Optimal joint replenishment policy for multiple non-instantaneous deteriorating items

2017 ◽  
Vol 55 (16) ◽  
pp. 4625-4642 ◽  
Author(s):  
Xue-Yi Ai ◽  
Jin-Long Zhang ◽  
Lin Wang
Keyword(s):  
Deteriorating Items ◽  
Joint Replenishment ◽  
Replenishment Policy

Sustainable optimal ordering quantity for non-instantaneous deteriorating items under joint replenishment with substitution and carbon emission

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ranu Singh ◽  
Vinod Kumar Mishra
Keyword(s):  
Cost Function ◽  
Carbon Emission ◽  
Inventory Model ◽  
Deteriorating Items ◽  
Joint Replenishment ◽  
Content Type ◽  
Total Cost ◽  
Optimal Ordering ◽  
Ordering Quantity ◽  
Total Cost Function

Purpose Carbon emission is a significant issue for the current business market and global warming. Nowadays, most countries have focused to reduce the environmental impact of business with durable financial benefits. The purpose of this study is to optimize the entire cost functions with carbon emission and to find the sustainable optimal ordering quantity for retailers. Design/methodology/approach This paper illustrates a sustainable inventory model having a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. In this model demand and deterioration rate are considered as deterministic, constant and triangular fuzzy numbers. The objective is to find the optimal ordering quantity for retailers and to minimize the total cost function per unit time with carbon emission. The model is then solved with the help of Maple software. Findings This paper presents a solution method and also develop an algorithm to determine the order quantities which optimize the total cost function. A numerical experiment illustrates the improvement in optimal total cost of the inventory model with substitution over without substitution. The graphical results show the convexity of the cost function. Finally, sensitivity analysis is given to get the impact of parameters and validity of the model. Originality/value This study considers a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. From the literature review, in the authors’ knowledge no researcher has undergone this kind of study.

An adaptive joint replenishment policy for items with non-stationary demands

2018 ◽  
Vol 20 (3) ◽  
pp. 1665-1684 ◽  
Author(s):  
Young Hyeon Yang ◽  
Jong Soo Kim
Keyword(s):  
Joint Replenishment ◽  
Replenishment Policy

Joint replenishment policy with backordering and special sale

2013 ◽  
Vol 46 (7) ◽  
pp. 1172-1198 ◽  
Author(s):  
Ata Allah Taleizadeh ◽  
Hadi Samimi ◽  
Babak Mohammadi
Keyword(s):  
Joint Replenishment ◽  
Replenishment Policy

scholarly journals Determining the Optimum Ordering Policy in Multi-Item Joint Replenishment Problem Using a Novel Method

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wen-Tsung Ho
Keyword(s):  
Cycle Time ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Lead Times ◽  
Joint Replenishment ◽  
Joint Replenishment Problem ◽  
Ordering Policy ◽  
Replenishment Policy ◽  
Demand Rate ◽  
Time Division

This work investigates the joint replenishment problem (JRP) involving multiple items where economies exist for replenishing several items simultaneously. The demand rate for each item is known and constant. Shortages are not permitted and lead times are negligible. Many heuristic algorithms have been proposed to find quality solutions for the JRP. In this paper, cycle time division and recursive tightening methods are developed to calculate an efficient and optimal replenishment policy for JRP. Two theorems are demonstrated to guarantee that an optimal solution to the problem can be derived using cycle time division and recursive tightening methods. Restated, cycle time division and recursive tightening methods theoretically yield the optimal solution in 100% of instances. The complexity of cycle time division and recursive tightening methods is justO(NlogN), whereNrepresents the number of items involved in the problem. Numerical examples are included to demonstrate the algorithmic procedures.

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