Browsing by Subject "Second order cone programming"

Now showing 1 - 11 of 11

Open Access
Airline scheduling to minimize operational costs and variability
(2021-08) Şimşek, Deniz
Airlines tend to design their ﬂights schedules with the primary concern of the minimization of operational costs. However, the recently emerging idea of resilient scheduling deﬁned as staying operational in case of unexpected disruptions and adaptability should be of great importance for airlines as well due to the high opportunity costs caused by the ﬂight cancellations and passenger inconvenience caused by delays in the schedule. In this study, we integrate resilient airline schedule design, aircraft routing and ﬂeet assignment problems with uncertain non-cruise times and controllable cruise times. We follow a data-driven method to estimate ﬂight delay probabilities to calculate the airport congestion coeﬃcients required for the probability distributions of non-cruise time random variables. We formulate the problem as a bi-criteria nonlinear mixed integer mathematical model with chance constraints. The nonlinearity caused by the fuel consumption and CO2 emission function associated with the controllable cruise times in our ﬁrst objective is handled by second order conic inequalities. We minimize the total absolute deviation of the aircraft path variabilities from the average in our second objective to generate balanced schedules in terms of resilience. We follow an ε-constraint approach to scalarize and solve our problem via commercial solvers and we also devise a discretized approximation and search algorithm to solve large instances. We compare the recovery performances of our proposed schedules to the minimum cost schedules by a scenario-based posterior analysis. As a key contribution, we show that in the schedule generation phase, designing resilient schedules by allowing them to deviate from the minimum cost within the trade-oﬀ between the operational costs and the variability, the potential recovery costs in case of unexpected disruptions can be reduced signiﬁcantly.
Open Access
Compromising system and user interests in shelter location and evacuation planning
(Elsevier Ltd, 2015) Bayram V.; Tansel, B.T.; Yaman H.
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 flow 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 importance of the number and locations of shelter sites and the trade-off between efficiency and fairness. © 2014 Elsevier Ltd.
Open Access
Green hub location problem
(Elsevier, 2019) Dükkancı, Okan; Peker, Meltem; Kara, Bahar Y.
This paper introduces the green hub location problem that finds the best locations for hubs, assignments of demand nodes to these hubs and speed of trucks/flights so as to route the demand between any origin-destination pairs. The aim of the service provider is to minimize the total amount of emissions that depends on vehicle speed and payload while routing the deliveries within a predetermined service time limit. In this study, we first propose a nonlinear model for this problem, which is then reformulated as a second order cone programming formulation. We strengthen the new model by using perspective reformulation approach. An extensive computational study on the CAB and TR datasets demonstrates the benefits of incorporating green transportation service activities to the classic hub location problems. We also provide insights for the carrier companies by analyzing the solutions with different discount factors, service time limits and number of hubs.
Open Access
An integrated approach for airline scheduling, aircraft fleeting and routing with cruise speed control
(Elsevier, 2016) Gürkan, H.; Gürel, S.; Aktürk, M. S.
To place an emphasis on profound relations among airline schedule planning problems and to mitigate the effect of unexpected delays, we integrate schedule design, fleet assignment and aircraft routing problems within a daily planning horizon while passengers' connection service levels are ensured via chance constraints. We propose a nonlinear mixed integer programming model due to the nonlinear fuel consumption and CO2 emission cost terms in the objective function, which is handled by second order conic reformulation. The key contribution of this study is to take into account the cruise time control for the first time in an integrated model of these three stages of airline operations. Changing cruise times of flights in an integrated model enables to construct a schedule to increase utilization of fuel efficient aircraft and even to decrease total number of aircraft needed while satisfying the same service level and maintenance requirements for aircraft fleeting and routing. There is a critical tradeoff between the number of aircraft needed to fulfill the required flights and overall operational expenses. We also propose two heuristic methods to solve larger size problems. Finally, computational results using real data obtained from a major U.S. carrier are presented to demonstrate potential profitability in applying the proposed solution methods.
Open Access
An integrated approach for robust airline scheduling, aircraft fleeting and routing with cruise speed control
(2014) Gürkan, Hüseyin
To place emphasis on profound relations among airline schedule planning problems and to mitigate the effect of unexpected delays, we integrate robust schedule design, fleet assignment and aircraft routing problems within a daily planning horizon while passengers’ connection service levels are ensured via chance constraints and maintenance requirements are satisfied. We propose a nonlinear mixed integer programming model. In the objective function, the cost functions due to fuel consumption and CO2 emission cost involve nonlinearity. This nonlinearity is handled by second order conic reformulation. The key contribution of this study is to take into account the cruise time control for the first time in an integrated model of these three stages of airline operations. Changing cruise times of flights in an integrated model enables to construct a schedule to increase utilization of efficient aircraft and even to decrease the total number of aircraft needed while satisfying service level and maintenance requirements for aircraft fleeting and routing. Besides, for the robust schedule design problem, it is possible to improve the solution since a routing decision could eliminate the necessity of inserting idle time or compressing cruise time. In addition, we propose two heuristic methods to solve large size problems faster than the integrated model. Eventually, computational results using real data obtained from a major U.S. carrier are presented to demonstrate potential profitability in applying the proposed solution methods.
Open Access
Minimizing energy and cost in range-limited drone deliveries with speed optimization
(Elsevier, 2021-02-06) Dukkanci, O.; Yetiş Kara, Bahar; Bektaş, T.
This paper introduces the Energy Minimizing and Range Constrained Drone Delivery Problem (ERDDP) in which drones are used to make deliveries to a number of customers and the drones themselves are transported by traditional vehicles that act as launch points. The ERDDP consists of (i) selecting the launch points from a potential set of sites from where drones will take off to serve a number of customers, (ii) assignments of customers to the launch points, and (iii) the speed at which drones are to travel between the customers and the launch points. The paper presents a nonlinear model for the ERDDP, which minimizes the total operational cost including an explicit calculation of the energy consumption of the drone as a function of the drone speed. The deliveries are limited by both a service time bound and the range of the drone. The model is reformulated using second order cone programming, and subsequently strengthened by the use of perspective cuts, that allows the use of off-the-shelf optimization software to solve the problem. Computational results are presented on a realistic data set that quantifies the effect of various parameters on location, assignment and speed decisions.
Open Access
Nonlinear mixed integer programming models and algorithms for fair and efficient large scale evacuation planning
(2015-07) Bayram, Vedat
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.
Open Access
Optimal oblivious routing under linear and ellipsoidal uncertainty
(Springer, 2008) Belotti, P.; Pınar, M. Ç.
In telecommunication networks, a common measure is the maximum congestion (i.e., utilization) on edge capacity. As traffic demands are often known with a degree of uncertainty, network management techniques must take into account traffic variability. The oblivious performance of a routing is a measure of how congested the network may get, in the worst case, for one of a set of possible traffic demands. We present two models to compute, in polynomial time, the optimal oblivious routing: a linear model to deal with demands bounded by box constraints, and a second-order conic program to deal with ellipsoidal uncertainty, i.e., when a mean-variance description of the traffic demand is given. A comparison between the optimal oblivious routing and the well-known OSPF routing technique on a set of real-world networks shows that, for different levels of uncertainty, optimal oblivious routing has a substantially better performance than OSPF routing.
Open Access
Resilient airline scheduling to minimize delay risks
(Elsevier Ltd, 2022-06-06) Şi̇mşek, D.; Aktürk, M. Seli̇m
Airlines tend to design their flights schedules with the primary concern of the minimization of operational costs. However, the recently emerging idea of resilient scheduling defined as staying operational in case of unexpected disruptions and adaptability should be of great importance for airlines as well due to the high opportunity costs caused by the flight cancellations and passenger inconvenience caused by delays in the schedule. In this study, we integrate resilient airline schedule design, aircraft routing and fleet assignment problems with uncertain non-cruise times and controllable cruise times. We follow a data-driven method to estimate flight delay probabilities to calculate the airport congestion coefficients required for the probability distributions of non-cruise time random variables. We formulate the problem as a bi-criteria nonlinear mixed integer mathematical model with chance constraints. The nonlinearity caused by the fuel consumption and CO2 emission function associated with the controllable cruise times in our first objective is handled by second order conic inequalities. We minimize the total absolute deviation of the aircraft path variability’s from the average in our second objective to generate balanced schedules in terms of resilience. We compare the recovery performances of our proposed schedules to the minimum cost schedules by a scenario-based posterior analysis.
Open Access
Stochastic lot sizing problem with controllable processing times
(Elsevier, 2015) Koca, E.; Yaman, H.; Aktürk, M. S.
In this study, we consider the stochastic capacitated lot sizing problem with controllable processing times where processing times can be reduced in return for extra compression cost. We assume that the compression cost function is a convex function as it may reflect increasing marginal costs of larger reductions and may be more appropriate when the resource life, energy consumption or carbon emission are taken into consideration. We consider this problem under static uncertainty strategy and α service level constraints. We first introduce a nonlinear mixed integer programming formulation of the problem, and use the recent advances in second order cone programming to strengthen it and then solve by a commercial solver. Our computational experiments show that taking the processing times as constant may lead to more costly production plans, and the value of controllable processing times becomes more evident for a stochastic environment with a limited capacity. Moreover, we observe that controllable processing times increase the solution flexibility and provide a better solution in most of the problem instances, although the largest improvements are obtained when setup costs are high and the system has medium sized capacities.
Open Access
A stochastic programming approach for Shelter location and evacuation planning
(EDP Sciences, 2018) Bayram, V.; Yaman, Hande
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