A heuristic approach to schedule reoptimization in the context of interactive optimization

Abstract

Optimization models used in planning and scheduling systems are not exempt from inaccuracies. These optimization systems often require an expert to assess solutions and to adjust them before taking decisions. However, adjusting a solution computed by an optimization procedure is difficult, especially because of the cascading effect. A small modification in a candidate solution may require to modify a large part of the solution. This obstacle to the adjustment of a solution can be overcome by interactive reoptimization. In this paper we analyze the impact of the cascading effect on a shift-scheduling problem and propose an efficient heuristic approach for reoptimizing solutions. The proposed approach is a local-search metaheuristic that has been adapted to the reoptimization. This approach is evaluated on a set of problem instances on which additional preferences are generated to simulate desired adjustments of a decision maker. Experimental results indicate that, even with a small perturbation, the cascading effect is manifest and cannot be efficiently tackled by applying recovery actions. Moreover, results show that the proposed reoptimization method provides significant cost gains within a short time while keeping a level of simplicity and modularity adequate for an implementation in a decision support system.

A benchmark set extension and comparative study for the HyFlex framework

“…Due to space constraints median results for the original HyFlex domains are omitted. 15 In addition we report the following performance measures for each domain: Let C x,i the set of results obtained by a method x ∈ X on an instance i ∈ P , where X and P are the sets of methods and instances considered, respectively.…”

“…An Evolutionary Programming Hyper-heuristic [15] with co-evolution. 6 This population-based approach ended up 5 th in the CHeSC competition.…”

Holarchic structures for decentralized deep learning: a performance analysis

“…In dynamic environments, rescheduling, or reoptimization [29] becomes a critical function for dealing with unpredictable disturbances. It usually raises the additional constraints of minimizing reoptimization time as well as the distance between the initial optimization solution and the one of reoptimization, e.g.…”

“…It usually raises the additional constraints of minimizing reoptimization time as well as the distance between the initial optimization solution and the one of reoptimization, e.g. dynamic rescheduling in manufacturing systems [26] or shift rescheduling [29]. For instance, a two-level holarchy is earlier adopted to combine global optimization scheduling with fast rescheduling when dynamic system disturbances occur [29].…”

“…dynamic rescheduling in manufacturing systems [26] or shift rescheduling [29]. For instance, a two-level holarchy is earlier adopted to combine global optimization scheduling with fast rescheduling when dynamic system disturbances occur [29]. In contrast, the holarchic schemes of this paper do not undermine the learning performance, while adding resilience by localizing the learning process without reinitiating it.…”