A probabilistic approach to the stochastic job-shop scheduling problem
Date
2018
Authors
Shoval, S.
Efatmaneshnik, M.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Procedia Manufacturing, 2018, vol.21, pp.533-540
Statement of Responsibility
Conference Name
15th Global Conference on Sustainable Manufacturing, GCSM 2017 (25 Sep 2017 - 27 Sep 2017 : Haifa, Israel)
Abstract
Uncertainty exists in almost any manufacturing process, and its effect may be detrimental to the manufacturing outcomes. In the Stochastic Job Shop Scheduling Problem (SJSSP), some of the process parameters are random variables, in particular the processing time. This paper considers another facet of the SJSSP, which is the probability for success (or failure) of a manufacturing job and its effect on other jobs. The paper presents a mathematical model for determining the expected manufacturing cost, and proposes heuristics for reducing that cost. The fundamental model is based on a single resource (e.g. a single machine) and a set of manufacturing jobs, each characterized by a cost and a probabilistic distribution for success. A failure causes either a re-work of the failed job, or restarting the entire process from the first job. Since the problem is NH Hard, a set of heuristics for scheduling the jobs is proposed, and simulation results validate these heuristics
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2018 The author(s), published by Elsevier Under a Creative Commons License (https://creativecommons.org/licenses/by-nc-nd/4.0/)