Minimal Perturbance in Dynamic Scheduling.
ABSTRACT This paper describes an algorithm, unimodular probing, conceived to optimally reconfigure schedules in response to a changing environ- ment. In the problems studied, resources may become unavailable, and scheduled activities may change. The total shift in the start and end times of activities should be kept to a minimum. This require- ment is captured in terms of a linear optimization function over lin- ear constraints. However, the disjunctive nature of many scheduling problems impedes traditional mathematical programming approaches. The unimodular probing algorithm interleaves constraint program- ming and linear programming. The linear programming solver is ap- plied to a dynamically controlled subset of the problem constraints, to guarantee that the values returned are discrete. Using a r epair strat- egy, these values are naturally integrated into the constra int program- ming search. We explore why the algorithm is effective and discuss its applicability to a wider class of problems. It appears th at other problems comprising disjunctive constraints and a linear o ptimiza- tion function may be suited to the algorithm. Unimodular probing outperforms alternative algorithms on randomly generated bench- marks, and on a major airline application.
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ABSTRACT: It is often the case that after a scheduling problem has been solved some small changes occur that make the solution of the original problem not valid. Solving the new problem from scratch can result in a schedule that is very different from the original schedule. In applications such as a university course timetable or flight scheduling, one would be interested in a solution that requires minimal changes for the users. The present paper considers the minimal perturbation problem. It is motivated by scenarios in which a Constraint Satisfaction Problem (CSP) is subject to changes. In particular, the case where some of the constraints are changed after a solution was obtained. The goal is to find a solution to the changed problem that is as similar as possible (e.g. includes minimal perturbations) to the previous solution. Previous studies proposed a formal model for this problem (Barták et al. 2004), a best first search algorithm (Ross et al. 2000), complexity bounds (Hebrard et al. 2005), and branch and bound based algorithms (Barták et al. 2004; Hebrard et al. 2005). The present paper proposes a new approach for solving the minimal perturbation problem. The proposed method interleaves constraint optimization and constraint satisfaction techniques. Our experimental results demonstrate the advantage of the proposed algorithm over former algorithms. Experiments were performed both on random CSPs and on random instances of the Meeting Scheduling Problem.Constraints 01/2011; 16:228-249. · 0.74 Impact Factor
Conference Paper: A Survey of Researches on Uncertain Project Scheduling Problems.[Show abstract] [Hide abstract]
ABSTRACT: This paper summarizes the recent researches on uncertain project scheduling problems. The literatures of the proactive project scheduling problems are presented firstly. Then we review the achievements of the reactive project scheduling problems. According to the methods of dealing with uncertain information, the developments of the stochastic project scheduling problems and the fuzzy project scheduling problems are described respectively. Ultimately, the paper is sumed up and the directions for further research in this field are indicated.The International Conference on E-Business and E-Government, ICEE 2010, 7-9 May 2010, Guangzhou, China, Proceedings; 01/2010
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ABSTRACT: Heuristic performance has been mainly measured by effectiveness (near optimality) and efficiency (computational complexity). More recently researchers have begun the difficult task of evaluating heuristic stability, or sensitivity, to perturbations in the problem specifications. Various stability measures have been proposed. Here we consider how Spearman's footrule, a measure of permutation disarray, may shed some further light on this, not as yet well understood, problem.Computers & Operations Research 01/2007; · 1.91 Impact Factor