Serhan Duran

Pre-Positioning of Emergency Items for CARE International

Each year, about 500 natural disasters kill approximately 70,000 people and affect more than 200 million people worldwide. In the aftermath of such events, large quantities of supplies are needed to provide relief aid to the affected. CARE International is one of the largest humanitarian organizations that provide relief aid to disaster survivors. The most vital issues in disaster response are agility in mobilizing supplies and effectiveness in distributing them. To improve disaster response, a research group from Georgia Institute of Technology collaborated with CARE to develop a model to evaluate the effect that pre-positioning relief items would have on CAREs average relief-aid emergency response time. The models results helped CARE managers to determine a desired configuration for the organizations pre-positioning network. Based on the results of our study and other factors, CARE has pre-positioned relief supplies in three facilities around the world.

Policies utilizing tactical inventory for service-differentiated customers

We consider a manufacturer serving two customer classes where one wants the item immediately and the second receives a discount to accept a delay. We show that an (S,R,B) base-stock policy is optimal under differentiation and non-differentiation where S, R, and B are the order-up-to, reserve-up-to, and backlog-up-to amounts.

Optimal production and inventory policies of priority and price-differentiated customers

Many firms are exploring production and supply chain strategies when customers may be segmented into different classes based on service level or priority. Such segmentation can result in a more efficient production system as well as a better match between supply and demand. In this research, we analyze a system with customer classes 1 and 2, where customer class 1 has a higher priority of fulfillment than customer class 2 in the same period. We develop an optimal production and inventory strategy that rations current and future limited capacity between customer classes 1 and 2, through reserving inventory for the future and accepting orders now for future delivery when demand and production are general stochastic functions. We show that a modified order-up-to policy (S*, R i*, B i*) is optimal in each period. S* is the targeted inventory level after production at the beginning of the period; R 1* represents the optimal inventory to be protected from being sold to both classes, and R 2* is the additional amount of inventory to protect from class 2. B 2* is the optimal amount of future capacity to make available to both classes through backlogging, and B 1* is the additional backlogging amount for class 1. Computational analysis shows that the differentiation strategy can result in a significant profit improvement over a traditional inventory policy.

Humanitarian Logistics: Advanced Purchasing and Pre-Positioning of Relief Items

Unfortunately, the world has experienced frequent disasters as well as mega-disasters in the last decade. The challenges faced during the relief efforts to those disasters called for improvements in the area of humanitarian logistics. In this chapter, first we present introductory knowledge on disaster management and humanitarian logistics. The complexities and inefficiencies in the current relief response practice are indicated. To improve the disaster response, we investigate the options of advance purchasing and pre-positioning of the relief items through applied projects performed for different humanitarian organizations.

Effects of natural disaster trends: a case study for expanding the pre-positioning network of CARE International.

The increasing number of natural disasters in the last decade necessitates the increase in capacity and agility while delivering humanitarian relief. A common logistics strategy used by humanitarian organizations to respond this need is the establishment of pre-positioning warehouse networks. In the pre-positioning strategy, critical relief inventories are located near the regions at which they will be needed in advance of the onset of the disaster. Therefore, pre-positioning reduces the response time by totally or partially eliminating the procurement phase and increasing the availability of relief items just after the disaster strikes. Once the pre-positioning warehouse locations are decided and warehouses on those locations become operational, they will be in use for a long time. Therefore, the chosen locations should be robust enough to enable extensions, and to cope with changing trends in disaster types, locations and magnitudes. In this study, we analyze the effects of natural disaster trends on the expansion plan of pre-positioning warehouse network implemented by CARE International. We utilize a facility location model to identify the additional warehouse location(s) for relief items to be stored as an extension of the current warehouse network operated by CARE International, considering changing natural disaster trends observed over the past three decades.

Pre-Positioning Disaster Response Facilities and Relief Items

ABSTRACT Large-scale disasters cause enormous damage to people living in the affected areas. Providing relief quickly to the affected is a critical issue in recovering the effects of a disaster. Pre-disaster planning has an important role on reducing the arrival time of relief items to the affected areas and efficiently allocating them. In this study, a mixed integer programming model is proposed in order to pre-position warehouses throughout a potential affected area and determine the amount of relief items to be held in those warehouses. Time between the strike of the disaster and arrival of relief items at the affected areas is aimed to be minimized. In addition, using probabilistic constraints, the model ensures that relief items arrive at affected areas within a certain time window with certain reliability. Considering instable fault lines on which Istanbul is located, the proposed model is applied to the Istanbul case for pre-positioning warehouses a priori to the possible expected large-scale earthquake.

Dynamic switching times from season to single tickets in sports and entertainment

Revenue management can be used in many industries where there is a limited, perishable capacity and the market can be segmented. In this paper we focus on the sales of event tickets in the Sports and Entertainment industries, where tickets are sold exclusively as season tickets initially or as single events later in the selling horizon. We specifically study the optimal time to switch between these market segments dynamically as a function of the state of the system. Under Poisson demand processes, we find the optimal switching time is a set of time thresholds that depends on the remaining inventory and time left in the horizon. We use numerical experiments to show that significant profit improvements can be obtained by dynamically deciding the optimal switch time over the case when the date is announced in advance. We also study an extension where “early switch to a low-demand event” is allowed.

Selection of event tickets for bundling in sports and entertainment industry

Most sports and entertainment firms offer season tickets first, and they allow purchasing single tickets at a later date. Basic decision problems within this context are the determination of optimal time at which switch from bundled tickets to single tickets should occur and the decision of which event tickets to include into the bundle. In this paper we focused on the second decision problem. For a given schedule of events, where ticket demands follow a Poisson Process, the tickets of a certain number of events are bundled and a discount is applied to the prices of these bundled tickets, with the aim of increasing total revenue. We find increase and decrease patterns in total revenue as the time slots of bundled events vary. With the help of these patterns, we develop a heuristic approach that creates easy-to-use business rules for the selection of profitable bundles for a given schedule.

League scheduling and game bundling in sports industry

Most sport clubs offer season tickets first and they allow purchasing single tickets at a later date. There are several decision problems within this context; the determination of the optimal time at which the switch from bundled tickets to single tickets should occur, the decision of which event tickets to include into the bundle depending on the schedule of the team and the creation of a league schedule enabling revenue enhancements from game bundling. In this paper we have focused on the last decision problem. We analyze league scheduling and game bundling decisions together for a double round robin tournament in order to maximize the total revenue generated by all of the participating teams in the league. A heuristic method is offered which utilizes the approximate expected revenue values obtained by revenue increase and decrease patterns of bundled tickets. We test the offered heuristics performance and observe significant benefits numerically.

Dynamically Switching among Bundled and Single Tickets with Time-Dependent Demand Rates

The most important market segmentation in sports and entertainment industry is the competition between customers that buy bundled and single tickets. A common selling practice is starting the selling season with bundled ticket sales and switching to selling single tickets later on. The aim of this practice is to increase the number of customers that buy bundles, which in return increases the load factor of the events with low demand. In this paper, we investigate the effect of time dependent demand on dynamic switching times from bundled to single ticket sales and the potential revenue gain over the case where the demand rate of events is assumed to be constant with time.