Development of a Fuzzy Economic Order Quantity Model of Deteriorating Items with Promotional Effort and Learning in Fuzziness with a Finite Time Horizon

Abstract

This study investigates an economic order quantity model of deteriorating items, where demand is fuzzy in nature and depends on promotional effort with full backorder for a given time horizon. The learning effect in the fuzzy environment is added in this model. A constant deterioration rate is assumed. Under these circumstances, a mathematical model is developed to curtail the total cost over a finite time horizon by determining the replenishment order quantity, number of replenishments, and the fraction of the replenishment cycle when inventory is positive. A solution algorithm is developed to find the optimal solutions. The applicability of the proposed model is illustrated through numerical examples. To get further insights, sensitivity analysis is carried out for the main parameters in crisp, fuzzy, and fuzzy-learning environments.

An inventory model with uncertain demand under preservation strategy for deteriorating items

“…Rajan and Uthayakumar [45] considered a deterministic inventory model by considering full backorder and holding cost that grow exponentially under permitted payment delays and promotional effort. Other researchers (Mahapatra et al [3], Soni and Suthar [21], Liao et al [25]) had considered inventory systems with demand sensitive to promotional strategies. Nouri et al [31] analysed the benefit of investing for promotion in a supplier-retailer chain model where demand was stochastic.…”

“…(4) The promotional effort cost (PEC) is kρ m which collect from the leading results of the cost-of-promotion function, where k and m are constant. (Mahapatra et al [3], Soni and Suthar [21]) (5) The impact of preservation investment u per unit time is that it reduces the effective rate of deterioration and is modeled by a term using a strictly monotonic increasing function, θ 0 (1 − e −au ) for u ≥ 0, where a = positive constant. This ensures that there is no impact when u = 0 and as u increases, the effect increase but is bounded by θ 0 , i.e., the maximum impact as u → ∞ is θ 0 (Shah et al [40]).…”

A Continuous Review Production-Inventory System with a Variable Preparation Time in a Fuzzy Random Environment

“…They minimized the expected total annual cost by simultaneously optimizing the cycle length, reorder point, and lead-time for the whole system based on fuzzy demand. Recently, Mahapatra et al [31] developed a fuzzy EOQ model to analyze the impact of learning to reduce fuzziness within a finite time horizon, as well as to consider the effect of promoting deteriorating items.…”

Solving multi-level multiobjective fractional programming problem with rough interval parameter in neutrosophic environment

“…Recently, rough linear programming is proposed by Hamzehee et al [16], Osman et al [23,24] and they introduced two solutions concepts as surely interval and possibly range for the existance of optimal solution. Many researchers such as Ahmad [3,4], Malik and Sarkar [20] Rashmanlou et al [28], Mahapatra et al [19], Imran et al [17] have worked on intuitionistic fuzzy and neutrosophic research domain. The rough set theory is based on fundamental logic that any uncertain quantification of any objects can be depicted by a pair of specified interval known as the lower and upper approximation of the uncertain quantification.…”