Sibel Salman - Academia.edu (original) (raw)

Papers by Sibel Salman

Research paper thumbnail of VLDP Data sets

Based on the data obtained from a freight transportation company that operates two lines, one of ... more Based on the data obtained from a freight transportation company that operates two lines, one of which has one-way transportation time of one day and the other with 2 days, we generated a set of benchmark VLDP data instances of three different sizes: small-sized (including 15 or 18 orders), medium-sized (20, 25, or 30 orders) and large-sized (40, 50, or 60 orders). The planning horizon is considered as seven days. Order related parameters are generated based on an analysis of the real data. We randomly selected some pairs of orders as related orders preferred to be transported by the same shipment, where the ratio of the number of related pair of orders to the number of orders varies between 0 and 0.32 in all instances as in the real data. The penalty associated with each additional distinct shipment day and each additional distinct shipment for related orders are set to 0.20.20.2 and 0.10.10.1, respectively. The vehicle fleet is assumed to be comprised of vehicles owned by the freight logistics company and rented vehicles. The unit cost of using an owned or a rented vehicle is $50 or \80, respectively. There are two types of owned or rented vehicles regarding their sizes and weight capacities: one type has a width of 5 units, a length of 25 units, and a weight capacity of 7500 units, and the other has a width of 5 units, a length of 30 units , and a weight capacity of 9000 units. The available day of an owned vehicle during the current planning horizon based on its delivery schedule in the previous planning horizon is determined according a procedure described in the related article of authors. The instances generated based on the above setting are considered as original instances and classified under group G1. Five additional groups of large-size data instances (Groups G2 to G6) are generated with controlled variety on sizes or due dates of orders in order to investigate the effect of the variety of sizes and the variety of due dates on the results. The details of the data set can be found in the related art [...]

Research paper thumbnail of Role of Analytics for Operational Risk Management in the Era of Big Data

Research paper thumbnail of Data Analytics for Operational Risk Management

Research paper thumbnail of Online Optimization of First-responder Routes in Disaster Response Logistics

IBM Journal of Research and Development

After a disaster, first-responders should reach critical locations in the disaster-affected regio... more After a disaster, first-responders should reach critical locations in the disaster-affected region in shortest time. However, road network edges can be damaged or blocked by debris. Since response time is crucial, relief operations may start before knowing on which edges are blocked. A blocked edge is revealed online when it is visited at one of its end-nodes. Multiple first-responder teams, who can communicate the blockage information, gather initially at an origin node and are assigned to target destinations (nodes) in the disaster affected area. We consider multiple teams assigned to one destination. The objective is to find an online travel plan such that at least one of the teams finds a route from the origin to the destination in minimum time. This problem is known as the online multi-agent Canadian Traveler Problem. We develop an effective online heuristic policy and test it on real city road networks as well as randomly generated networks leading to instances with multiple blockages. We compare the performance of the online strategy with the offline optimum and obtain an average competitive ratio of 1.164 over 70100 instances with varying parameter values.

Research paper thumbnail of A data-driven optimization framework for routing mobile medical facilities

Annals of Operations Research

Research paper thumbnail of Minimizing latency in post-disaster road clearance operations

European Journal of Operational Research

Research paper thumbnail of Inequity-averse shelter location for disaster preparedness

IISE Transactions

We study the problem of selecting a set of shelter locations in preparation for natural disasters... more We study the problem of selecting a set of shelter locations in preparation for natural disasters. Shelters provide victims of a disaster both a safe place to stay and relief necessities such as food, water and medical support. Individuals from the affected population living in a set of population points go to, or are transported to the assigned open shelters. We aim to take both efficiency and inequity into account, thus we minimize a linear combination of: (i) the mean distance between opened shelter locations and the locations of the individuals assigned to them; and (ii) Gini's Mean Absolute Difference of these distances. We develop a stochastic programming model with a set of scenarios that consider uncertain demand and disruptions in the transportation network. A chance constraint is defined on the total cost of opening the shelters and their capacity expansion. In this stochastic context, a weighted mean of the so-called ex ante and ex post versions of the inequity-averse objective function under uncertainty is optimized. Since the model can be solved to optimality only for small instances, we develop a tailored Genetic Algorithm (GA) that utilizes a mixed-integer programming subproblem to solve this problem heuristically for larger instances. We compare the performance of the mathematical program and the GA via benchmark instances where the model can be solved to optimality or near optimality. It turns out that the GA yields small optimality gaps in much shorter time for these instances. We run the GA also on Istanbul data to drive insights to guide decision-makers for preparation.

Research paper thumbnail of On the randomized online strategies for the k-Canadian traveler problem

Journal of Combinatorial Optimization

Research paper thumbnail of On the online multi-agent O–D k-Canadian Traveler Problem

Journal of Combinatorial Optimization

Research paper thumbnail of Relief aid stocking decisions under bilateral agency cooperation

Socio-Economic Planning Sciences

Research paper thumbnail of Multiple facility location on a network with linear reliability order of edges

Journal of Combinatorial Optimization

Research paper thumbnail of Arc routing problems to restore connectivity of a road network

Transportation Research Part E: Logistics and Transportation Review

Research paper thumbnail of An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit operations

Research paper thumbnail of Facility Location on a Network with Unreliable Links

In this paper we study a simple vulnerability-based stochastic dependency model of link failures ... more In this paper we study a simple vulnerability-based stochastic dependency model of link failures in a network prone to disasters. Under this model, we study the problem of locating k facilities to maximize the expected demand serviced within a given distance, and show its equivalence to the well-studied maximum k-facility location problem. In the special case when there is no distance constraint, we give two solutions to the k-facility location problem using dynamic programming and a greedy algorithm.

Research paper thumbnail of Buy-at-Bulk Network Design: Approximating the Single-Sink Edge Installation Problem

We initiate the algorithmic study of an important but NP-hard problem that arises commonly in net... more We initiate the algorithmic study of an important but NP-hard problem that arises commonly in network design. The input consists of (1) An undirected graph with one sink node and multiple source nodes, a speci ed length for each edge, and a speci ed demand, dem v , for each source node v.

Research paper thumbnail of Approximating the Single-Sink Edge Installation Problem in Network Design

We initiate the algorithmic study of an important but NP-hard problem that arises commonly in net... more We initiate the algorithmic study of an important but NP-hard problem that arises commonly in network design. The input consists of (1) An undirected graph with one sink node and multiple source nodes, a speci ed length for each edge, and a speci ed demand, dem v , for each source node v.

Research paper thumbnail of JOCO-2000-Approximation Algorithms for the Multiple Knapsack Problem with Assignment Restrictions

Motivated by a real world application, we study the multiple knapsack problem with assignment res... more Motivated by a real world application, we study the multiple knapsack problem with assignment restrictions (MKAR). We are given a set of items, each with a positive real weight, and a set of knapsacks, each with a positive real capacity. In addition, for each item a set of knapsacks that can hold that item is specified. In a feasible assignment of items to knapsacks, each item is assigned to at most one knapsack, assignment restrictions are satisfied, and knapsack capacities are not exceeded. We consider the objectives of maximizing assigned weight and minimizing utilized capacity.

Research paper thumbnail of Investing in the Links of a Stochastic Network to Minimize Expected Shortest Path. Length

We consider a network whose links are subject to independent, random failures due to a disruptive... more We consider a network whose links are subject to independent, random failures due to a disruptive event. The survival probability of a link is increased, if it is strengthened by investment. A given budget is to be allocated among the links with the objective of optimizing the post-event performances of the network. Specifically, we seek to minimize the expected shortest path Length between a specified origin node and destination node in the network. This criterion is defined through the use of a fixed penalty cost for those network realizations in the expectation, that do not have a path connecting the origin node to the destination node. This problem type arises in the pre-disasters, by upgrading its weakest elements. We model the problem as a two-stage stochastic program in which the underlying probability distribution of the random variables is dependent on the first stage decision variables. Using a path-based approach we construct its equivalent deterministic program and deriv...

Research paper thumbnail of Analysis of Mobile Phone Call Data of Istanbul Residents

Geo-Intelligence and Visualization through Big Data Trends, 2015

Research paper thumbnail of Product Assortment Under Customer-Driven Demand Substitution in Retail Operations

International Conference on Information Systems, 2000

In this paper, we model the problem of product assortment and i nventory planning under customer-... more In this paper, we model the problem of product assortment and i nventory planning under customer-driven demand substitution in a multi-peri od setting. Our model also takes into account other realistic issues in a retail context suchas supplier selection, shelf space constraints, and poor quality procurement. First, the char acteristics of optimal assortment for different substitution costs is examined. Next,

Research paper thumbnail of VLDP Data sets

Based on the data obtained from a freight transportation company that operates two lines, one of ... more Based on the data obtained from a freight transportation company that operates two lines, one of which has one-way transportation time of one day and the other with 2 days, we generated a set of benchmark VLDP data instances of three different sizes: small-sized (including 15 or 18 orders), medium-sized (20, 25, or 30 orders) and large-sized (40, 50, or 60 orders). The planning horizon is considered as seven days. Order related parameters are generated based on an analysis of the real data. We randomly selected some pairs of orders as related orders preferred to be transported by the same shipment, where the ratio of the number of related pair of orders to the number of orders varies between 0 and 0.32 in all instances as in the real data. The penalty associated with each additional distinct shipment day and each additional distinct shipment for related orders are set to 0.20.20.2 and 0.10.10.1, respectively. The vehicle fleet is assumed to be comprised of vehicles owned by the freight logistics company and rented vehicles. The unit cost of using an owned or a rented vehicle is $50 or \80, respectively. There are two types of owned or rented vehicles regarding their sizes and weight capacities: one type has a width of 5 units, a length of 25 units, and a weight capacity of 7500 units, and the other has a width of 5 units, a length of 30 units , and a weight capacity of 9000 units. The available day of an owned vehicle during the current planning horizon based on its delivery schedule in the previous planning horizon is determined according a procedure described in the related article of authors. The instances generated based on the above setting are considered as original instances and classified under group G1. Five additional groups of large-size data instances (Groups G2 to G6) are generated with controlled variety on sizes or due dates of orders in order to investigate the effect of the variety of sizes and the variety of due dates on the results. The details of the data set can be found in the related art [...]

Research paper thumbnail of Role of Analytics for Operational Risk Management in the Era of Big Data

Research paper thumbnail of Data Analytics for Operational Risk Management

Research paper thumbnail of Online Optimization of First-responder Routes in Disaster Response Logistics

IBM Journal of Research and Development

After a disaster, first-responders should reach critical locations in the disaster-affected regio... more After a disaster, first-responders should reach critical locations in the disaster-affected region in shortest time. However, road network edges can be damaged or blocked by debris. Since response time is crucial, relief operations may start before knowing on which edges are blocked. A blocked edge is revealed online when it is visited at one of its end-nodes. Multiple first-responder teams, who can communicate the blockage information, gather initially at an origin node and are assigned to target destinations (nodes) in the disaster affected area. We consider multiple teams assigned to one destination. The objective is to find an online travel plan such that at least one of the teams finds a route from the origin to the destination in minimum time. This problem is known as the online multi-agent Canadian Traveler Problem. We develop an effective online heuristic policy and test it on real city road networks as well as randomly generated networks leading to instances with multiple blockages. We compare the performance of the online strategy with the offline optimum and obtain an average competitive ratio of 1.164 over 70100 instances with varying parameter values.

Research paper thumbnail of A data-driven optimization framework for routing mobile medical facilities

Annals of Operations Research

Research paper thumbnail of Minimizing latency in post-disaster road clearance operations

European Journal of Operational Research

Research paper thumbnail of Inequity-averse shelter location for disaster preparedness

IISE Transactions

We study the problem of selecting a set of shelter locations in preparation for natural disasters... more We study the problem of selecting a set of shelter locations in preparation for natural disasters. Shelters provide victims of a disaster both a safe place to stay and relief necessities such as food, water and medical support. Individuals from the affected population living in a set of population points go to, or are transported to the assigned open shelters. We aim to take both efficiency and inequity into account, thus we minimize a linear combination of: (i) the mean distance between opened shelter locations and the locations of the individuals assigned to them; and (ii) Gini's Mean Absolute Difference of these distances. We develop a stochastic programming model with a set of scenarios that consider uncertain demand and disruptions in the transportation network. A chance constraint is defined on the total cost of opening the shelters and their capacity expansion. In this stochastic context, a weighted mean of the so-called ex ante and ex post versions of the inequity-averse objective function under uncertainty is optimized. Since the model can be solved to optimality only for small instances, we develop a tailored Genetic Algorithm (GA) that utilizes a mixed-integer programming subproblem to solve this problem heuristically for larger instances. We compare the performance of the mathematical program and the GA via benchmark instances where the model can be solved to optimality or near optimality. It turns out that the GA yields small optimality gaps in much shorter time for these instances. We run the GA also on Istanbul data to drive insights to guide decision-makers for preparation.

Research paper thumbnail of On the randomized online strategies for the k-Canadian traveler problem

Journal of Combinatorial Optimization

Research paper thumbnail of On the online multi-agent O–D k-Canadian Traveler Problem

Journal of Combinatorial Optimization

Research paper thumbnail of Relief aid stocking decisions under bilateral agency cooperation

Socio-Economic Planning Sciences

Research paper thumbnail of Multiple facility location on a network with linear reliability order of edges

Journal of Combinatorial Optimization

Research paper thumbnail of Arc routing problems to restore connectivity of a road network

Transportation Research Part E: Logistics and Transportation Review

Research paper thumbnail of An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit operations

Research paper thumbnail of Facility Location on a Network with Unreliable Links

In this paper we study a simple vulnerability-based stochastic dependency model of link failures ... more In this paper we study a simple vulnerability-based stochastic dependency model of link failures in a network prone to disasters. Under this model, we study the problem of locating k facilities to maximize the expected demand serviced within a given distance, and show its equivalence to the well-studied maximum k-facility location problem. In the special case when there is no distance constraint, we give two solutions to the k-facility location problem using dynamic programming and a greedy algorithm.

Research paper thumbnail of Buy-at-Bulk Network Design: Approximating the Single-Sink Edge Installation Problem

We initiate the algorithmic study of an important but NP-hard problem that arises commonly in net... more We initiate the algorithmic study of an important but NP-hard problem that arises commonly in network design. The input consists of (1) An undirected graph with one sink node and multiple source nodes, a speci ed length for each edge, and a speci ed demand, dem v , for each source node v.

Research paper thumbnail of Approximating the Single-Sink Edge Installation Problem in Network Design

We initiate the algorithmic study of an important but NP-hard problem that arises commonly in net... more We initiate the algorithmic study of an important but NP-hard problem that arises commonly in network design. The input consists of (1) An undirected graph with one sink node and multiple source nodes, a speci ed length for each edge, and a speci ed demand, dem v , for each source node v.

Research paper thumbnail of JOCO-2000-Approximation Algorithms for the Multiple Knapsack Problem with Assignment Restrictions

Motivated by a real world application, we study the multiple knapsack problem with assignment res... more Motivated by a real world application, we study the multiple knapsack problem with assignment restrictions (MKAR). We are given a set of items, each with a positive real weight, and a set of knapsacks, each with a positive real capacity. In addition, for each item a set of knapsacks that can hold that item is specified. In a feasible assignment of items to knapsacks, each item is assigned to at most one knapsack, assignment restrictions are satisfied, and knapsack capacities are not exceeded. We consider the objectives of maximizing assigned weight and minimizing utilized capacity.

Research paper thumbnail of Investing in the Links of a Stochastic Network to Minimize Expected Shortest Path. Length

We consider a network whose links are subject to independent, random failures due to a disruptive... more We consider a network whose links are subject to independent, random failures due to a disruptive event. The survival probability of a link is increased, if it is strengthened by investment. A given budget is to be allocated among the links with the objective of optimizing the post-event performances of the network. Specifically, we seek to minimize the expected shortest path Length between a specified origin node and destination node in the network. This criterion is defined through the use of a fixed penalty cost for those network realizations in the expectation, that do not have a path connecting the origin node to the destination node. This problem type arises in the pre-disasters, by upgrading its weakest elements. We model the problem as a two-stage stochastic program in which the underlying probability distribution of the random variables is dependent on the first stage decision variables. Using a path-based approach we construct its equivalent deterministic program and deriv...

Research paper thumbnail of Analysis of Mobile Phone Call Data of Istanbul Residents

Geo-Intelligence and Visualization through Big Data Trends, 2015

Research paper thumbnail of Product Assortment Under Customer-Driven Demand Substitution in Retail Operations

International Conference on Information Systems, 2000

In this paper, we model the problem of product assortment and i nventory planning under customer-... more In this paper, we model the problem of product assortment and i nventory planning under customer-driven demand substitution in a multi-peri od setting. Our model also takes into account other realistic issues in a retail context suchas supplier selection, shelf space constraints, and poor quality procurement. First, the char acteristics of optimal assortment for different substitution costs is examined. Next,