Ebru K. Bish

A multiple-crane-constrained scheduling problem in a container terminal

Abstract We consider a container terminal loading and unloading containers to and from a set of ships, and storing the containers in the terminal yard. Each ship is served by multiple quay cranes, which load and unload containers to and from ships. Containers are moved between the ships and the yard using a fleet of vehicles, each with unit capacity. The problem is (i) to determine a storage location for each unloaded container, (ii) to dispatch vehicles to containers, and (iii) to schedule the loading and unloading operations on the cranes, so as to minimize the maximum time it takes to serve a given set of ships. This problem is NP-hard, and therefore we develop a heuristic algorithm based on formulating the problem as a transshipment problem. The effectiveness of the heuristic is analyzed from both worst-case and computational points of view.

Dispatching vehicles in a mega container terminal

Abstract.We consider a container terminal discharging and uploading containers to and from ships. The discharged containers are stored at prespecified storage locations in the terminal yard. Containers are moved between the ship area and the yard using a fleet of vehicles, each of which can carry one container at a time. The problem is to dispatch vehicles to the containers so as to minimize the total time it takes to serve a ship, which is the total time it takes to discharge all containers from the ship and upload new containers onto the ship. We develop easily implementable heuristic algorithms and identify both the absolute and asymptotic worst-case performance ratios of these heuristics. In simple settings, most of these algorithms are optimal, while in more general settings, we show, through numerical experiments, that these algorithms obtain near-optimal results for the dispatching problem.

Airline fleet assignment concepts, models, and algorithms

The fleet assignment problem (FAP) deals with assigning aircraft types, each having a different capacity, to the scheduled flights, based on equipment capabilities and availabilities, operational costs, and potential revenues. An airlines fleeting decision highly impacts its revenues, and thus, constitutes an essential component of its overall scheduling process. However, due to the large number of flights scheduled each day, and the dependency of the FAP on other airline processes, solving the FAP has always been a challenging task for the airlines. In this paper, we present a tutorial on the basic and enhanced models and approaches that have been developed for the FAP, including: (1) integrating the FAP with other airline decision processes such as schedule design, aircraft maintenance routing, and crew scheduling; (2) proposing solution techniques that include additional considerations into the traditional fleeting models, such as considering itinerarybased demand forecasts and the recapture effect, as well as investigating the effectiveness of alternative approaches such as randomized search procedures; and (3) studying dynamic fleeting mechanisms that update the initial fleeting solution as departures approach and more information on demand patterns is gathered, thus providing a more effective way to match the airlines supply with demand. We also discuss future research directions in the fleet assignment arena. � 2005 Elsevier B.V. All rights reserved.

Optimal Investment Strategies for Flexible Resources, Considering Pricing and Correlated Demands

We study the optimal resource investment decision faced by a two-product, price-setting firm that operates in a monopolistic setting and employs a postponed pricing scheme. The firm has the option to invest in dedicated resources as well as a more expensive, flexible resource that can satisfy both products. While the resource investment decision is made under demand uncertainty, pricing and resource allocation decisions are postponed to the time when demand curves are realized. Our analysis provides the structure of the firms optimal resource investment strategy as a function of demand parameters and investment costs, and shows that the flexible resource investment decision follows athreshold policy. We also show that it can be optimal for the firm to invest in the flexible resource even when demand patterns are perfectly positively correlated. The reason for flexible capacity investment in this case is financial rather than risk pooling. On the other hand, we show that it can be optimal for the firm not to invest in the flexible resource even when demand patterns are perfectly negatively correlated. The flexible resource investment decision in this case depends on the profitability of the two products. Based on our analysis, we provide principles on the firms optimal resource investment decision.

Managing Flexible Capacity in a Make-to-Order Environment

Flexible capacity has been shown to be very effective to hedge against forecast errors at the investment stage. In a make-to-order environment, this flexibility can also be used to hedge against variability in customer orders in the short term. For that purpose, production levels must be adjusted each period to match current demands, to give priority to the higher margin product, or to satisfy the closest customer. However, this will result in swings in production, inducing larger order variability at upstream suppliers and significantly higher component inventory levels at the manufacturer. Through a stylized two-plant, two-product capacitated manufacturing setting, we show that the performance of the system depends heavily on the allocation mechanism used to assign products to the available capacity. Although managers would be inclined to give priority to higher-margin products or to satisfy customers from their closest production site, these practices lead to greater swings in production, result in higher operational costs, and may reduce profits.

Polyhedral Analysis and Algorithms for a Demand-Driven Refleeting Model for Aircraft Assignment

The current airline practice in conducting fleet assignments is to begin assigning aircraft capacity to scheduled flights well in advance of departures. However, the accuracy of the passenger demand forecast improves markedly over time, and revisions to the initial fleet assignment become naturally pertinent when the observed demand differs considerably from the assigned aircraft capacities. The demand-driven refleeting (DDR) approach proposed in this paper offers a dynamic reassignment of aircraft capacities to the flight network, when improved demand forecasts become available, so as to maximize the total revenue. Because of the need to preserve the initial crew schedule, this reassignment approach is limited within a single family of aircraft types and to the flights assigned to this particular family. This restriction makes it computationally tractable to include more relevant path-level demand information into the DDR model. Accordingly, we construct a mixed-integer programming model for this enhanced problem context and study its polyhedral structure to explore ways for tightening its representation and for deriving certain classes of valid inequalities. Various schemes for implementing such reformulation techniques are investigated and tested using a set of simulated and real instances obtained from United Airlines.

Optimal capacity for substitutable products under operational postponement

We consider a monopolist producing two substitutable products with one flexible (shared) capacity. The demand of each product is a linear function of the prices of both products, and is subject to an additive shock. We study the impact of two key drivers, namely the degree of substitution between the products and the level of operational postponement, on the optimal capacity and the resulting expected profit. We show that the relationship between the optimal capacity and the degree of product substitution is not impacted by the different postponement strategies the firm can utilize or by the different settings (forced clearance versus holdback) considered in the previous literature. On the other hand, how capacity is affected by postponement critically depends on how closely substitutable the products are. In particular, we show that the well-known result that operational postponement and capacity are strategic complements in a single-product setting (Van Mieghem and Dada, 1999) no longer holds in our setting, because the two substitutable products are now linked through consumer-driven substitution, which the firm can influence through pricing. In particular, capacity and operational postponement (in the form of quantity postponement) can be either strategic substitutes or strategic complements, and this depends on both the firms cost structure and the degree of substitution between the products. We also study the impact of forced clearance on the firms expected profit and find that clearance deteriorates the firms earnings more when the products it offers are highly differentiated.

Cost-effectiveness of Babesia microti antibody and nucleic acid blood donation screening using results from prospective investigational studies.

Babesia microti causes transfusion‐transmitted babesiosis (TTB); currently, blood donor screening assays are unlicensed but used investigationally.

Optimal Capacity, Product Substitution, Linear Demand Models, and Uncertainty

We study the capacity, pricing, and production decisions of a monopolist producing two substitutable products with flexible capacity. Although the capacity decision needs to be made ex ante, under demand uncertainty, pricing and production decisions can be postponed until after uncertainty is resolved. We show how key demand parameters (the nature of uncertainty, market size, and market risk) impact the optimal capacity decision under the linear demand function. In particular, we show that if the demand shock is multiplicative, then in terms of the “invest or not” decision, the firm will be immune to forecast errors in parameters of the underlying demand distribution. Furthermore, incorrectly modeling the demand shock as additive, when, in fact, it is multiplicative, may lead to overinvestment. On the other hand, although the concept of a growth in market size leads to similar conclusions under both additive and multiplicative demand shocks, how market risk affects the optimal capacity decision depends critically on the form of the demand shock as well as its correlation structure. Our analysis provides insights and principles on the optimal capacity investment decision under various demand settings.

Impact of Manufacturing Flexibility on Supply Chain Performance in the Automotive Industry

The basis of competition in the automotive industry is changing. While product innovation and styling remain the most important areas of competition, an almost equally fierce battle is now developing in the areas of customization and order fulfillment (Stalk, Stephenson and King [35]). Currently, all models of vehicle distribution are fundamentally inventory-driven and do not promote customized ordering. However, several vehicle manufacturers — most notably Ford and General Motors — have recently launched initiatives to transform their companies from predominantly make-to-stock to predominantly make-to-order producers. This will enable vehicle manufacturers and their dealers not only to dramatically reduce their finished goods inventory but also respond to challenges and threats from third party Internet companies.