Okan Örsan Özener

Lane-Exchange Mechanisms for Truckload Carrier Collaboration

Because of historically high fuel prices, the trucking industrys operating expenses are higher than ever and thus profit margins are lower than ever. To cut costs, the trucking industry is searching for and exploring new ideas. We investigate the potential of collaborative opportunities in truckload transportation. When carriers serve transportation requests from many shippers, they may be able to reduce their repositioning costs by exchanging one or more of them. We develop optimization models to determine the maximum benefit that can be derived from collaborating. We also develop various exchange mechanisms which differ in terms of information sharing requirements and side payment options that allow carriers to realize some or all of the costs savings opportunities.

Collaboration in Cargo Transportation

We discuss two forms of collaboration in cargo transportation: carrier alliances in sea and air cargo, and shipper collaborations in trucking. After discussing the current industry settings that make such collaborations beneficial, we present a set of questions that need to be answered in the collaborative setting. These questions deal with issues such as (i) what is the maximum benefit the collaboration can achieve? (ii) How should these benefits be allocated? (iii) What membership rules and regulations will increase the sustainability of a given alliance? We provide a set of models for resolving these issues and analyze the properties of the solutions obtained from them.

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.

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.

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.

Solving a large-scale crew pairing problem

Airline companies seek to solve the problem of determining an assignment of crews to a pre-determined flight schedule with minimum total cost, called the Crew Pairing Problem (CPP). Most of the existing studies focus on the CPP of North American airlines, which widely differs from that of most European airline companies in terms of the objective function, the flight structure, and the planning horizon. In this study, we develop an optimization-driven heuristic algorithm that can efficiently handle large-scale instances of the CPP that must be solved on a monthly basis. We perform computational experiments using flight schedules of an European airline company to test the performance of the solution method. Our computational results demonstrate that our algorithm is able to provide high-quality solutions to monthly instances with up to 27 000 flight legs.

Developing a Collaborative Planning Framework for Sustainable Transportation

Currently, as being the highest petroleum consuming sector in the world, transportation significantly contributes to the total greenhouse gas emissions in the world. Road transportation not only is responsible for approximately 20% of the total emissions of carbon dioxide in the EU and in the US but also has a steadily increasing trend in contributing to global warming. Initiatives undertaken by authorities, such as Emission cap and trade in the EU, limit the emissions resulted from the actions of the companies and also give economic incentives to companies to reduce their emissions. However, in logistics systems with multiple entities, it is difficult to assess the responsibilities of the companies both in terms of costs and emissions. In this study, we consider a delivery network with multiple customers served by a single carrier, which executes a delivery plan with the minimum transportation cost, and allocate the resulting costs and the emissions among the customers in a fair manner. We develop allocation mechanisms for both costs and emissions. In order to develop a mechanism that provides further reduction of the emissions, we study a setting where the carrier takes the responsibility of the emissions and reflects the resulting inefficiencies while charging the customers.

Solving a large-scale integrated fleet assignment and crew pairing problem

Airline schedule planning problems are typically decomposed into smaller problems, which are solved in a sequential manner, due to the complexity of the overall problems. This results in suboptimal solutions as well as feasibility issues in the consecutive phases. In this study, we address the integrated fleet assignment and crew pairing problem (IFACPP) of a European Airline. The specific network and cost structures allow us to develop novel approaches to this integrated problem. We propose an optimization-driven algorithm that can efficiently handle large scale instances of the IFACPP. We perform a computational study on real-world monthly flight schedules to test the performance of our solution method. Based on the results on instances with up to 27,500 flight legs, we show that our algorithm provides solutions with significant cost savings over the sequential approach.

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.