Joakim Ekström

A Mean Value Cross Decomposition Strategy for Demand-side Management of a Pulping Process

Energy is becoming a critical resource for process industries as introduction of new policies drive changes in the energy supply systems. Energy availability and pricing is much more volatile. In this study, we propose a Mean Value Cross Decomposition approach to functionally separate production scheduling from energy-cost optimization. Such a decomposition makes it possible to exploit existing optimization solutions avoiding a need to create a new monolithic model. The proposed framework is applied to a continuous process of thermo-mechanical pulping using a discrete-time Resource-Task Network model. Example case study scenarios show that the approach gives optimal system-wide solutions while keeping the models separated.

Solving a mixed integer linear program approximation of the toll design problem using constraint generation within a branch-and-cut algorithm

This paper addresses the global optimality of the toll design problem (TDP) by formulating a mixed integer linear program (MILP) approximation. In the TDP, the objective is to maximise the social surplus by adjusting toll locations and levels in a road traffic network. The resulting optimisation problem can be formulated as a mathematical program with equilibrium constraints. An MILP is obtained by piecewise linear approximation of the nonlinear functions in the TDP, and we present a domain reduction scheme to reduce the error introduced by these approximations. Previous approaches for solving the MILP approximation have been relying on a large number of MILPs to be solved iteratively within a cutting constraint algorithm (CCA). This paper instead focuses on the development of a solution algorithm for solving the MILP approximation in which the CCA is integrated within a branch-and-cut algorithm, which only requires one MILP to be solved.

Optimal toll locations and toll levels in congestion pricing schemes: a case study of Stockholm

As congestion pricing has moved from theoretical ideas in the literature to real-world implementation, the need for decision support when designing pricing schemes has become evident. This paper deals with the problem of finding optimal toll levels and locations in a road traffic network and presents a case study of Stockholm. The optimisation problem of finding optimal toll levels, given a predetermined cordon, and the problem of finding both optimal toll locations and levels are presented, and previously developed heuristics are used for solving these problems. For the Stockholm case study, the possible welfare gains of optimising toll levels in the current cordon and optimising both toll locations and their corresponding toll levels are evaluated. It is shown that by tuning the toll levels in the current congestion pricing cordon used in Stockholm, the welfare gain can be increased significantly, and furthermore improved by allowing a toll on a major bypass highway. It is also shown that, by optimising both toll locations and levels, a congestion pricing scheme with welfare gain close to what can be achieved by marginal social cost pricing can be designed with tolls being located on only a quarter of the tollable links.

Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed.

Traffic management for smart cities

Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management . This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example, short-term prediction and control.

Adjustments of public transit operations planning process for the use of electric buses

Abstract This work investigates and discusses how the introduction of electric buses (EB), both battery and plug-in hybrid EB, will and should change the operations planning of a public transit system. It is shown that some changes are required in the design of a transit route network, and in the timetabling and vehicle scheduling processes. Other changes are not required, but are advisable, using this opportunity upon the introduction of EB. The work covers the main characteristics of different types of EB with a short description, including the most popular charging technologies, and it presents the generally accepted transit operations planning process. Likewise, it describes and analytically formulates new challenges that arise when introducing EB. The outcome of the analyses shows that multiple new considerations must take place. It is also shown that the different charging techniques will influence the operations planning process in different ways and to a varying extent. With overnight, quick and continuous charging, the main challenges are in the network route design step, given the possibility of altering the existing network of routes, with efficient and optimal changes of the timetabling and vehicle scheduling components. An illustrative example, based on four bus lines in Norrköping, Sweden, is formulized and introduced using three problem instances of 48, 82, and 116 bus trips. The main results exhibit the minimum number of vehicles required using different scenarios of charging stations.