Satisfying flexible due dates in fuzzy job shop by means of hybrid evolutionary algorithms

This paper tackles the job shop scheduling problem with fuzzy sets modelling uncertain durations and flexible due dates. The objective is to achieve high-service level by maximising due-date satisfaction, considering two different overall satisfaction measures as objective functions. We show how these functions model different attitudes in the framework of fuzzy multicriteria decision making and we define a measure of solution robustness based on an existing a-posteriori semantics of fuzzy schedules to further assess the quality of the obtained solutions. As solving method, we improve a memetic algorithm from the literature by incorporating a new heuristic mechanism to guide the search through plateaus of the fitness landscape. We assess the performance of the resulting algorithm with an extensive experimental study, including a parametric analysis, and a study of the algorithm’s components and synergy between them. We provide results on a set of existing and new benchmark instances for fuzzy job shop with flexible due dates that show the competitiveness of our method.This research has been supported by the Spanish Government under research grant TIN2016-79190-R

Metaheuristic strategies for scheduling problems with uncertainty

Scheduling problems have formed an important body of research during the last decades. A scheduling problem consists in scheduling a set of jobs {J1, . . . , Jn} on a set of physical resources or machines {M1, . . . , Mm}. Each job Ji is composed of m tasks or operations {θi1, . . . , θim} with processing time pij . At the same time, we usually have constraints that establish that two task belonging to the same job cannot overlap their execution in time and that each task requires the uninterrupted and exclusive use of one of the machines for its whole processing time. Depending on the additional constraints we define, we may obtain different families of problems. The most popular in the literature are the job shop (JSP), the open shop (OSP) and the flow shop (FSP) but there exists also variants of them as the flexible job shop (FJSP) among others. Commonly, the objective function to optimise is the earliest time in which all jobs can be finished or makespan. However many other objectives may be optimised, being the most popular the tardiness, the idleness and the total flow time. In classical scheduling problems all input data are assumed to be well defined and all constraints are assumed to be hard, which is not so common in real-life applications. To reduce the gap between theory and practice, this thesis focuses on solving scheduling problems considering that uncertainty and vagueness. For instance, we shall consider uncertain task durations as well as flexible due-date constraint

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Solving Fuzzy Job-Shop Scheduling Problems with a Multiobjective Optimizer

International audienceIn real-world manufacturing environments, it is common to face a job-shop scheduling problem (JSP) with uncertainty. Among different sources of uncertainty, processing times uncertainty is the most common. In this paper, we investigate the use of a multiobjective genetic algorithm to address JSPs with uncertain durations. Uncertain durations in a JSP are expressed by means of triangular fuzzy numbers (TFNs). Instead of using expected values as in other work, we consider all vertices of the TFN representing the overall completion time. As a consequence, the proposed approach tries to obtain a schedule that optimizes the three component scheduling problems (corresponding to the lowest, most probable, and largest durations) all at the same time. In order to verify the quality of solutions found by the proposed approach, an experimental study was carried out across different benchmark instances. In all experiments, comparisons with previous approaches that are based on a single-objective genetic algorithm were also performed

Robust schedules for tardiness optimization in job shop with interval uncertainty

This paper addresses a variant of the job shop scheduling problem with total tardiness minimization where task durations and due dates are uncertain. This uncertainty is modelled with intervals. Different ranking methods for intervals are considered and embedded into a genetic algorithm. A new robustness measure is proposed to compare the different ranking methods and assess their capacity to predict ‘expected delays’ of jobs. Experimental results show that dealing with uncertainty during the optimization process yields more robust solutions. A sensitivity analysis also shows that the robustness of the solutions given by the solving method increases when the uncertainty grows.This research has been supported by the Spanish Government under research grants PID2019-106263RB-I00 and TIN2017-87600-P