Abstract
The present article was developed for the economic order quantity (EOQ) inventory model under daytime, non-random, uncertain demand. In any inventory management problem, several parameters are involved that are basically flexible in nature with the progress of time. This model can be split into three different sub-models, assuming the demand rate and the cost vector associated with the model are non-randomly uncertain (i.e., fuzzy), and these may include some of the retained learning experiences of the decision-maker (DM). However, the DM has the option of revising his/her decision through the application of the appropriate key vector of the fuzzy locks in their final state. The basic novelty of the present model is that it includes a computer-based decision-making process involving flowchart algorithms that are able to identify and update the key vectors automatically. The numerical study indicates that when all parameters are assumed to be fuzzy, the double keys of the fuzzy lock provide a more accurate optimum than other methods. Sensitivity analysis and graphical illustrations are made for better justification of the model.
Highlights
The traditional economic order quantity (EOQ) model has been enriched with the help of modern researchers, by using a variety of approximations and methodologies to represent an uncertain world.Nash [1] started an initiative with the bargaining problem that is one of the pioneering articles in the field of decision-making for inventory management problems
Maity et al [29] developed a two-decision-makers single-decision inventory model utilizing the concept of the triangular dense fuzzy lock set
Since Model 3 gives over all optimum so the sensitivity analysis has been made for the double key parameters of the cost vector and demand rate which are associated in the EOQ model
Summary
The traditional economic order quantity (EOQ) model has been enriched with the help of modern researchers, by using a variety of approximations and methodologies to represent an uncertain world. The concept of integrating the impact of learning experiences into fuzzy sets, namely, the triangular dense fuzzy set (TDFS), along with new defuzzification methods, was described by De and Beg [24], and they utilized this fuzzy set in philosophical contexts (De and Beg [25]). In their observations, the Cauchy sequence was employed, which naturally converges to zero. Maity et al [29] developed a two-decision-makers single-decision inventory model utilizing the concept of the triangular dense fuzzy lock set. Sensitivity analysis and graphical illustration are carried out to explore the novelty of the present research
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