European Journal of Operational Research最新文献

We investigate counterparty credit risk and credit valuation adjustments in portfolios including derivatives with early-exercise opportunities, under a netting agreement. We show that credit risk and netting agreements have a significant impact on the way portfolios are managed (that is, on options’ exercise strategies) and, therefore, on the value of the portfolio and on the price of counterparty risk. We derive the value of a netted portfolio as the solution of a zero-sum, finite horizon, discrete-time stochastic game. We show that this dynamic-game interpretation can be used to determine the value of the reglementary capital charges required of financial institutions to cover for counterparty credit risk and we propose a numerical valuation method. Numerical investigations show that currently used numerical approaches can grossly misestimate the value of credit valuation adjustments.

Demand uncertainty can lead to excess inventory holdings, capacity creation, emergency deliveries, and stock-outs. The costs of demand uncertainty may be directly borne by upstream suppliers, but can propagate downstream in the form of higher prices. To address these problems, we investigate a practical application of a fixed order commitment contract (FOCC) in which a manufacturer commits to a minimum fixed order quantity each period and receives a per unit price discount from the supplier for the commitment. We model a FOCC as a Stackelberg game in which the supplier offers a price discount anticipating the manufacturer’s response, and the manufacturer subsequently decides on the optimal commitment quantity. We show that a FOCC can smooth the orders received by the supplier, mitigating the negative consequences of demand uncertainty for the supplier, the manufacturer, and the supply chain. We extend the current literature by solving for an endogenous price discount instead of treating it as an exogenous value, and validate our model insights with our research partner, a large international materials handling equipment manufacturer. Using data on 863 parts, we evaluate the relationships between the model parameters, contract parameters, and the contract effectiveness, and show the conditions under which the FOCC generates greater cost savings for both the manufacturer and supplier. Our results help operations managers better understand how to obtain the optimal contract parameters for a FOCC and the circumstances under which such a contract is most beneficial for the company and its supply chain.

The problem under study is based on the challenges faced by the Orthopaedic Clinic at St. Olav’s Hospital in Trondheim, Norway. Variations in demand and supply cause fluctuating waiting lists, and it is challenging to level the activities between the clinic’s two units, the outpatient clinic and the operating theater, to obtain short waiting times for all activities. Based on these challenges, we describe and present a planning problem referred to as the Long-term Master Scheduling Problem (LMSP), where the objective is to construct an integrated Long-term Master Schedule (LMS) that facilitates short waiting times in both units. The LMS can be separated into two schedules, one cyclic high-level schedule, and one non-cyclic low-level schedule. The demand for outpatient clinic consultations and surgeries is stochastic, as are the waiting lists. To account for this, we propose a planning framework consisting of an optimization model to solve the LMSP, and a two-level planning procedure. In the planning procedure, we first solve the LMSP to construct the LMS for the upcoming planning horizon. Then, to adjust to the fluctuating waiting lists, we periodically refine the low-level schedule by solving a constrained LMSP. We also develop a simulation-based evaluation procedure to evaluate the planning framework in a real-life setting and use this to investigate different planning strategies. We find that imposing flexible, dynamic and agile planning strategies improve waiting time outcomes and patient throughput. Furthermore, combining the strategies yields additive improvements.

After a natural disaster such as a hurricane or flooding, the navy can help by bringing supplies, clearing roads, and evacuating victims. If destinations cannot be reached over land, resources can be transported using smaller ships and helicopters, called connectors. To start aid on land as soon as possible this must be done efficiently. In the ship-to-shore problem, trips with their accompanying resources are determined while minimising the makespan. Limited (un)loading capacities, heterogeneous connector characteristics and constraints posed by priority of the resources and grouping of the resources (resource sets) all require that the connector trips are carefully coordinated. Despite the criticality of this coordination, existing literature does not consider resource sets and has only developed heuristics. We provide a formulation that incorporates resource sets and develop (i) an exact branch-and-price algorithm and (ii) a tailored greedy heuristic that can provide upper bounds. We find that 84% of our 98 practical instances terminate within an hour in on average 80 s. Our greedy heuristic can find optimal solutions in two-thirds of these instances, mostly for instances that are very constrained in terms of the delivery order of resources. When improvements are found by the branch-and-price algorithm, the average gap with the makespan of the greedy solution is 40% and, in most cases, these improvements are obtained within three minutes. For the 20 artificial instances, the greedy heuristic has consistent performance on the different types of instances. For these artificial instances improvements of on average 35% are found in reasonable time.

The growing literature on operations management in the context of the sharing economy typically assumes that both customers and providers are fully rational. In contrast, we consider an on-demand service platform (e.g., Didi and Uber) with boundedly rational customers and providers that sets a price charged to customers and a wage paid to providers. Both customers and providers are sensitive to the payment terms set by the platform and also to congestion in the system (given by the relative numbers of available customers and providers in the market). We capture bounded rationality using a model in which customers and providers are incapable of accurately estimating the congestion level. We examine the impact of bounded rationality on the platform profit, consumer surplus, and labor welfare. We find that both customers’ and providers’ bounded rationalities may benefit the platform. Specifically, when customers’ or providers’ bounded rationality level and service valuation are relatively large or the valuation is relatively small, more irrational customers or providers increases the platform’s profit. Moreover, we find that the platform can exploit the bounded rationality differences between customers and providers to gain profit. Counterintuitively, we also demonstrate that the high bounded rationality of customers or providers may increase consumer surplus and/or labor welfare. Finally, bounded rationality on one side (e.g., customer side) can make bounded rationality on the other side (e.g., provider side) more likely to increase the platform’s profit, consumer surplus, or labor welfare under certain conditions.