Designing a single-vendor and multiple-buyers’ integrated production inventory model for interval type-2 fuzzy demand and fuzzy rule based deterioration

Abstract

In this paper, a single-vendor and multiple-buyers' integrated production inventory model is investigated where deterioration rate of the item is assumed to change in accordance with the weather conditions of a particular region. It relies upon the values of certain attributes that have a direct influence on the extent of deterioration. These parameter values are easily forecasted and thereby can be utilized to determine the item depletion rate, which is executed here using Mamdani fuzzy inference scheme. Besides, a nearest interval approximation formula for the defuzzification of interval type-2 fuzzy number (IT2FN) is developed. Its application in the proposed model is brought off by considering imprecise demand patterns at the buyers' locations which are in the form of IT2FNs. The model optimizes the total number of shipments to be made to the buyers within a complete cycle so as to minimize the overall integrated cost incurred. An optimization problem with interval objective function is formulated. A detailed illustration of the theoretical results is further demonstrated with the help of numerical example, followed by sensitivity analysis which provides insights into better decision making.

A Sustainable Supply Chain Model with Low Carbon Emissions for Deteriorating Imperfect-Quality Items under Learning Fuzzy Theory

“…In a similar way, Jaggi et al [52] derived a fuzzy-based inventory model with a trade credit strategy under shortages and without an inspection strategy and analyzed the effect of fuzzy input with other inventory parameters on the buyer's profit. Rout et al [53], motivated by the work of Jaggi et al [52], developed a new type of inventory model with a fuzzy-2 strategy under are filling process for inventory items. A lot of authors have worked on inspection processes with some realistic situations, and Patro et al [54] generalized the work of Rout et al [53] by using the learning concept for imperfect deteriorating-quality items under the fuzzy concept and presented In this section, we selected only literature reviews which relate to imperfect-quality items, especially because this is the motivation for the proposed model.…”

“…Rout et al [53], motivated by the work of Jaggi et al [52], developed a new type of inventory model with a fuzzy-2 strategy under are filling process for inventory items. A lot of authors have worked on inspection processes with some realistic situations, and Patro et al [54] generalized the work of Rout et al [53] by using the learning concept for imperfect deteriorating-quality items under the fuzzy concept and presented In this section, we selected only literature reviews which relate to imperfect-quality items, especially because this is the motivation for the proposed model. Jaggi et al [50] explained the EOQ with a screening strategy, shortages, and trade credit policy under the fuzzy concept for deteriorating items and also showed the effect of deterioration, shortages, and trade credit time on the buyer's profit.…”

“…Rout et al [53], motivated by the work of Jaggi et al [52], developed a new type of inventory model with a fuzzy-2 strategy under are filling process for inventory items. A lot of authors have worked on inspection processes with some realistic situations, and Patro et al [54] generalized the work of Rout et al [53] by using the learning concept for imperfect deteriorating-quality items under the fuzzy concept and presented good results for future work through managerial insights. Most authors have developed inventory models by changing the inventory items.…”

“…The concept of learning was first suggested by Wright [56]. Wright [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] presented many production inventory models by applying the learning concept under some realistic situations. Jayaswal et al [57] studied the many literature reviews of Wright [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] and developed an inventory model with a learning concept and cloudy fuzzy environment for good inventory items under a credit policy system.…”

“…Wright [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] presented many production inventory models by applying the learning concept under some realistic situations. Jayaswal et al [57] studied the many literature reviews of Wright [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] and developed an inventory model with a learning concept and cloudy fuzzy environment for good inventory items under a credit policy system. Jayaswal et al [58] derived a model with human error and backorders under a fuzzy environment and also calculated the lot size for the ordering policies, as well as showed the effect of lot size and backorders on the total inventory cost.…”