An Integrated Single Vendor-Buyer Stochastic Inventory Model with Partial Backordering under Imperfect Production and Carbon Emissions

This paper develops an integrated single vendor single buyer inventory model with imperfect quality and environmental impact. The demand during lead time is assumed to be stochastic and follows the normal distribution. An integrated system with controllable lead time and logarithmic investment to reduce the defective percentage is discussed in this model.100% error-free screening process is adopted by the buyer to separate defective and non-defective items. We assume that shortages are allowed and are partially backordered at the buyer’s end. Logistics management is the component of supply chain management that focusses on how and when to get raw materials, intermediate products and finished goods from their respective origins to their destinations.Thus, transportation play a major role in supply chain. As transportation increases, it affects the weather by the matter of carbon emission.The fixed and variable carbon emission cost for both vendor and buyer is considered. The prime motive is to determine the optimal policies regarding optimal order quantity, reorder point, lead time and the number of lots delivered in a production run by minimizing the expected total cost of the system. Finally, a numerical example is provided to demonstrate the model

Periodic review inventory policy with variable ordering cost, lead time, and backorder rate

In this paper, a stochastic periodic review inventory model is developed. The backorder rate (backorder price discount), ordering cost (safety stock), lead time, and review period are treated as decision variables. The ordering cost and lead time can be controlled by using capital investment and crashing cost, respectively. It is assumed that shortages are allowed and partially backlogged. If an item is out of stock, the supplier may offer a negotiable price discount to the loyal, tolerant and obliged customers to pay off the inconvenience of backordering. Furthermore, it is assumed that the protection interval demand follows a normal distribution. Our objective is to develop an algorithm to determine the optimal decision variables, so that the total expected annual cost incurred has a minimum value. Finally, a numerical example is presented to illustrate the solution procedure, and sensitivity analysis is carried out to analyze the proposed model. The numerical results show that a significant amount of savings can be obtained by making decisions with capital investment in reducing ordering cost

Efficient near-optimal procedures for some inventory models with backorders-lost sales mixture and controllable lead time, under continuous or periodic review

This paper considers a number of inventory models with backorders-lost sales mixture, stockout costs, and controllable lead time. The lead time is a linear function of the lot size and includes a constant term that is made of several components. These lot-size-independent components are assumed to be controllable. Both single- and double-echelon inventory systems, under periodic or continuous review, are considered. To authors knowledge, these models have never been previously studied in literature. The purpose of this paper is to analyse and optimize these novel inventory models. The optimization is carried out by means of heuristics that work on an ad hoc approximation of the cost functions. This peculiarity permits to exploit closed-form expressions that make the optimization procedure simpler and more readily applicable in practice than standard approaches. Finally, numerical experiments investigate the efficiency of the proposed heuristics and the sensitivity of the developed models

A periodic review policy with quality improvement, setup cost reduction, backorder price discount, and controllable lead time

This paper explores a periodic review inventory model under stochastic demand. The setup (or ordering) cost and the lead time are controllable. The model considers an imperfect production process, whose quality can be improved by means of an investment. A backorder price discount to motivate customers to wait for backorders is included. The demand in the protection interval is first assumed Gaussian; then, the distribution-free approach is adopted. The objective is to determine the review period, the setup cost, the quality level, the backorder price discount, and the length of lead time that minimize the long-run expected total cost per time unit. A solution method for each case is presented. Numerical experiments show that substantial savings can be achieved if the quality level, the setup cost and the lead time are controlled, and if a backorder price discount is applied. A sensitivity analysis is finally carried out

A fuzzy periodic review integrated inventory model involving stochastic demand, imperfect production process and inspection errors

In this study, we investigate an integrated production-inventory system consisting of a single-vendor and single-buyer. The buyer manages its inventory level periodically at a certain period of time. We consider a fuzzy annual demand, imperfect production, inspection errors, partial backordering, and adjustable production rate in the proposed model. Additionally, it is assumed that the protection interval demand follows a normal distribution. The model contributes to the current literature by allowing the inclusion of fuzzy annual demand, adjustable production rate and imperfect production and inspection processes. Our objective is to optimize the number of deliveries from vendor to buyer, the buyer’s review period, and the vendor’s production rate, so that the joint expected total annual cost incurred has the minimum value. Furthermore, an iterative procedure is proposed to find the optimal solutions of the model. We also provide a numerical example and conduct a simple sensitivity analysis to illustrate the model’s behaviour and feasibility. The results from the sensitivity analysis show that the defective rate, type I inspection error, fuzzy annual demand, fixed production cost, variable production cost and setup cost give impacts to both the review period and production rate. Finally, it is concluded that the proposed model can be applied by managers or practitiones for managing inventories across the supply chain involving a vendor and a buyer

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Optimizing Vendor-Buyer Inventory Model with Exponential Quality Degradation for Food Product Using Grey Wolf Optimizer

Inventory is an essential factor in the supply chain. Inventory problems are increasingly complex for perishable products such as food. This study proposes a Single Vendor-Single Buyer (SVSB) model for food products by considering exponential quality degradation. The objective function of this problem is to maximize the Joint Total Profit (JTP) of the SVSB system. The frequency of ordering raw materials (m), the frequency of delivery of the finished product (n), and the time of the inventory cycle (T) were the three (3) decision variables introduced in the study. This study proposes the Grey Wolf Optimizer (GWO) algorithm as an optimization tool for SVSB problems. A case study was conducted on a food company in Indonesia. Sensitivity analysis on costs, revenue, and JTP was also presented. The results showed that raw materials' quality degradation level affected JTP. The results also suggested that the GWO algorithm performs better than the Genetic Algorithm (GA) to optimize the SVSB inventory model