Abstract
: Received 5 September 2012 Received in revised format 25 September 2012 Accepted September 27 2012 Available online 27 September 2012 In hierarchical production planning system, Aggregate Production Planning (APP) falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multi-period aggregate production planning (APP) with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem) and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.
Highlights
Aggregate production planning is associated with the determination of inventory, production and work force levels to consider fluctuating demand needs over a planning horizon, which ranges from six months up to a year
The proposed Multi-Objective Genetic Algorithm (MOGA) approach focuses on the multi-periods and multiproducts problems in an aggregate production planning (APP) decision making process
This work presents a novel interactive MOGA approach for solving multi product and multi period APP decision problems with the forecast demand, related operating costs, and capacity
Summary
Aggregate production planning is associated with the determination of inventory, production and work force levels to consider fluctuating demand needs over a planning horizon, which ranges from six months up to a year. With the consideration of NP-hard problems Moghaddam & Safaei (2006) presented a genetic algorithm (GA) for solving a generalized model of single-item resource constrained aggregate production planning (APP) with linear cost functions. In their paper, they developed a new genetic algorithm with effective operators and integer representation.
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