlimited

Optimization models for large scale network evacuation planning and management: A literature review

This study presents a comprehensive review of network-based large scale emergency evacuation planning and management literature. Evacuation planning and management approaches are mostly based on traffic assignment approaches. For that reason, for a complete grasp of the ideas in evacuation planning and management, the relevant literature in urban transportation is covered including traffic assignment approaches, travel time modeling to represent congestion and traffic flow propagation approaches. Correct estimation of evacuation response rates and demand distributions by human behavior studies covered in this review contribute to an efficient evacuation planning and management at a large extent. Since it is not cost effective to design the evacuation network from scratch for rare disasters, the existing road network must be efficiently used for avoiding congestion to enable the evacuation of the disaster area in a timely manner. We present studies that propose effective supply and demand management strategies that aim to achieve this. We focus on macroscopic approaches in static/dynamic, deterministic/stochastic/robust evacuation modeling that consider different evacuee behavior assumptions, traffic assignment methodologies and supply and demand management strategies. We review the optimization-based solution methodologies so as to identify research gaps and limitations and suggest future research directions

Adil ve etkin büyük ölçekli tahliye planlaması için doğrusal olmayan karışık tamsayılı modeller ve algoritmalar

Cataloged from PDF version of thesis.Includes bibliographical references (leaves 132-154).Thesis (Ph. D.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2015.Shelters are safe facilities that protect a population from possible damaging effects of a disaster. Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. From the evacuation management authority's point of view, the desirable goal is to minimize the total evacuation time by computing a system optimum (SO). However, evacuees may not be willing to take long routes enforced on them by a SO solution; but they may consent to taking routes with lengths not longer than the shortest path to the nearest shelter site by more than a tolerable factor. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the ow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the trade-of between efficiency and fairness. Disasters are uncertain events. Related studies and real-life practices show that a significant uncertainty regarding the evacuation demand and the impact of the disaster on the infrastructure exists. The second model we propose is a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to shelters and paths to minimize the expected total evacuation time, under uncertainty. The model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for an impending earthquake in Istanbul, Turkey. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values. We also propose an exact algorithm based on Benders decomposition to solve the stochastic problem. To the best of our knowledge, ours is the first algorithm that uses duality results for second order cone programming in a Benders decomposition setting. We solve practical size problems with up to 1000 scenarios in moderate CPU times. We investigate methods such as employing a multi-cut strategy, deriving pareto-optimal cuts, using a reduced primal subproblem and preemptive priority multiobjective program to enhance the proposed algorithm. Computational results confirm the efficiency of our algorithm. This research is supported by TUBITAK, The Scientific and Technological Research Council of Turkey with project number 213M434.by Vedat Bayram.Ph.D

A STOCHASTIC PROGRAMMING APPROACH FOR SHELTER LOCATION AND EVACUATION PLANNING

© 2018 EDP Sciences, ROADEF, SMAI. Shelter location and traffic allocation decisions are critical for an efficient evacuation plan. In this study, we propose a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to nearest shelters and to shortest paths within a tolerance degree to minimize the expected total evacuation time. Our model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for a potential earthquake in Istanbul. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values.status: publishe