1,727 research outputs found

    Reinforcement learning based local search for grouping problems: A case study on graph coloring

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    Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms

    Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes.

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    AbstractThe allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II

    Heuristics for the dynamic facility layout problem with unequal area departments

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    The facility layout problem (FLP) is a well researched problem of finding positions of departments on a plant floor such that departments do not overlap and some objective(s) is (are) optimized. In this dissertation, the FLP with unequal area rectangular shaped departments is considered, when material flows between departments change during the planning horizon. This problem is known as the dynamic FLP. The change in material flows between pairs of departments in consecutive periods may require rearrangements of departments during the planning horizon in order to keep material handling costs low. The objective of our problem is to minimize the sum of the material handling and rearrangement costs. Because of the combinatorial structure of the problem, only small sized problems can be solved in reasonable time using exact techniques. As a result, construction and improvement heuristics are developed for the proposed problem. The construction algorithms are boundary search heuristics as well as a dual simplex method, and the improvement heuristics are tabu search and memetic heuristics with boundary search and dual simplex (linear programming model) techniques. The heuristics were tested on a generated data set as well as some instances from the literature. In summary, the memetic heuristic with the boundary search technique out-performed the other techniques with respect to solution quality

    Robustness - a challenge also for the 21st century: A review of robustness phenomena in technical, biological and social systems as well as robust approaches in engineering, computer science, operations research and decision aiding

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    Notions on robustness exist in many facets. They come from different disciplines and reflect different worldviews. Consequently, they contradict each other very often, which makes the term less applicable in a general context. Robustness approaches are often limited to specific problems for which they have been developed. This means, notions and definitions might reveal to be wrong if put into another domain of validity, i.e. context. A definition might be correct in a specific context but need not hold in another. Therefore, in order to be able to speak of robustness we need to specify the domain of validity, i.e. system, property and uncertainty of interest. As proofed by Ho et al. in an optimization context with finite and discrete domains, without prior knowledge about the problem there exists no solution what so ever which is more robust than any other. Similar to the results of the No Free Lunch Theorems of Optimization (NLFTs) we have to exploit the problem structure in order to make a solution more robust. This optimization problem is directly linked to a robustness/fragility tradeoff which has been observed in many contexts, e.g. 'robust, yet fragile' property of HOT (Highly Optimized Tolerance) systems. Another issue is that robustness is tightly bounded to other phenomena like complexity for which themselves exist no clear definition or theoretical framework. Consequently, this review rather tries to find common aspects within many different approaches and phenomena than to build a general theorem for robustness, which anyhow might not exist because complex phenomena often need to be described from a pluralistic view to address as many aspects of a phenomenon as possible. First, many different robustness problems have been reviewed from many different disciplines. Second, different common aspects will be discussed, in particular the relationship of functional and structural properties. This paper argues that robustness phenomena are also a challenge for the 21st century. It is a useful quality of a model or system in terms of the 'maintenance of some desired system characteristics despite fluctuations in the behaviour of its component parts or its environment' (s. [Carlson and Doyle, 2002], p. 2). We define robustness phenomena as solution with balanced tradeoffs and robust design principles and robustness measures as means to balance tradeoffs. --
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