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

Evidence for a narrow dip structure at 1.9 GeV/c2^2 in 3π+3π−3\pi^+ 3\pi^- diffractive photoproduction

A narrow dip structure has been observed at 1.9 GeV/c$^2$ in a study of diffractive photoproduction of the $~3\pi^+3\pi^-$ final state performed by the Fermilab experiment E687.Comment: The data of Figure 6 can be obtained by downloading the raw data file e687_6pi.txt. v5 (2nov2018): added Fig. 7, the 6 pion energy distribution as requested by a reade

Belief logic programming: Uncertainty reasoning with correlation of evidence

Abstract. Belief Logic Programming (BLP) is a novel form of quantitative logic programming in the presence of uncertain and inconsistent information, which was designed to be able to combine and correlate evidence obtained from non-independent information sources. BLP has non-monotonic semantics based on the concepts of belief combination functions and is inspired by Dempster-Shafer theory of evidence. Most importantly, unlike the previous efforts to integrate uncertainty and logic programming, BLP can correlate structural information contained in rules and provides more accurate certainty estimates. The results are illustrated via simple, yet realistic examples of rule-based Web service integration.