Abstract
Deterministic inventory model, the economic order quantity (EOQ), reveals that carrying inventory or ordering frequency follows a relation of tradeoff. For probabilistic demand, the tradeoff surface among annual order, expected inventory and shortage are useful because they quantify what the firm must pay in terms of ordering workload and inventory investment to meet the customer service desired. Based on a triobjective inventory model, this paper employs the successive approximation to obtain efficient control policies outlining tradeoffs among conflicting objectives. The nondominated solutions obtained by successive approximation are further used to plot a 3D scatterplot for exploring the relationships between objectives. Visualization of the tradeoffs displayed by the scatterplots justifies the computation effort done in the experiment, although several iterations needed to reach a nondominated solution make the solution procedure lengthy and tedious. Information elicited from the inverse relationships may help managers make deliberate inventory decisions. For the future work, developing an efficient and effective solution procedure for tradeoff analysis in multiobjective inventory management seems imperative.
Highlights
Inventory control is an important activity that appears in any kind of organization
The insight gained from the oldest inventory model, economic order quantity (EOQ), reveals that inventory management should be considered as a biobjective optimization problem to strike a balance between inventory carrying and annual orders
The motivation of this study aims to develop an intrinsically multiobjective approach for building the tradeoff surface of probabilistic inventory systems
Summary
Inventory control is an important activity that appears in any kind of organization. For this reason, it has been studied extensively in the past several decades. Most inventory models aggregate different cost concepts, such as ordering cost, carrying cost, and shortage cost, into a single-objective formulation. Optimal decisions about when to order and how much to order are solved by single-objective optimization techniques. The insight gained from the oldest inventory model, economic order quantity (EOQ), reveals that inventory management should be considered as a biobjective optimization problem to strike a balance between inventory carrying and annual orders. Speaking, inventory decisions involve tradeoffs related to operational efficiency and customer service
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