The vehicle pricing game (VPG) which addresses the vehicles' viewpoints within a vehicle routing problem (VRP) is introduced. Each vehicle acts as a player who demands a price per kilometer. That is, vehicles represent decentralized actors in transport systems, e.g., carriers under subcontracts. Based on these prices, a VRP is solved and profits are generated. Which price should a vehicle choose to maximize its own profit, considering the competition among vehicles? To answer this question, site dependencies leading to inhomogeneous vehicles are included. More detailed, skill-levels, e.g., relating to the size of a vehicle, are used to indicate a vehicle's ability to carry out particular services. Moreover, penalty options are added. The VPG serves as an element of a vertical collaboration in a transport scenario and thus provides decision support for cooperative models. Theoretical results for the VPG are provided for a particular case of a two-player ring network game, for which the full set of equilibria is described and their uniqueness is discussed. It is shown that the uniqueness of the higher-skilled vehicle's payoff is guaranteed even for multiple equilibria. The competition ratio is defined; it restricts a vehicle's price to keep its competitiveness. Moreover, the acceptance ratio gives a lower bound on prices such that a loss of market share is still accepted. Experimental results are provided for general networks including the analysis of penalty options. It is demonstrated that strict site dependencies by tendency lead to monopolistic structures. In addition, particular penalty types show a positive effect regarding load imbalances caused by universally skilled vehicles.

The dominating view in the literature is that renewable electricity production increases the price variance on spot markets for electricity. In this paper, we critically review this hypothesis. Using a static market model, we identify the variance of the infeed from intermittent electricity sources (IES) and the shape of the industry supply curve as two pivotal factors influencing the electricity price variance. The model predicts that the overall effect of IES infeed depends on the produced amount: while small to moderate quantities of IES tend to decrease the price variance, large quantities have the opposite effect. In the second part of the paper, we test these predictions using data from Germany, where investments in IES have been massive in the recent years. The results of this econometric analysis largely conform to the predictions from the theoretical model. Our findings suggest that subsidy schemes for IES capacities should be complemented by policy measures supporting variance absorbing technologies such as smart-grids, energy storage, or grid interconnections to ensure the build-up of sufficient capacities in time.

Enhanced indexation (EI) is the problem of selecting a portfolio that should produce excess return with respect to a given benchmark index. In this work, we propose a linear bi-objective optimization approach to EI that maximizes average excess return and minimizes underperformance over a learning period. Our model can be efficiently solved to optimality by means of standard linear programming techniques. On the theoretical side, we investigate conditions that guarantee or forbid the existence of a portfolio strictly outperforming the index. On the practical side, we support our model with extensive empirical analysis on publicly available real-world financial datasets, including comparison with previous studies, performance and diversification analysis, and verification of some of the proposed theoretical results on real data.

Railway crew scheduling deals with generating driver duties for a given train timetable such that all work regulations are met and the resulting schedule has minimal cost. Typical problem instances in the freight railway industry require the generation of duties for thousands of drivers operating tens of thousands of trains per week. Due to short runtime requirements, common solution approaches decompose the optimization problem into smaller subproblems that are solved separately. Several studies have shown that the way of decomposing the problem significantly affects the solution quality. An overall best decomposition strategy for a freight railway crew scheduling problem, though, is not known. In this paper, we present general considerations on when to assign two scheduled train movements to separate subproblems (and when to rather assign them to the same subproblem) and deduct a graph partitioning based decomposition algorithm with several variations. Using a set of real-world problem instances from a major European railway freight carrier, we evaluate our strategy and benchmark the performance of the decomposition algorithm both against a common non-decomposition algorithm and a lower bound on the optimal solution schedule. The test runs show that our decomposition algorithm is capable of producing high-quality solution schedules while significantly cutting runtimes compared to the non-decomposition solution algorithm. We are following a "greenfield" approach, where no information on previous schedules is needed. Hence, our approach is applicable to any railway crew scheduling setting, including network enlargement, integration of new customers, etc.

This paper develops a generic forecasting framework for product returns that combines concepts used in different disciplines. If more-step ahead forecasts of product returns are required, estimating sales data is necessary. This is accomplished by adopting growth curve models based on the extended Kalman filter. In order to capture the process generating product returns more adequately than in the literature, we propose an adaptive Bayesian approach to forecast future returns. The combination of these two concepts enables us to conduct more-step ahead forecasts. We evaluate the robustness of this approach against Holts approach, a Kalman filter based approach, and the model by Toktay et al. (Manag Sci 46:1412---1426, 2000) for varying degrees of misspecification. In addition, we create a link between forecasting accuracy and the economic value added. This enables the user to choose the economically worthwhile forecasting method that trades-off additional operating costs and savings in working capital. Our theoretical and numerical results indicate that our approach operates on high accuracy even in situations when the underlying assumptions are obviously violated.

In this paper, the problem of robust load planning for trains in intermodal container terminals is studied. The goal of load planning is to choose wagon settings and assign load units to wagons of a train such that the utilization of the train is maximized, and setup and transportation costs in the terminal are minimized. However, in real-world applications, many of the parameters needed for the model are not known exactly. Since feasibility of the resulting load distribution has always to be guaranteed, we decided to use a robust approach. In particular, we apply the concepts of strict and adjustable robustness to enhance the load planning problem. Based on a formulation developed in Bruns and Knust (OR Spectrum 34:511–533, 2012) for the deterministic load planning problem, we propose mixed-integer linear programming formulations for most of the respective robust counterparts, dependent on the type of uncertainty. An experimental study shows that most of the robust problems can be solved within runtimes of a few minutes, which is good enough for real-world applications. Furthermore, our results indicate that robust solutions may improve the planning considerably, and that it is promising to add robustness even to large mixed-integer programs with many and diverse technical constraints.

Outsourcing has been employed in supply chain management to reduce capital investment, enhance flexibility, reduce manufacturing cost, shorten cycle time, and improve service quality. This study aims to propose a portfolio optimization model considering risk diversification and delivery uncertainty for vendor portfolio selection and order allocation. Three portfolio objectives for maximizing performance of selected vendors, diversifying the portfolio risk, and minimizing total cost are considered. A comparison of two proposed stochastic programming techniques shows that robust optimization can generate a better solution whereas probabilistic models can identify the potential alternative vendor based on a data-driven approach. An application study was conducted in a leading semiconductor company in Taiwan to estimate the validity of the proposed approach. The results have shown practical viability that the proposed approach can generate the results with better vendor performance, diversified risk, and minimal total cost.

The trend of globalization and outsourcing makes supply unreliable and companies begin to have supplier diversity embedded into their procurement departments. Traditionally, contract suppliers are a major supply channel for many companies, while the effectiveness of reactive supply sources, such as spot markets, is often ignored. Spot markets have negligible lead times and higher average prices comparing with contract suppliers. In our research, procurement utilizes the combinatorial benefits of proactive supply (a contract supplier) and reactive supply (a spot market). The uncertainties of yields, spot prices, and demand, and the correlations among them are also taken into consideration when designing procurement plans. The objectives of this paper were to evaluate the effectiveness of dealing with uncertain supply using the spot market along with the contract supplier and to model the dependences among all the potential uncertainties. This research also seeks high expected profits without overlooking the associated variances. The analytical expression to determine the optimal order quantity is obtained under the most general situations where commodities can be both bought and sold via the spot market. Some properties are derived to provide useful managerial insights. In addition, reference scenarios, such as pure contract sourcing and the spot market restricted for buying or selling only, are included for comparison purposes.

When applying the partial least squares structural equation modeling (PLS-SEM) method, the assumption that the data stem from a single homogeneous population is often unrealistic. For the full set of data, unobserved heterogeneity in the PLS path model estimates may result in misleading interpretations. This research presents the PLS genetic algorithm segmentation (PLS-GAS) method to account for unobserved heterogeneity in the path model estimates. The results of a simulation study guide an assessment of this novel approach. PLS-GAS allows for uncovering unobserved heterogeneity and identifying different groups within a data set. In an application on customer satisfaction data and the American customer satisfaction index model, the method identifies distinctive group-specific PLS path model estimates which allow for a further differentiated interpretation of the results.

At a cross-docking terminal, inbound shipments are directly transshipped across the terminal to designated outbound trucks, so that delays and inventories are kept as low as possible. We consider an operational truck scheduling problem, where a dock door and a start time have to be assigned to each inbound truck. A set of outbound trucks is scheduled beforehand and, therefore, departure times are fixed. If a shipment is not unloaded, transshipped to the outbound gate and loaded onto the designated outbound truck before its departure, we consider the shipments's value as lost profit. The objective is to minimize total lost profit. This paper at hand formalizes the resulting truck scheduling problem. We settle its computational complexity and develop heuristics (namely, decomposition procedures and simulated annealing) in order to tackle the problem. We show the efficiency of these heuristics by means of a computational study. Last but not least, a case study is presented.