Aggregate production planning (APP) deals with the simultaneous determination of plant’s production, inventory and vocation levels over a finite time horizon. The aim of aggregate production planning is to finalize overall output levels in the near to medium future in uncertain demands.
In this paper, three different cases of aggregate production planning problems are presented and optimized by using Passing Vehicle Search (PVS) algorithm. To overcome the poor convergence of PVS algorithm, its performance is experimented by combining it with conjugate gradient (CG) optimization method. PVS algorithm is efficient is finding global optimum solutions whereas CG method is an effective method to search local optimum solutions.
Proposed memetic PVS combines the global search capabilities of basic PVS and efficient local search of CG method to address challenging problems of aggregate production planning. The experimental results demonstrates that combined PVS-CG is more efficient compared to basic PVS algorithm and CG method in solving APP problems.
