Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

Abstract

In recent decades, many researchers have studied the cellular manufacturing system with consideration of various issues such as scheduling, production planning, layout, reliability, etc. However, limited research papers have investigated this problem in an uncertain environment. The present paper addresses a stochastic problem in cellular manufacturing systems considering simultaneous multiple routings and subcontracting. In the developed problem, each part can be simultaneously produced in multiple processing routes. It is also assumed that the unsatisfied part demands as a result of limited machine capacity or high manufacturing cost could be outsourced. A two-stage stochastic programming approach is employed to take the uncertainty into consideration and to formulate the problem. The objective function is to minimize the summation of production, subcontracting, material handling, and machine idleness costs. A sample average approximation method is applied as a solution method. Also, for further illustration of the problem, a numerical example is solved and sensitivity analyses are conducted. Finally, through some numerical examples extracted from related literature, the advantages of constructing a stochastic optimization model for the problem are demonstrated.

Mixed integer linear programming model for integrating cell formation, group layout and group scheduling

“…In [12] a stochastic cell formation problem (SCFP) was considered assuming stochastic processing times for parts. Processing times and due dates of parts were considered under different scenarios to obtain the CF that would minimize the expected tardiness cost.…”

“…Equation (11) ensures that the completion time of the first operation of a part at least exceeds its processing time. Equation (12) ensures that the starting time of any operation of a part at least exceeds the completion time of the preceding operation in this part's route, in addition to the intracellular and intercellular (if any) transportation time between the corresponding machines. Equations (13) and (14) ensure that no machine will be occupied by more than one part simultaneously, taking into consideration the sequence dependent set-up times between different parts.…”

An improved algorithm for layout design in cellular manufacturing systems

“…Cellular manufacturing problems are nondeterministic polynomial time (NP) hard problems [42] and by proposing a nonlinear mathematical model for the facility layout makes it more complicated. Hence, the use of a meta-heuristic method is reasonable.…”

A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines

“…They solved the uncertainty using a queuing-based analysis. Ghezavati and Saidi-Mehrabad [35] applied a scenario-based stochastic programming technique to solve the cell formation problem integrated with the group scheduling decision, concurrently. Also, Renna and Ambrico [36] proposed a new approach to design a CMS.…”