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District Heating Networks (DHN) can provide higher efficiencies and better pollution control compared to local heat generation. However, there are still many areas, which can be improved and optimized in these systems. A DHN is a complex distributed system of different customer substations and components such as boilers, accumulators, pipes, and in many cases also turbines for electricity production. How to schedule the components with the objective of maximizing the profit of heat and electricity production over a finite time horizon is receiving increased attention, and is the problem that has been dealt with in this work. This mixed integer linear programming (MILP) problem has been formulated as a unit commitment problem (UCP), which involves finding the most profitable unit dispatch regarding production costs and heat and electricity sell prices, while simultaneously meeting the predicted district heating demands and satisfying network operational constraints. The heating demands within the optimization time horizon are predicted based on season and weather forecasts. In this work, the district heating plant in Uppsala, Sweden, owned by Vattenfall AB, has been considered as a reference plant for modeling and optimization. The optimization model is formulated in Python using Pyomo modeling language, and solved by Gurobi and GLPK solvers. An hourly- based data of five consecutive days is used as the time horizon. The results demonstrate the fact that with an accurate model of the DHN, it is possible to significantly increase the revenue of the DHN by finding the most economical way to dispatch different production components.

In this paper, we discuss the application of search-based software testing techniques for unit level testing of a real-world telecommunication middleware at Ericsson. Input data for the system under test consists of nested data structures, and includes non-trivial variables such as uninitialized pointers. Our current implementation analyzes the existing test cases to discover how to handle pointers, set global system parameters, and any other setup code that needs to run before the actual test case. Hill climbing (HC) and (1+1) evolutionary algorithm (EA) metaheuristic search algorithms are used to generate input data for branch coverage. We compare HC, (1+1)EA, and random search as a baseline of performance with respect to effectiveness, measured as branch coverage, and efficiency, measured as number of executions needed. Difficulties arising from the specialized execution environment and the adaptations for handling these problems are also discussed.

This paper offers a solution for the optimal EMU train (Electric Multiple Unit) operation with the aim of minimizing the energy consumption. EMU is an electric train with traction motors in more than one carriage. The algorithm is based on dynamic programming and the Hamilton-Jacobi-Bellman equation. To model the train, real data has been used, which was provided by experts from Bombardier Transportation Västerås. To evaluate the model, some experiments have been done on the energy saving in exchange for the increase in the trip time. Moreover a simple accuracy factor is introduced to evaluate the accuracy of the model. The final goal is to use this approach as a base for a driver advisory system, therefore it is important to have the amount of calculations as minimum as possible. The paper also includes the studies done on the calculation time. The solution can be used for driverless trains as well as normal trains. It should be mentioned that this paper is a part of a research which is still in progress and the final model will also be used by Bombardier Transportation Västerås as an evaluation tool for the propulsions systems and trains. Future Energy Profile

Efficient and correct freight train marshalling is vital for high quality carload freight transportations. During marshalling, it is desirable that cars are sorted according to their individual drop-off locations in the outbound freight trains. Furthermore, practical limitations such as non-uniform and limited track lengths and the arrival and departure times of trains need to be considered. This paper presents a novel optimisation method for freight marshalling scheduling under these circumstances. The method is based on an integer programming formulation that is solved using column generation and branch and price. The approach minimises the number of extra shunting operations that have to be performed, and is evaluated on real-world data from the Hallsberg marshalling yard in Sweden. RanPlan

In this paper we present a new testing tool for safety critical applications described in Function Block Diagram (FBD) language aimed to support both a model and a search-based approach. Many benefits emerge from this tool, including the ability to automatically generate test suites from an FBD program in order to comply to quality requirements such as component testing and specific coverage measurements. Search-based testing methods are used to generate test data based on executable code rather than the FBD program, alleviating any problems that may arise from the ambiguities that occur while creating FBD programs. Test cases generated by both approaches are executed and used as a way of cross validation. In the current work, we describe the architecture of the tool, its workflow process, and a case study in which the tool has been applied in a real industrial setting to test a train control management system. ATAC - Advanced Test Automation for Complex Software-Intensive System (ITEA2/Vinnova) ITS-EASY Post Graduate School for Embedded Software and Systems

Sammanfattning Rapporten innehåller kortfattade slutsatser och resultat från en studie genomförd i projektet RANPLAN, som har utförts av SICS Swedish ICT AB på uppdrag av Trafikverket under åren 2010-2013. Fokus är på Hallsbergs rangerbangård, men resultaten är tillämpbara även på andra rangerbangårdar med vall. Datorkörningar visar att blanddragen kan öka kapaciteten på rangerbangårdar väsentligt, mätt i antalet samtidiga tåg som kan hanteras, till en kostnad av en ökad mängd vagnsrörelser. I en jämförande datorstudie av simulering och optimering framgick också att de optimala planerna var betydligt effektivare, mätt i antalet vagnsrörelser, än de simulerade planerna. Resultaten pekar tydligt på att datorstödd optimering av planeringsprocessen för rangerbangårdar både är praktiskt möjligt och kan ge stora effektivitetsvinster.

We consider the planning of railway freight classification at hump yards, where the problem involves the formation of departing freight train blocks from arriving trains subject to scheduling and capacity constraints. The hump yard layout considered consists of arrival tracks of sufficient length at an arrival yard, a hump, classification tracks of non-uniform and possibly non-sufficient length at a classification yard, and departure tracks of sufficient length. To increase yard capacity, freight cars arriving early can be stored temporarily on specific mixed-usage tracks. The entire hump yard planning process is covered in this paper, and heuristics for arrival and departure track assignment, as well as hump scheduling, have been included to provide the neccessary input data. However, the central problem considered is the classification track allocation problem. This problem has previously been modeled using direct mixed integer programming models, but this approach did not yield lower bounds of sufficient quality to prove optimality. Later attempts focused on a column generation approach based on branch-and-price that could solve problem instances of industrial size. Building upon the column generation approach we introduce a direct arc-based integer programming model, where the arcs are precedence relations between blocks on the same classification track. Further, the most promising models are adapted for rolling-horizon planning. We evaluate the methods on historical data from the Hallsberg shunting yard in Sweden. The results show that the new arc-based model performs as well as the column generation approach. It returns an optimal schedule within the execution time limit for all instances but from one, and executes as fast as the column generation approach. Further, the short execution times of the column generation approach and the arc-indexed model make them suitable for rolling-horizon planning, while the direct mixed integer program proved to be too slow for this. Extended analysis of the results shows that mixing was only required if the maximum number of concurrent trains on the classification yard exceeds 29 (there are 32 available tracks), and that after this point the number of extra car roll-ins increases heavily.

Planning the operational procedures in a railway marshalling yard is a complex problem. When a train arrives at a marshalling yard, it is uncoupled on an arrival yard and then its cars are rolled to a classification yard. All cars should eventually be rolled to the classification track that has been assigned to the train they’re supposed to depart with. However, there is normally not enough capacity to compound all trains at once. In Sweden, cars arriving before a track has been assigned to their train can be stored on separate tracks called mixing tracks. All cars on mixing tracks will be pulled back to the arrival yard, and then rolled to the classification yard again to allow for reclassification. Today all procedures are planned by experienced dispatchers, but there are no documented strategies or guidelines for efficient manual planning. The aim of this paper is to examine operational planning strategies that could help dispatchers find a feasible marshalling schedule that minimizes unnecessary mixing. In order to achieve this goal, two different online planning strategies have been tested using deterministic and stochastic simulation. The Hallsberg marshalling yard was used as a case study, and was simulated for the time period between December 2010 and May 2011. The first tested strategy simply assigns tracks to trains on a first come-first served basis, while the second strategy uses time limits to determine when tracks should be assigned to departing trains. The online planning algorithms have been compared with an offline optimized track allocation. The results from both the deterministic and the stochastic simulation show that the optimized allocation is better than all online strategies and that the second strategy with a time limit of 32 hours is the best online method. This work has been done at SICS and it is also a part of the dissertation submitted to KTH, Royal Institute of Technology division of traffic and logistics, railway group, to be granted a master of science degree in "Transport Systems. RanPlan

We consider planning of freight train classification at hump yards using integer programming. The problem involves the formation of departing freight trains from arriving trains subject to scheduling and capacity constraints. To increase yard capacity, we allow the temporary storage of early freight cars on specific mixed-usage tracks. The problem has previously been modeled using a direct integer programming model, but this approach did not yield lower bounds of sufficient quality to prove optimality. In this paper, we formulate a new extended integer programming model and design a column generation approach based on branch-and-price to solve problem instances of industrial size. We evaluate the method on historical data from the Hallsberg hump yard in Sweden, and compare the results with previous approaches. The new method managed to find optimal solutions in all of the 192 problem instances tried. Furthermore, no instance took more than 13 minutes to solve to optimality using fairly standard computer hardware., OpenAccess Series in Informatics (OASIcs), 25, ISSN:2190-6807, 12th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2012), ISBN:978-3-939897-45-3

I denna rapport presenteras de modeller och resultat som projektet/RANPLAN-beräkningsstöd för planering och resursallokering/ producerat. RANPLAN finansierades av Trafikverkets FUD-program (F 09-11546/AL50) och pågick från januari 2010 till december 2011. Under projektets gång har ett flertal heuristiska algoritmer och optimeringmodeller tagits fram som skulle kunna förbättra och förenkla planeringen vid rangerbangårdar. En demonstrator baserad på historiska data visar att metoderna är så pass skalbara och effektiva att de är attraktiva för kommersiell implementering. Vidare presenteras förslag på lämpliga effektivitets- och kvalitetsmått för rangering. Report is written in Swedish. RanPlan

We consider the process of forming outbound trains from cars of inbound trains at rail-freight hump yards. Given the arrival and departure times as well as the composition of the trains, we study the problem of allocating classification tracks to outbound trains such that every outbound train can be built on a separate classification track. We observe that the core problem can be formulated as a special list coloring problem in interval graphs, which is known to be NP-complete. We focus on an extension where individual cars of different trains can temporarily be stored on a special subset of the tracks. This problem induces several new variants of the list-coloring problem, in which the given intervals can be shortened by cutting off a prefix of the interval. We show that in case of uniform and sufficient track lengths, the corresponding coloring problem can be solved in polynomial time, if the goal is to minimize the total cost associated with cutting off prefixes of the intervals. Based on these results, we devise two heuristics as well as an integer program to tackle the problem. As a case study, we consider a real-world problem instance from the Hallsberg Rangerbangard hump yard in Sweden. Planning over horizons of seven days, we obtain feasible solutions from the integer program in all scenarios, and from the heuristics in most scenarios., OpenAccess Series in Informatics (OASIcs), 20, ISSN:2190-6807, 11th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ISBN:978-3-939897-33-0

The problem of finding efficient maintenance and inspection schemes in the case of components with a stochastic life time is studied and a mixed integer programming solution is proposed. The problem is compared with the two simpler problems of which the studied problem is a generalisation: The opportunistic replacement problem, assuming components with a deterministic life time and The opportunistic replacement problem for components with a stochastic life time, for maintenance schemes without inspections. DUST

In rail freight operation, freight cars need to be separated and reformed into new trains at hump yards. The classification procedure is complex and time consuming, and hump yards often constitute bottlenecks in the rail freight network. One of the problems is that planning for the allocation of tracks at hump yards is difficult, given that the planner has limited resources (tracks, shunting engines, etc.) and needs to foresee the future capacity requirements when planning for the current inbound trains. In this paper, we consider the problem of allocating classification tracks in a rail freight hump yard for arriving and departing trains. Arrival and departure times are predetermined and may originate in timetables or estimated arrival and departure times (in case of disturbances in the rail system). The core problem can be formulated as a special list coloring problem. We focus on an extension where individual cars can temporarily be stored on a special subset of the tracks. We model the problem using mixed integer programming, and also propose several heuristics that can quickly give feasible track allocations. As a case study, we consider a real-world problem instance from the Hallsberg Rangerbangård hump yard in Sweden. Planning over horizons over two to four days, we obtain feasible solutions from both the exact and heuristic approaches that allow all outgoing trains to leave on time. This work was supported by Trafikverket under FUD grant F 09-11546/AL50 and by the Swiss National Science Foundation (SNF) under grant 200021-125033/1. RanPlan

We describe the implementation and deployment of a software decision support tool for the maintenance planning of gas turbines. The tool is used to plan the maintenance for turbines manufactured and maintained by Siemens Industrial Turbomachinery AB (SIT AB) with the goal to reduce the direct maintenance costs and the often very costly production losses during maintenance downtime. The optimization problem is formally defined, and we argue that feasibility in it is NP-complete. We outline a heuristic algorithm that can quickly solve the problem for practical purposes, and validate the approach on a real-world scenario based on an oil production facility. We also compare the performance of our algorithm with results from using mixed integer linear programming, and discuss the deployment of the application. The experimental results indicate that downtime reductions up to 65% can be achieved, compared to traditional preventive maintenance. In addition, using our tool is expected to improve availability with up to 1% and reduce the number of planned maintenance days with 12%. Compared to a mixed integer programming approach, our algorithm not optimal, but is orders of magnitude faster and produces results which are useful in practice. Our test results and SIT AB’s estimates based on operational use both indicate that significant savings can be achieved by using our software tool, compared to maintenance plans with fixed intervals. Proceedings of the Twenty-First Conference on Innovative Applications of Artificial Intelligence (IAAI'09) published by IEEE Computer Society Camp

A combinatorial optimization problem is an optimization problem where the number of possible solutions are finite and grow combinatorially with the problem size. Combinatorial problems exist everywhere in industrial systems. This thesis focuses on solving three such problems which arise within two different areas where industrial computer systems are often used. Within embedded systems and real-time systems, we investigate the problems of allocating stack memory for an system where a shared stacks may be used, and of estimating the highest response time of a task in a system of industrial complexity. We propose a number of different algorithms to compute safe upper bounds on run-time stack usage whenever the system supports stack sharing. The algorithms have in common that they can exploit commonly-available information regarding timing behaviour of the tasks in the system. Given upper bounds on the individual stack usage of the tasks, it is possible to estimate the worst-case stack behaviour by analysing the possible and impossible preemption patterns. Using relations on offset and precedences, we form a preemption graph, which is further analysed to find safe upper-bounds on the maximal preemptions chain in the system. For the special case where all tasks exist in a single static schedule and share a single stack, we propose a polynomial algorithm to solve the problem. For generalizations of this problem, we propose an exact branch-and-bound algorithm for smaller problems and a polynomial heuristic algorithm for cases where the branch-and-bound algorithm fails to find a solution in reasonable time. All algorithms are evaluated in comprehensive experimental studies. The polynomial algorithm is implemented and shipped in the developer tool set for a commercial real-time operating system, Rubus OS. The second problem we study in the thesis is how to estimate the highest response time of a specified task in a complex industrial real-time system. The response-time analysis is done using a best-effort approach, where a detailed model of the system is simulated on input constructed using a local search procedure. In an evaluation on three different systems we can see that the new algorithm were able to produce higher response times much faster than what has previously been possible. Since the analysis is based on simulation and measurement, the results are not safe in the sense that they are always higher or equal to the true response time of the system. The value of the method lies instead in that it makes it possible to analyse complex industrial systems which cannot be analysed accurately using existing safe approaches. The third problem is in the area of maintenance planning, and focus on how to dynamically plan maintenance for industrial systems. Within this area we have focused on industrial gas turbines and rail vehicles. We have developed algorithms and a planning tool which can be used to plan maintenance for gas turbines and other stationary machinery. In such problems, it is often the case that performing several maintenance actions at the same time is beneficial, since many of these jobs can be done in parallel, which reduces the total downtime of the unit. The core of the problem is therefore how to (or how not to) group maintenance activities so that a composite cost due to spare parts, labor and loss of production due to downtime is minimized. We allow each machine to have individual schedules for each component in the system. For rail vehicles, we have evaluated the effect of replanning maintenance in the case where the component maintenance deadline is set to reflect a maximum risk of breakdown in a Gaussian failure distribution. In such a model, we show by simulation that replanning of maintenance can reduce the number of maintenance stops when the variance and expected value of the distribution are increased. For the gas turbine maintenance planning problem, we have evaluated the planning software on a real-world scenario from the oil and gas industry and compared it to the solutions obtained from a commercial integer programming solver. It is estimated that the availability increase from using our planning software is between 0.5 to 1.0 %, which is substantial considering that availability is currently already at 97-98 %.

This paper introduces two Mixed Integer Linear Programming (MILP) models for railway traffic planning using a cumulative scheduling constraint and associated pre-processing filters. We compare standard solver performance for these models on three sets of problems from the railway domain and for two of them, where tasks have unitary resource consumption, we also compare them with two more conventional models. In the experiments, the solver performance of one of the cumulative models is clearly the best and is also shown to scale very well for a large scale practical railway scheduling problem. The Proceedings of the 9th Workshop on Algorithmic Methods and Models for Optimization of Railways (ATMOS) published by Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany DDTP

Rapporten beskriver ett antal provkörningar av TIMM-demonstratorn utförda för att undersöka hur antalet besök i verkstaden varierar givet en förväntad underhållsvinst från CBM. TIMM-demonstratorn är en programvara framtagen för att under drift planera in underhållsbesök för ett antal simulerade fordon i spårtrafik, där underhållsbehovet styrs av dynamiskt varierande mätvärden på fordonen. Report is written in Swedish.

This paper introduces two MILP models for the cumulative scheduling constraint and associated pre-processing filters. We compare standard solver performance for these models on three sets of problems and for two of them, where tasks have unitary resource consumption, we also compare them with two models based on a geometric placement constraint. In the experiments, the solver performance of one of the cumulative models, is clearly the best and is also shown to scale very well for a large scale industrial transportation scheduling problem.

Denna rapport beskriver och motiverar leveranstågplanens roll och utformning i relation till den dynamiska tågplanen. Med utgångspunkt i formen för de krav som specificerats i den framtida tåglägesansökan föreslår vi en form för leveranstågplanen vars syfte är att både kunders och trafikutövares krav på förutsägbarhet och samtidigt ger utrymme för effektiv operativ styrning och kortare ledtider vid förändrad efterfrågan. I sin första version är detta dokument främst tänkt som ideskiss för diskussion och kritisk analys bland kapacitetsfördelningsprocessen intressenter. Report is written in Swedish.

TIME står för Train Information Management Environment. TIME är ett tänkt övergripande informationssystem för Järnväg. Viktiga aspekter hos TIME är utformningen av en plattform för kommunikation mellan aktörerna i järnvägstransportbranschen och information mellan fordon och system med en fast plats. TIME gäller alla delar i ett informationssystem, hur data produceras och processas, infrastruktur för information och principer för datalagring och informationsutbyte samt funktioner och tjänster baserade på denna information. TIME avser t.ex. att medverka till att samverkan mellan järnvägstransportbranschens aktörer fungerar bra, dessa aktörers egen verksamhet blir effektiv och att kunder till järnvägen och andra som beror av järnvägen erhåller rätt information. Report is written in Swedish.

Rapporten beskriver möjliga ansatser för att lösa det abstraherade tidtabellproblemet som bl.a. diskuteras i rapporten "Leveranstågplan: Specifikation och åtagande" (DDTP Arbetsdokument SICS-DDTP-002). Till grund för de olika ansatserna ligger en modell med avgångstider och hålltider (dvs. väntetider och i viss mån traverseringstider) som tillåts variera inom vissa tidsintervall. Huvudidén är att arbeta med förenklade kapacitetsvillkor på bana och bangård, för att på ett effektivt sätt kunna beräkna tidtabeller på en nivå som tillåter anpassning av tidtabellen till det gällande transportbehovet och den rådande trafiksituationen. Report is written in Swedish.

In this work we present a novel method to automate the computation of global constraints cost for local search. The method is based on the representation of a global constraints as graph properties on a binary constraint network. This formulation simplifies the implementation of global constraints in local search, and provides a cost that can be readily compared to one obtained for subproblems using binary constraints exclusively. The cost obtained can be efficiently updated during the search using incremental methods. The representation of a global constraint as outlined above can also be used for generation of suitable neighborhoods for the constraint. This is done using simple repair functions applied on the elementary constraints in the global constraint graph. We show the usability of our approach by presenting formulations of global constraints in non-overlapping and cumulative scheduling.

This master of science thesis describes a new design and its implementation for an Oz compiler. The project is based on the existing Oz compiler. The new compiler is designed more modular, with separate software components that can be replaced and modified locally. A prototype has been implemented, but further development is necessary. We give an overview of the language Oz, its features and the underlying calculus. The features of Oz regarding object orientation, functional programming, logic and constraint programming are also discussed. The liveness analysis and register allocation problems in general and regarding Oz specific compilers are analyzed, together with current and future optimizations suitable for the Mozart platform. The design of the new compiler and information about the old one is presented, and future work regarding the compiler, optimizations, and analysis phases is discussed. Appendices describing the interfaces between the phases of the compiler is included, together with documentation regarding the internal code formats used.

The constraint satisfaction problem and its derivate, the propositional satisfiability problem (SAT), are fundamental problems in computing theory and mathematical logic. SAT was the first proved NP-complete problem, and although complete algorithms have been dominating the constraint satisfaction field, incomplete approaches based on local search has been successful the last ten years. In this report we give a general framework for constraint satisfaction using local search as well as an different techniques to improve this basic local search framework. We also give an overview of algorithms for problems of constraint satisfaction and optimization using heuristics, and discuss hybrid methods that combine complete methods for constraint satisfaction with local search techniques.