Combinatorial Online Optimization in Real Time

Grötschel, Martin; Krumke, Sven O.; Rambau, Jörg; Winter, Thomas Nelson; Zimmermann, Uwe

doi:10.1007/978-3-662-04331-8_33

Cited by 24 publications

(28 citation statements)

References 29 publications

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“…We show that for the related objective "minimize the makespan" (the time the system needs to serve a set of requests) we find a 5/2-competitive algorithm. This is the REPLAN-heuristics already discussed in [10]. This algorithm is, however, not competitive with respect to the minimization of the total unloaded travel distance.…”

Section: Four Real-world Examples and Why They Raise Questionsmentioning

confidence: 99%

“…The methods are ordered by decreasing mathematical strength, that is to say, the first method -if successful -yields the most rigorous analysis of the ones in this section, the last one is merely experimental. (Classical competitive analysis as described in [10] would belong to the very beginning of the section. )…”

Section: Modeling and Evaluation Techniquesmentioning

confidence: 99%

“…For the exposition we consider the Online Traveling Salesman Problem ON-LINETSP on the non-negative real numbers WQ endowed with the Euclidean metric (see [10,Example 4] for the ONLINETSP in general metric spaces). The origin of the salesman is the point 0.…”

Section: Competitive Analysis With Restricted Adversariesmentioning

confidence: 99%

See 2 more Smart Citations

Online Optimization of Complex Transportation Systems

Grötschel

Krumke

Rambau

2001

Online Optimization of Large Scale Systems

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This paper discusses online optimization of real-world transportation systems. We concentrate on transportation problems arising in production and manufacturing processes, in particular in company internal logistics. We describe basic techniques to design online optimization algorithms for such systems, but our main focus is decision support for the planner: which online algorithm is the most appropriate one in a particular setting? We show by means of several examples that traditional methods for the evaluation of online algorithms often do not suffice to judge the strengths and weaknesses of online algorithms. We present modifications of well-known evaluation techniques and some new methods, and we argue that the selection of an online algorithm to be employed in practice should be based on a sound combination of several theoretical and practical evaluation criteria, including simulation. INTRODUCTIONThe strategic planning of complex transportation systems such as public transportation networks, automatically guided vehicles in warehouses, etc. has received a considerable amount of attention in the last decade. Strategic planning is the stage of system design where an object (e.g., a telecommunication network) is designed that will remain static for a certain planning period (the network topology, and edge capacities will not change) such that a few control parameters (e.g., routing and switching) will allow an (almost) optimal handling of all input data (within a certain realistic or predicted range). The system itself is usually not yet operational when this "strategic optimization" takes place. Here, methods of offline optimization apply. The increasing computing power and significant advances in traditional optimization techniques have resulted in substantial savings of resources in this area. Despite many successes of this approach, e.g., for transportation systems, it has turned out that achieving savings also requires an optimized operational control. Such a control involves actions to be executed while a system is running; i.e., input data arise over time, have to be processed, and (irrevocable) decisions have to be made before all input data are known. This means that methods of online optimization have to be employed. In many cases decision making has to satisfy certain real-time requirements: every decision has to be made within strict time limits.In this paper, we survey some new methods (beyond standard competitive analysis) to obtain decision support for the choice of online algorithms in real-world transportation systems. In each case, we are looking for a "good" online control on the basis of online algorithms. The methods discussed are competitive analysis against restricted adversaries (a variant of competitive analysis where the offline adversary is given less power), analysis under A-reasonable load (we compare the

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Section: Four Real-world Examples and Why They Raise Questionsmentioning

confidence: 99%

Section: Modeling and Evaluation Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Online Optimization of Complex Transportation Systems

Grötschel

Krumke

Rambau

2001

Online Optimization of Large Scale Systems

Self Cite

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“…However, this approach is typically applicable only for small, theoretical instances of online problems, whereas for real-world cases, one has to assess the performance experimentally (e.g. through simulation) by comparing it to that of some selected reference, although not necessarily optimal, algorithms [4]. In this paper, exact offline optimization methods are proposed as benchmarks for online taxi dispatching strategies where the objective is to minimize the total passenger waiting time.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Minimum Passenger Waiting Time in Online Taxi Dispatching With Exact Offline Optimization Methods

Maciejewski

2014

AoT

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“…The data is later revealed in a certain order or at specific times [2] and some decisions can be changed further. Decisions that were made earlier may be hard to change and thus they can have critical effects on future decisions.…”

Section: Introductionmentioning

confidence: 99%

Block Erection Sequencing in Shipbuilding With General Lifting and Joining Times

Tokola¹,

Niemi²,

Remes³

2013

j ship prod des

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Manufacturing companies can use it as a part of their information systems (including e.g. material requirements planning (MRP), enterprise resource planning (ERP), and manufacturing execution systems (MES)) for controlling the production system, i.e. for the adjustment of their inventory level, defining the capacity, and even scheduling the starting times of the jobs. A method that can be used to improve the decisions dynamically is online optimisation. Online optimisation repeatedly optimises and adjusts the decisions when there are changes or disturbances in the system. Online optimisation differs from traditional optimisation. First, the data is uncertain but, luckily, the uncertainty might decrease as time goes by. Second, quick decisions are needed but they should be made carefully as the decisions will also affect future decisions. This thesis studies online optimisation from the production planning perspective. In practice, many real factors, such as rescheduling intervals and material deliveries, affect online optimisation. The six publications of the thesis focus, first, on finding different short-term planning problems in manufacturing companies, second, on worker reactive coordination in parallel stations, third, on periodical rescheduling, fourth, on the effect of disturbances on assemble-to-order systems, fifth, on production line rescheduling, and, sixth, on the erection of the hull in shipbuilding in the event of material delays. The methods that are used in the publications of the thesis include survey, Markov models, simulation, mixed-integer-linear programming, and stochastic modelling.The modelling of online solutions to practical short-term planning problems is complex because of the large number of variables, most of which have to be considered. As the variables cannot be aggregated in the short term, a single variation in a variable, even a small one, can have significant consequences for the system. The thesis shows that online optimisation gives an advantage in certain short-term situations, such as in the cases of rush jobs or delays of components.Keywords Optimisation, Production planning Valmistava teollisuus voisi käyttää reaaliaikaista tietoa tietojärjestelmissään (joita ovat mm. materiaalinhallintajärjestelmät, toiminnanohjausjärjestelmät ja tuotannonohjausohjelmistot) ohjaamaan tuotantoa esimerkiksi säätämällä varastotasoja, määrittelemällä kapasiteetit tai jopa aikatauluttamalla töiden aloitusajat. Yksi menetelmä, jolla reaaliaikaista tietoa voi käyttää dynaamisesti päätöksenteossa, on online-optimointi. Onlineoptimoinnissa toistuvasti optimoidaan ja säädetään päätöksiä silloin kun järjestelmään tulee muutoksia. Online-optimointi eroaa perinteisestä optimoinnista monin tavoin. Onlineoptimoinnissa tieto on epävarmaa, mutta epävarmuus voi vähetä kun aika menee eteenpäin. Lisäksi online-optimoinnissa tarvitaan nopeita päätöksiä, jotka täytyy tehdä huolellisesti, koska ne vaikuttavat usein tulevaisuuden päätöksiin. Väitöskirja tutkii online-optimointia tuotannonohjauksen näkökulmasta....

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Combinatorial Online Optimization in Real Time

Cited by 24 publications

References 29 publications

Online Optimization of Complex Transportation Systems

Online Optimization of Complex Transportation Systems

Benchmarking Minimum Passenger Waiting Time in Online Taxi Dispatching With Exact Offline Optimization Methods

Block Erection Sequencing in Shipbuilding With General Lifting and Joining Times

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