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

Benchmarks for fuzzy job shop problems

The fuzzy job shop scheduling problem with makespan minimisation is a problem with a significant presence in the scientific literature. However, a common meaningful comparison base is missing for such problem. This work intends to fill the gap in this domain by reviewing existing benchmarks as well as proposing new benchmark problems. First, we shall survey the existing test beds for the fuzzy job shop, analysing whether they are sufficiently varied and, most importantly, whether there is room for improvement on these instances - an essential requirement if the instances are to be useful for the scientific community in order to compare and develop new solving strategies. In the light of this analysis, we shall propose a new family of more challenging benchmark problems and provide lower bounds for the expected makespan of each instance as well as reference makespan values obtained with a memetic algorithm from the literature. The resulting benchmark will be made available so as to facilitate experiment reproducibility and encourage research competition

Neuroprotective Effect of Lucium chinense Fruit on Trimethyltin-Induced Learning and Memory Deficits in the Rats

In order to the neuroprotective effect of Lycium chinense fruit (LCF), the present study examined the effects of Lycium chinense fruit on learning and memory in Morris water maze task and the choline acetyltransferase (ChAT) and cyclic adenosine monophosphate (cAMP) of rats with trimethyltin (TMT)-induced neuronal and cognitive impairments. The rats were randomly divided into the following groups: naïve rat (Normal), TMT injection+saline administered rat (control) and TMT injection+LCF administered rat (LCF). Rats were administered with saline or LCF (100 mg/kg, p.o.) daily for 2 weeks, followed by their training to the tasks. In the water maze test, the animals were trained to find a platform in a fixed position during 6d and then received 60s probe trial on the 7th day following removal of platform from the pool. Rats with TMT injection showed impaired learning and memory of the tasks and treatment with LCF (p<0.01) produced a significant improvement in escape latency to find the platform in the Morris water maze at the 2nd day. Consistent with behavioral data, treatment with LCF also slightly reduced the loss of ChAT and cAMP in the hippocampus compared to the control group. These results demonstrated that LCF has a protective effect against TMT-induced neuronal and cognitive impairments. The present study suggests that LCF might be useful in the treatment of TMT-induced learning and memory deficit

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