Production flow prototyping subject to imprecise activity specification

Abstract

PurposeThe purpose of this paper is to present research in the area of the applications of knowledge‐based and constraint programming (CP)‐driven methodology in production planning and development of decision‐making software supporting scheduling of multi‐robot in a multi‐product job shop, taking into account imprecise (fuzzy) activity specification, and resource sharing by some industrial processes that simultaneously produce different products.Design/methodology/approachApplications of the knowledge‐based, logic‐algebraic and CP‐driven approach for multi‐robot task allocation problem and generating of fuzzy plan/schedule of production activities for a given period of time.FindingsThis paper illustrates the useful information that can be obtained from fuzzy and crispy‐like schedule describing production activities in a multi‐product job shop.Research limitations/implicationsThe use of knowledge‐based and CP‐driven methodology for production planning in a multi‐product job shop was a very effective method dedicated to solve typical decision problems in the area of project‐driven production flow management applied in make‐to‐order manufacturing.Practical implicationsThe methodology discussed in the paper can be used to design fuzzy Gantt diagrams, which define admissible schedule of production orders for a given period of time.Originality/valueThe paper's contribution covers various issues of decision making while employing the knowledge‐ and CP‐based framework. The proposed approach provides the framework allowing one to take into account distinct (pointed), and imprecise (fuzzy) data, in a unified way and treat it in a unified form of a discrete, constraint satisfaction problem.