Researcher: Salman, Fatma Sibel
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Salman, Fatma Sibel
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Publication Metadata only An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit operations(Wiley, 2017) Bozkaya, Burçin; Department of Industrial Engineering; N/A; Salman, Fatma Sibel; Telciler, Kaan; Faculty Member; Master Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 178838; N/AWe consider the route optimization problem of transporting valuables in cash-in-transit (CIT) operations. The problem arises as a rich variant of the capacitated vehicle routing problem (CVRP) with time windows and pickup and deliveries. Due to the high-risk nature of this operation (e.g., robberies) we consider a bi-objective function where we attempt to minimize the total transportation cost and the security risk of transporting valuables along the designed routes. For risk minimization, we propose a composite risk measure that is a weighted sum of two risk components: (i) following the same or very similar routes, and (ii) visiting neighborhoods with low socioeconomic status along the routes. We also consider vehicle capacities in terms of monetary value carried as per insurance regulations. We develop an adaptive randomized bi-objective path selection algorithm that uses the composite risk measure in choosing alternative paths between origin-destination pairs over a sequence of days. We solve the rich CVRP approximately for each day with updated costs. We test our solution approach on a data set from a CIT delivery service provider and provide insights on how the routes diversify daily. Our approach generates a spectrum of solutions with costrisk trade-off to support decision making.Publication Metadata only Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity(Elsevier Science Bv, 2017) Department of Industrial Engineering; Department of Industrial Engineering; Akbari, Vahid; Salman, Fatma Sibel; Teaching Faculty; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; N/A; 178838After a natural disaster roads can be damaged or blocked by debris, while bridges and viaducts may collapse. This commonly observed hazard causes some road sections to be closed and may even disconnect the road network. In the immediate disaster response phase work teams are dispatched to open a subset of roads to reconnect the network. Closed roads are traversable only after they are unblocked/cleared by one of the teams. The main objective of this research is to provide an efficient solution method to generate a synchronized work schedule for the road clearing teams. The solution should specify the synchronized routes of each clearing team so that: 1) connectivity of the network is regained, and 2) none of the closed roads are traversed unless their unblocking/clearing procedure is finished. In this study we develop an exact Mixed Integer Programming (MIP) formulation to solve this problem. Furthermore, we propose a matheuristic that is based on an MIP-relaxation and a local search algorithm. We prove that the optimality gap of the relaxation solution is bounded by K times the lower bound obtained from the relaxed model, where K is the number of teams. We show computationally that the matheuristic obtains optimal or near-optimal solutions. (C) 2016 Elsevier B.V. All rights reserved.Publication Metadata only Managing home health-care services with dynamic arrivals during a public health emergency(Institute of Electrical and Electronics Engineers (IEEE), 2022) Araz, Ozgur M.; N/A; Department of Industrial Engineering; N/A; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert; PhD Student; Faculty Member; Master Student; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 178838; N/AWe consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches.Publication Metadata only Locating disaster response facilities in İstanbul(Taylor & Francis, 2011) Görmez, N.; Köksalan, M.; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We study the problem of locating disaster response and relief facilities in the city of Istanbul, where a massively destructive earthquake is expected to occur in the near future. The Metropolitan Municipality of Istanbul decided to establish facilities to preposition relief aid and execute post-disaster response operations. We propose a two-tier distribution system that utilizes existing public facilities locally in addition to the new facilities that will act as regional supply points. We develop mathematical models to decide on the locations of the new facilities with the objectives of minimizing the average-weighted distance between casualty locations and closest facilities, and opening a small number of facilities, subject to distance limits and backup requirements under regional vulnerability considerations. We analyze the trade-offs between these two objectives under various disaster scenarios and investigate the solutions for several modelling extensions. The results demonstrate that a small number of facilities will be sufficient and their locations are robust to various parameter and modelling changes.Publication Metadata only Multi-item dynamic lot-sizing with delayed transportation policy(Elsevier, 2011) N/A; Department of Industrial Engineering; Sancak, Emre; Salman, Fatma Sibel; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 178838We optimize ordering and inbound shipment decisions for a manufacturer that sources multiple items from a single supplier. the objective is to satisfy the requirements in the production plan with minimum transportation and inventory holding costs over a multi-period planning horizon. Transportation costs are charged to the manufacturer on a per truck shipment basis. We investigate the option of delaying a less-than-full truckload shipment to the next period, by utilizing the safety stocks as needed. We analyze the impact of delaying shipments on both cost and service levels in stochastic environments through experiments with data from a bus manufacturer. the results indicate that the proposed policy reduces both holding and transportation costs without creating much stock-out risk.Publication Metadata only An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem(Elsevier, 2014) Department of Business Administration; Department of Industrial Engineering; Department of Industrial Engineering; N/A; Aksen, Deniz; Kaya, Onur; Salman, Fatma Sibel; Tüncel, Özge; Faculty Member; Faculty Member; Faculty Member; Master Student; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Sciences; College of Engineering; Graduate School of Sciences and Engineering; 40308; 28405; 178838; N/AWe study a selective and periodic inventory routing problem (SPIRP) and develop an Adaptive Large Neighborhood Search (ALNS) algorithm for its solution. The problem concerns a biodiesel production facility collecting used vegetable oil from sources, such as restaurants, catering companies and hotels that produce waste vegetable oil in considerable amounts. The facility reuses the collected waste oil as raw material to produce biodiesel. It has to meet certain raw material requirements either from daily collection, or from its inventory, or by purchasing virgin oil. SPIRP involves decisions about which of the present source nodes to include in the collection program, and which periodic (weekly) routing schedule to repeat over an infinite planning horizon. The objective is to minimize the total collection, inventory and purchasing costs while meeting the raw material requirements and operational constraints. A single-commodity flow-based mixed integer linear programming (MILP) model was proposed for this problem in an earlier study. The model was solved with 25 source nodes on a 7-day cyclic planning horizon. In order to tackle larger instances, we develop an ALNS algorithm that is based on a rich neighborhood structure with 11 distinct moves tailored to this problem. We demonstrate the performance of the ALNS, and compare it with the MILP model on test instances containing up to 100 source nodes.Publication Metadata only Manufacturing parts sourcing with delayed transportation policy(Ieee, 2007) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Erkan, Tuğçe; Sancak, Emre; Yıldırım, Emre Alper; Salman, Fatma Sibel; Undergraduate Student; Master Student; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; N/A; College of Engineering; College of Engineering; N/A; N/A; 28415; 178838We propose a joint inventory and transportation policy for a manufacturer that needs to source multiple parts from a single supplier over a multiperiod planning horizon in order to facilitate its production plan. Instead of shipping trucks from the supplier to the manufacturer immediately whenever an order is given in a period, we allow delaying transportation to the next period in order to decrease the number of truck shipments with low truck loads. At the same time we maintain a minimum safety stock of each part at the manufacturer so that the production plan is not disrupted. We introduce a mixed integer programming model that represents the interdependency between ordering and transportation decisions and minimizes the sum of both transportation and inventory holding costs incurred to the manufacturer under the proposed policy. This model is utilized to improve the parts sourcing operations of a bus manufacturer in Istanbul with estimated sizeable savings on total costs.Publication Metadata only Prioritized single nurse routing and scheduling for home healthcare services(Elsevier, 2021) Bozkaya, Burçin; N/A; Department of Industrial Engineering; Çınar, Ahmet; Salman, Fatma Sibel; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 178838We study a real-life problem in which a nurse is required to check upon patients she is responsible for either by home visits or phone calls. Due to the large number of patients and their varying conditions, she has to select carefully which patients to visit at home for the upcoming days. We propose assigning priorities to patients according to factors such as the last visit time and the severity of their condition so that the priorities of unvisited patients increase exponentially by day. The solution to this problem should simultaneously specify which patients to visit on each day of the planning horizon, as well as the sequence of the visits to the selected patients on each day that obeys patients' time window requests. The objective is to maximize the total priority of the visited patients primarily and to minimize the total traveling time secondarily. After having observed the computational limits of an exact formulation, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm and a matheuristic to generate near optimal solutions for realistic-sized instances. We measure the quality of both algorithms by computing the optimality gaps using upper bounds generated by Lagrangean relaxation. Tests on real-life data show that both algorithms yield high quality solutions, but the matheuristic outperforms ALNS in large instances. On the other hand, the ALNS algorithm provides very short running times, while the running times of the matheuristic increase exponentially with problem size. (C) 2019 Elsevier B.V. All rights reserved.Publication Metadata only Order acceptance and scheduling decisions in make-to-order systems(Elsevier Science Bv, 2010) Yalçın, Zehra Bilgintuerk; Department of Industrial Engineering; Department of Industrial Engineering; Oğuz, Ceyda; Salman, Fatma Sibel; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 6033; 178838We examine simultaneous order acceptance and scheduling decisions where the orders are defined by their release dates, due dates, deadlines, processing times, sequence dependent setup times and revenues in a single machine environment. The objective is to maximize total revenue, where the revenue from an order is a function of its tardiness and deadline. We give an MILP formulation which can be solved to optimality up to 15 orders. We develop three heuristic algorithms to solve large sized problems. Computational tests indicate that the proposed algorithms are both computationally efficient and effective even for instances up to 300 orders.Publication Metadata only Solving the capacitated local access network design problem(The Institute for Operations Research and the Management Sciences (INFORMS), 2008) Ravi, R.; Hooker, John N.; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We propose an exact solution method for a routing and capacity installation problem in networks. Given an input graph, the problem is to route traffic from a set of source nodes to a sink node and to install transmission facilities on the edges of the graph to accommodate the flow at minimum cost. We give a branch-and-bound algorithm that solves relaxations obtained by approximating the noncontinuous cost function by its lower convex envelope. The approximations are refined by branching on the flow ranges on selected edges. Our computational experiments indicate that this method is effective in solving moderate-size problems and provides very good candidate solutions early in the branch-and-bound tree.