Stecke [21] has developed mathematical programming approaches for determining, from a set of part type requirements, the production ratios (part types to be produced next, and their proportions) which maximize overall machine utilizations by balancing machine workloads in a flexible manufacturing system (FMS). These mathematical programming (MP) approaches are aggregate in the sense that they d...

dynamic cellular manufacturing systems,   mixed-integer non-linear programming,   production planning, manufacturing attributes   this paper presents a novel mixed-integer non-linear programming model for the design of a dynamic cellular manufacturing system (dcms) based on production planning (pp) decisions and several manufacturing attributes. such an integrated dcms model with an extensive c...

The solutions or states of optimization problems or simulations are evaluated by using objective functions. The weights for these objective functions usually have to be estimated from experts’ evaluations, which are likely to be qualitative and somewhat subjective. Although such estimation tasks are normally regarded as quite suitable for machine learning, we propose a mathematical programming-...

In this paper, a multi-product single machine scheduling problem with the possibility of producing defected jobs, is considered. We concern rework in the scheduling environment and propose a mixed-integer programming (MIP) model for the problem.  Based on the philosophy of just-in-time production, minimization of the sum of earliness and tardiness costs is taken into account as the objective fu...

    Dynamic cellular manufacturing systems,   Mixed-integer non-linear programming,   Production planning, Manufacturing attributes   This paper presents a novel mixed-integer non-linear programming model for the design of a dynamic cellular manufacturing system (DCMS) based on production planning (PP) decisions and several manufacturing attributes. Such an integrated DCMS model with an extensi...