Dynamic lot sizing and tool management in automated manufacturing systems

The overall aim of this study is to show that there is a critical interface between the lot sizing and tool management decisions, and these two problems cannot be viewed in isolation. We propose five alternative algorithms to solve lot sizing, tool allocation and machining conditions optimization problems simultaneously. The first algorithm is an exact algorithm which finds the global optimum solution, and the others are heuristics equipped with a look-ahead mechanism to guarantee at least local optimality. The computational results indicate that the amount of improvement is statistically significant for a set of randomly generated problems. The magnitude of cost savings is dependent on the system parameters. In most of the studies on tool management, lot sizes are taken as a predetermined input while deciding on tool allocations and machining parameters. This might create empty feasible solution spaces and otherwise unnecessarily limit the number of alternatives possible for the tool management problem. In this study, we consider the integration of lot sizing and tool management problems ot minimize total production cost for multiple periods under dynamic demand. By integrating these decisions we not only improve the overall solution, but also prevent any infeasibility that might occur for the tool management problem due to decisions made at the lot sizing level. © 2002 Elsevier Science Ltd. All rights reserved

Integrated lot sizing and tool management in automated manufacturing systems

We propose a new algorithm to solve lot sizing, tool allocation and machining conditions optimization problems simultaneously to minimize total production cost in a CNC environment. Most of the existing lot sizing and tool management methods solve these problems independently using a two-level optimization approach. Thus, we not only improve the overall solution by exploiting the interactions, but also prevent any infeasibility that might occur for the tool management problem due to decisions made at the lot sizing level. We showed that in a set of randomly generated problems 22.5% of solutions found by the two-level approach were infeasible and we improved the solution on the average by 6.79% for the remaining cases with an average computation time of 63.4 seconds

A new dominance rule to minimize total weighted tardiness with unequal release dates

We present a new dominance rule by considering the time-dependent orderings between each pair of jobs for the single machine total weighted tardiness problem with release dates. The proposed dominance rule provides a sufficient condition for local optimality. Therefore, if any sequence violates the dominance rule then switching the violating jobs either lowers the total weighted tardiness or leaves it unchanged. We introduce an algorithm based on the dominance rule, which is compared to a number of competing heuristics for a set of randomly generated problems. Our computational results indicate that the proposed algorithm dominates the competing algorithms in all runs, therefore it can improve the upper bounding scheme in any enumerative algorithm. The proposed time-dependent local dominance rule is also implemented in two local search algorithms to guide these algorithms to the areas that will most likely contain the good solutions. © 2001 Elsevier Science B.V. All rights reserved