Ali Ekici

Pandemic influenza response

Recent incidents of avian flu (H5N1) in Asia and the pandemic influenza cases in history (1918, 1957 and 1968) suggest that a future pandemic influenza is inevitable and likely imminent. Governments and non-governmental organizations prepare response plans on how to react to a pandemic influenza. In this paper, we study the logistics side of the problem, specifically, food distribution logistics during the pandemic influenza. For this purpose, we develop a disease spread model that assists in estimating the food need geographically at a given time. Then, we develop an integrated solution approach called the Dynamic Update Approach to build the food distribution network. We run our integrated disease spread and facility location model for the state of Georgia and present the estimated number of infections and meals needed in each census tract for a one year period.

Solution approaches for the cutting stock problem with setup cost

In this paper, we study the Cutting Stock Problem with Setup Cost (CSP-S) which is a more general case of the well-known Cutting Stock Problem (CSP). In the classical CSP, one wants to minimize the number of stock items used while satisfying the demand for smaller-sized items. However, the number of patterns/setups to be performed on the cutting machine is ignored. In most cases, one has to find the trade-off between the material usage and the number of setups in order to come up with better production plans. In CSP-S, we have different cost factors for the material and the number of setups, and the objective is to minimize total production cost including both material and setup costs. We develop a mixed integer linear program and analyze a special case of the problem. Motivated by this special case, we propose two local search algorithms and a column generation based heuristic algorithm. We demonstrate the effectiveness of the proposed algorithms on the instances from the literature.

Modeling Influenza Pandemic and Planning Food Distribution

Based on the recent incidents of H5N1, H1N1, and influenza pandemics in history (1918, 1957, and 1968) experts believe that a future influenza pandemic is inevitable and likely imminent. Although the severity of influenza pandemics vary, evidence suggests that an efficient and rapid response is crucial for mitigating morbidity, mortality, and costs to society. Hence, preparing for a potential influenza pandemic is a high priority of governments at all levels (local, state, federal), nongovernmental organizations (NGOs), and companies. In a severe pandemic, when a large number of people are ill, infected persons and their families may have difficulty purchasing and preparing meals. Various government agencies and NGOs plan to provide meals to these households. In this paper, in collaboration with the American Red Cross, we study food distribution planning during an influenza pandemic. We develop a disease spread model to estimate the spread pattern of the disease geographically and over time, combine it with a facility location and resource allocation network model for food distribution, and develop heuristics to find near-optimal solutions for large instances. We run our combined disease spread and facility location model for the state of Georgia and present the estimated number of infections and the number of meals needed in each census tract for a one-year period along with a design of the supply chain network. Moreover, we investigate the impact of voluntary quarantine on the food demand and the food distribution network and show that its effects on food distribution can be significant. Our results could help decision makers prepare for a pandemic, including how to allocate limited resources and respond dynamically.

Coordinating collection and appointment scheduling operations at the blood donation sites

Coordinating pickup and appointment schedules improves the platelet supply.Mathematical formulation can find solutions with an average optimality gap of around 6%.A priori clustering improves both the solution quality and the runtime. According to the regulations imposed by the U.S. Food and Drug Administration and the American Association of Blood Banks, in order to extract platelets, donated blood units have to be processed at a processing center within six hours of donation time. In this paper, considering this processing time requirement of donated blood units for platelet production we study collection and appointment scheduling operations at the blood donation sites. Specifically, given the blood donation network of a blood collection organization, we try to coordinate pickup and appointment schedules at the blood donation sites to maximize platelet production. We call the problem under consideration Integrated Collection and Appointment Scheduling Problem. We first provide a mixed integer linear programming model for the problem. Then, we propose a heuristic algorithm called Integer Programming Based Algorithm. We perform a computational study to test the performance of the proposed model and algorithm in terms of solution quality and computational efficiency on the instances from Gulf Coast Regional Blood Center located in Houston, TX.

Multiple agents maximum collection problem with time dependent rewards

In this paper, we study Multiple Agents Maximum Collection Problem with Time Dependent Rewards (MAMCP-TDR) which is a variant of Multiple Tour Maximum Collection Problem (MTMCP) (Butt & Cavalier, 1994). In MAMCP-TDR, there are multiple agents to collect linearly decreasing rewards over time. The objective is to maximize total surplus (total reward collected minus total travel cost) by routing multiple agents from a central depot. We propose a mathematical formulation for the problem. Since the problem is strongly NP-hard, we develop a heuristic algorithm, called Cluster-and-Route Algorithm (CRA), to find near-optimal solutions. In CRA, first we cluster the targets/rewards using the well-known k-means clustering algorithm. Then, we find tours by solving a single agent problem for each cluster. To solve the single agent problem, we propose several routing algorithms. Finally, we implement several improvement ideas to improve the final solution. We conduct computational experiments on randomly generated instances and instances of a related problem in the literature to test the performance of routing algorithms for single agent case and the CRA for MAMCP-TDR in terms of solution time and solution quality. Compared to the mathematical formulation, we conclude that CRA provides solutions with similar quality for small instances and performs significantly better both in terms of solution time and solution quality on medium and large instances.

Pricing decisions in a strategic single retailer/dual suppliers setting under order size constraints

In this paper, we study a duopolistic market of suppliers competing for the business of a retailer. The retailer sets the order cycle and quantities from each supplier to minimize its annual costs. Different from other studies in the literature, our work simultaneously considers the order size restriction and the benefit of order consolidation, and shows non-trivial pricing behaviour of the suppliers under different settings. Under asymmetric information setting, we formulate the pricing problem of the preferred supplier as a non-linear programming problem and use Karush–Kuhn–Tucker conditions to find the optimal solution. In general, unless the preferred supplier has high-order size limit, it prefers sharing the market with its competitor when retailer’s demand, benefit of order consolidation or fixed cost of ordering from the preferred supplier is high. We model the symmetric information setting as a two-agent non-zero sum pricing game and establish the equilibrium conditions. We show that a supplier might set a ‘threshold price’ to capture the entire market if its per unit fixed ordering cost is sufficiently small. Finally, we prove that there exists a joint-order Nash equilibrium only if the suppliers set identical prices low enough to make the retailer place full-size orders from both.

Cyclic Delivery Schedules for an Inventory Routing Problem

We consider an inventory routing problem where a common vendor is responsible for replenishing the inventories of several customers over a perpetual time horizon. The objective of the vendor is to minimize the total cost of transportation of a single product from a single depot to a set of customers with deterministic and stationary consumption rates over a planning horizon while avoiding stock-outs at the customer locations. We focus on constructing a repeatable cyclic delivery schedule for the product delivery. We propose a novel algorithm, called the Iterative Clustering-Based Constructive Heuristic Algorithm, to solve the problem in two stages: i clustering, and ii delivery schedule generation. To test the performance of the proposed algorithm in terms of solution quality and computational efficiency, we perform a computational study on both randomly generated instances and real-life instances provided by an industrial gases manufacturer. We also compare the performance of the proposed algorithm against an algorithm developed for general routing problems.

Improving blood products supply through donation tailoring

Abstract Recent technological advances, called Multicomponent Apheresis, allow tailoring the blood donations based on the demand and current inventory levels of blood products. Different from the most common type of blood donation (known as Whole Blood Donation), Multicomponent Apheresis allows the donation of one or more transfusable units of one or more blood products. Considering the changing demand for blood products during a planning horizon, deferral times, perishability of blood products, and limited donor pool, Multicomponent Apheresisprovides an opportunity for increased donor utilization and hence a better managed blood supply chain. However, except some general guidelines proposed by blood donation organizations, the literature lacks analytical tools which can be used to fully explore the potential advantages of Multicomponent Apheresis, including the reduction in donation related costs and better utilization of the donor pool. In this paper, we develop models and solution approaches for tailoring the donations in order to quantify the potential benefits of Multicomponent Apheresis. More specifically, we define the Blood Donation Tailoring Problem where the objective is to minimize the total donation, inventory and disposal costs of blood products while satisfying the demand for blood products during a planning horizon by determining the donation schedule of a given donor pool. We develop a mathematical model and a column generation approach to tailor the donations. We also propose a more practical rule-of-thumb which can be easily implemented by the blood donation organizations. We compare the performances of the proposed approaches against a lower bound and the current practice at an apheresis facility. Finally, we also show that the proposed column generation approach can easily be modified to handle realistic aspects of the problem including stock-out and donor eligibility/preferences.

Blood Supply Chain Management and Future Research Opportunities

In this chapter, we discuss the challenges and research opportunities in the blood collection operations and explore the benefits of recent advances in the blood donation process. According to the regulations, donated blood has to be processed in a processing facility within 6 h of donation. This forces blood donation organizations to schedule continuous pickups from donation sites. The underlying mathematical problem is a variant of well-known Vehicle Routing Problem (VRP). The main differences are the perishability of the product to be collected, and the continuity of donations. We discuss the implications of such differences on collection routes from donation centers. Recent advances such as multicomponent apheresis (MCA) allow the donation of more than one component and/or more than one transfusable unit of each blood product. MCA provides several opportunities including (1) increasing the donor utilization, (2) tailoring the donations based on demand, and (3) reducing the infection risks in the transfusion. We also discuss MCA, its potential benefits and how to best use MCA in order to improve blood products availability and manage donation/disposal costs.

Managing platelet supply through improved routing of blood collection vehicles

Abstract In this paper, we study the routing of blood collection vehicles for improving the platelet supply in the blood supply chain. In order to extract platelets, donated blood has to be processed at a central processing facility within six hours of donation time. Blood collection organizations have to dispatch collection vehicles and schedule pickups from the donation sites so that the donated units can be used in platelet production. Because of the accumulating behavior of donations and the six-hour processing time limit, routing of blood collection vehicles is a time-sensitive routing problem. We analyze the routing decisions in such a setting and propose an integrated clustering and routing framework to collect and process the maximum number of donations for platelet production. In our analysis, motivated by the practices in real-life, we cluster the donation sites so that only a single vehicle serves the donation sites in each cluster. In the proposed framework, we make the clustering and routing decisions in an integrated manner so that we can foresee the impact of adding a donation site to a cluster on the routing decisions. For the routing step, we propose several heuristic algorithms, two of which have a greedy nature and the others are based on a priori tour generation and selection scheme. To evaluate the performances of the proposed heuristics, we develop an upper bound by relaxing the number of vehicles so that one vehicle is available for each donation site. Using the proposed heuristic algorithms, we obtain solutions with around 15% optimality gaps with respect to the upper bound.