Replenishment Policy Research Articles

Application of improved meta-heuristic algorithms for green preservation technology management to optimize dynamical investments and replenishment strategies

In this study, an application of green preservation technology to obtain optimal pricing and replenishment policies is developed. The decreasing value in waste is considered along with retailer dynamic promotional and green preservation technology investments under a price-promotion model with ramp-type demand. To maximize total profits, the study aims to clean the environment while simultaneously optimizing selling price, replenishment schedule, order quantity, green preservation technology investment, and dynamic investment rate. Pontryagin’s maximum principle is adopted to obtain the optimal dynamic investment rate for waste reduction. Additionally, simulated annealing, particle swarm optimization, and BAT algorithms are individually applied to obtain an optimal decision for waste reduction via green preservation technology. Results showed that the investment rate in promotion is significantly affected by price changes and that the investment in green preservation technology is affected by the nature of the product. Extensive computational experiments are performed to validate the proposed model. Under Ramp-type demand, price differentiation is more rewarding than the uniform pricing of the retailer. Results demonstrate the significant role of dynamic investment strategy. It has invested more based on the low-price sensitivity in the second period, and the optimal investment follows a reverse trend.

Effects of all-units discount on pricing and replenishment policies of an inventory model under power demand pattern

A profit optimisation inventory problem is studied for the products whose demand is interconnected with the per unit selling price and products’ consumption duration as a multiplicative form of the impacts of the selling price as a logit-function and time as a power function. A reduction opportunity of the unit purchase price is offered to the retailer against an order quantity-based prepayment schedule. This study identifies when an inventory procedure is profitable or non-profitable by examining all possibilities of the optimum order quantity under an order quantity-based advance and discount scheme. Combining all the derived theoretical results, an efficacious solution algorithm is established to determine the optimal solution to the problem. Several numerical problems of businesses are solved in order to clarify the algorithm and derive results as well. Finally, the effects of the all-units discount and prepayment system on the retailer's best pricing and replenishing strategies are explored by implementing sensitivity analyses.

Optimizing production-inventory replenishment and lead time decisions under a fill rate constraint in a two-echelon sustainable supply chain with quality issues

This paper considers a single vendor-single buyer coordinated supply chain under stochastic demand. GHG emissions arise from the vendor's production process. A carbon tax and penalties for exceeding multiple emissions limits are considered. A random number of defectives characterises the batch received by the buyer, who performs an inspection activity. Setup and transportation times are controllable through a capital investment, whose outcome is uncertain. An optimisation problem is formulated under the minimax distribution-free approach. The objective is to find the production-inventory replenishment policy and the length of setup and transportation times that minimise the long-run expected total system cost rate considering a fill rate constraint. Several properties of the cost function are demonstrated, and an optimisation method is developed. Finally, numerical experiments are carried out to draw insights on the effect of the model parameters, including different legislation schemes concerning GHG emissions, on the optimal system performance. The novelty of the paper consists into investigating the effect of a controllable lead time on coordinated supply chain decisions in presence of GHG emissions and quality issues, in a stochastic environment. A major finding is that controlling lead time enhances both the overall cost efficiency and the environmental sustainability of the system.

Proactive in-house part-feeding for mixed-model assembly systems with dynamics

The rich data collected from smart shop floors necessitate proactive decision-making to relieve system nervousness in a dynamic environment. This proactivity takes real-time actions based on disruption-driven prediction but not on disruption-created negative effects. This paper focuses on investigating a proactive in-house part-feeding (PIP) approach by integrating real-time data collected from shop floors with the physical properties of a mixed-model assembly system. An event-driven proactive prediction for replenishments is first implemented after coupling such data as disruptions, job rescheduling, part re-releasing, and real-time lineside inventories with a widely used reorder-point-based replenishment policy. This prediction can provide sufficient prior information about replenishments and, in turn, trigger tow-train rerouting to simultaneously minimize the costs of distribution and lineside inventories. In terms of limited tow train capacity and the just-in-time part-feeding goal, this rerouting problem should address various constraints on multiple trips, time windows, and the real-time working status of tow trains. An adaptive large neighbourhood search (ALNS) is developed to obtain the best solution for rerouting by designing customized removal and insertion operators, as well as local search heuristics. A case study of a real-life car seat assembly system is presented to verify the efficiency of the PIP. Several rerouting benchmark instances are obtained from the case study to evaluate the performance of ALNS, and the computational results indicate that the proposed ALNS has a satisfactory performance in solving the rerouting instances compared with some of the formerly proposed and published methods. The results of the practical case study illustrate that the PIP may increase the lineside inventory cost slightly but can significantly reduce the distribution cost compared with prevailing reactive approaches

Integrating Replenishment Policy and Maintenance Services in a Stochastic Inventory System with Bilateral Movements

We study an inventory control problem with two storage facilities: a primary warehouse (PW) of limited capacity M, and a subsidiary one (SW) of sufficiently large capacity. Two types of customers are considered: individual customers arriving at (positive and negative) linear rates governed by a Markov chain, and retailers arriving according to a Markov arrival process and bringing a (positive and negative) random number of items. The PW is managed according to a triple-parameter band policy (M,S,s),0≤s<S≤M, under a lost sales assumption. Under this policy, as soon as the stock level at the PW falls below s, a refilling to S is performed by a distributor after a random lead-time. However, if the stock exceeds level S when the distributor arrives, no refilling is carried out, and only maintenance services are performed. Items that exceed level M are transferred to the SW at a negligible amount of time for those used in related products. Our cost structure includes a fixed order cost, a variable cost for each item supplied by the distributor, a cost for the additional maintenance, a salvage payment for each transferred item from the PW to the SW, and a loss cost for each unsatisfied item due to demands. We seek to determine the optimal thresholds that minimize the expected overall cost under the discounted criterion. Applying first-passage time results, we present a simple set of equations that provide managers with a useful and an efficient tool to derive the optimal thresholds. Sensitivity analysis and fruitful conclusions along with future scope of research directions are provided.

An inventory model with permissible delay in payment and different interest rate charges

Adopting the permissible delay method by suppliers offers ample opportunity to overcome the burden of holding costs from retailers. This compensates retailers against the competitive situation as an alternative to price discounts. We present an inventory model for a finite time horizon for deteriorating items. The model focuses on an optimal replenishment policy to maximize profit through inventory control by considering two different interest rate charges. The implementation is possible by the proposed mathematical model for a ramp-type demand with a permissible delay of payment approach. We compare two scenarios: with and without permissible delay in payment. The computational algorithm, numerical examples, theorems, Hessian matrix, and graphs confirm the effectiveness of the inventory model proposed in this study. Sensitivity analysis is used to find risk-compensate opportunities in a peculiar situation.

Retail inventory policy under demand uncertainty and inventory-level-dependent demand

PurposeThe purpose of this paper is to determine the inventory replenishment policies when demand rate is a function of the inventory space allocated to the products on retail shelves. Existing results on inventory policies with inventory-level-dependent demand (ILDD) assume deterministic functional forms of the demand rate. In this paper, the authors model the inventory decisions when demand is a function of shelf-space allocation and random uncertainty. The authors provide managerial insights of this paper's results.Design/methodology/approachThe demand rate is assumed to be a function of shelf-space allocation based on two settings in the literature. First, the authors model the demand rate as a function of initial shelf-space allocation. In the next setting, the authors assume that the demand rate is a function of instantaneous inventory level on shelves. In both the settings, the authors also model random demand uncertainty in addition to the shelf-space dependency of demand rate. The objective is to maximize the expected profit and determine the inventory parameters.FindingsIn addition to the demand uncertainty, the authors consider linear, power and exponential functional forms of demand rate. Inventory policy that maximizes expected profit is determined when demand rate is a function of initial allocation and displayed inventory level. The results are implementable for practitioners for optimizing the shelf-space allocation and related inventory policy.Originality/valueMost of the extant results on inventory policy with shelf-space-dependent demand do not model the demand uncertainty. The authors model a variety of functional forms of demand rate with ILDD in addition to the demand uncertainty. The results are a building block for more applications in inventory management for real-life applications.

Optimal replenishment and transshipment management with two locations

AbstractWe study the problem of optimally managing an inventory system with backorders over a finite time horizon where the objective is minimization of expected total discounted costs. The system consists of two locations each stocking the same product. At the beginning of each time period decisions are made about replenishment at each location and about any quantity to transship between the locations before demand is observed. Leveraging the L♮‐convexity of the problem's cost function; we characterize the optimal replenishment and transshipment policy for this system. More specifically, we show the optimal policy can be described using switching curves monotone in the system state. We also discuss two extensions. For a lost‐sales model, we establish L♮‐convexity and apply it to characterize the optimal policy, simplifying the analysis found in previous work. In the other extension, we investigate the optimal policy for a partial transshipment problem where only one location orders from the external supply source and then transships to the other location.

On the order-up-to policy with intermittent integer demand and logically consistent forecasts

We measure the impact of a first-order integer auto-regressive, INAR(1), demand process on order-up-to (OUT) replenishment policy dynamics. We obtain a unique understanding of the bullwhip behaviour for slow moving integer demand. We forecast the integer demand in two ways; with a conditional mean and a conditional median. We investigate the impact of the two forecasting methods on the bullwhip effect and inventory variance generated by the OUT replenishment policy. While the conditional mean forecasts result in the tightest inventory control, they result in real-valued orders and inventory levels which is inconsistent with the integer demand. However, the conditional median forecasts are integer-valued and produce logically consistent integer order and inventory levels. The conditional median forecasts minimise the expected absolute forecasting error, but it is not possible to obtain closed form variance expressions. Numerical experiments reveal existing results remain valid with high volume correlated demand. However, for low volume demand, the impact of the integer demand on the bullwhip effect is often significant. Bullwhip with conditional median forecasts can be both lower and higher than with conditional mean forecasts; indeed it can even be higher than a known conditional mean upper bound (that is valid for all lead times under real-valued, first-order auto-regressive, AR(1), demand), depending on the auto-regressive parameter. Numerical experiments confirm the conditional mean inventory variance is a lower bound for the conditional median inventory variance.

Double-Sources Queuing-Inventory Systems with Finite Waiting Room and Destructible Stocks

Models of double-source queuing-inventory systems are studied in the presence of a finite buffer for waiting in the queue of consumer customers, where instant destruction of inventory is possible. It is assumed that the lead times of orders, as well as the cost of delivery from various sources, differ from each other. Replenishment of stocks from various sources is carried out according to the following scheme: if the inventory level drops to the reorder point s, then a regular order for the supply of inventory to a slow source is generated; if the inventory level falls below a certain threshold value r, where r < s, then the system instantly cancels the regular order and generates an emergency order to the fast source. Models of systems that use (s, S) or (s, Q) replenishment policies are studied. Exact and approximate methods for finding the performance measures of the models under study are proposed. The problems of minimizing the total cost are solved by choosing the appropriate values of the parameters s and r when using different replenishment policies. Numerical examples demonstrated the high accuracy of an approximate method as well as compared performance measures of the system under various replenishment policies.

Inventory models for deteriorating items with fixed lifetime, partial backordering and carbon emissions policies

In this study, a sustainable inventory model is devised to obtain the retailer’s optimal pricing and replenishment policies for degrading items having a certain lifetime incorporating partial back order as a shortage and dynamic demand under two different scenarios (a) carbon cap and trade policy (b) carbon tax policy. The primary objective of this study is to maximize the retailer’s annual total profit. The retailer’s profit function has been optimized with the help of convexity/concavity criteria employing classical optimization techniques. Based on a real case study, two different numerical examples and corresponding optimal solutions have been shown for both models with the help of Lingo 17 software. Moreover, the impact of the major inventory parameters and prominent managerial insights are presented for the robustness of the proposed model that can cooperate with industrial managers/decision-makers for the overall improvement of his/her industry to take effective and qualitative decisions.

The inventory replenishment policy in an uncertain production-inventory-routing system

<p style='text-indent:20px;'>This study introduces an uncertain programming model for the integrated production routing problem (PRP) in an uncertain production-inventory-routing system. Based on uncertainty theory, an uncertain programming model is proposed firstly and then transformed into a deterministic and equivalent model. The study further probes into different types of replenishment policies under the condition of uncertain demands, mainly the uncertain maximum level (UML) policy and the uncertain order-up to level (UOU) policy. Some inequalities are put forward to define the UML policy and the UOU policy under the uncertain environments, and the influences brought by uncertain demands are highlighted. The overall costs with optimal solution of the uncertain decision model grow with the increase of the confidence levels. And they are simultaneously affected by the variances of uncertain variables but rely on the value of confidence levels. Results show that when the confidence levels are not less than 0.5, the cost difference between the two policies begins to narrow along with the increase of the confidence levels and the variances of uncertain variables, eventually being trending to zero. When there are higher confidence levels and relatively large uncertainty in realistic applications, in which the solution scale is escalated, being conducive to its efficiency advantage, the comprehensive advantages of the UOU policy is obvious.</p>

Minimizing food waste in grocery store operations: Literature review and research agenda

Research on grocery waste in food retailing has recently attracted particular interest. Investigations in this area are relevant to address the problems of wasted resources and ethical concerns, as well as economic aspects from the retailer’s perspective. Reasons for food waste in retail are already well-studied empirically, and based on this, proposals for reduction are discussed. However, comprehensive approaches for preventing food waste in store operations using analytics and modeling methods are scarce. No work has yet systematized related research in this domain. As a result, there is neither any up-to-date literature review nor any agenda for future research. We contribute with the first structured literature review of analytics and modeling methods dealing with food waste prevention in retail store operations. This work identifies cross-cutting store-related planning areas to mitigate food waste, namely (1) assortment and shelf space planning, (2) replenishment policies, and (3) dynamic pricing policies. We introduce a common classification scheme of literature with regard to the depth of food waste integration and the characteristics of these planning problems. This builds our foundation to review analytics and modeling approaches. Current literature considers food waste mainly as a side effect in costing and often ignores product age dependent demand by customers. Furthermore, approaches are not integrated across planning areas. Future lines of research point to the most promising open questions in this field.

Automatic refilling vending machine using Amazon DRS AWS

Presently, the existing replenishment device may be handiest when systematically reviewing the inventories. Whereas, there are ongoing vending machines that may not be able to provide or discover whether the substances are doing nicely to be replenished or no longer be replenished. This effect is an inefficient replenishment policy and there will be regular inventory-out among the products. This undertaking intends to provide the automatic replenishment of smart merchandising gadget with the intention to provide and support inventories to the administrator. Amazon sprint replenishment service (Amazon DRS) permits, while the vending device is about to run out of the stock/product, it to automatically locate orders on Amazon, administering which of the supplies may be due for replenishment. By executing this venture, inventory-out of the vending machines may be prevented and a green manner of the replenishment system can be carried out.

Optimal joint dynamic pricing and inventory replenishment policy for perishable products with freshness-sensitive demand

Optimal joint dynamic pricing and inventory replenishment policy for perishable products with freshness-sensitive demand

Asymptotic Optimality of Simple Replenishment Policies for a Lost-Sales Inventory System with Delivery Lead Time and Purchase Returns

Asymptotic Optimality of Simple Replenishment Policies for a Lost-Sales Inventory System with Delivery Lead Time and Purchase Returns

Sustainable production model with stochastic machine breakdown using smart manufacturing under circular economy

<abstract> <p>Machine breakdown usually implies unexpected physical damage to machinery due to any reason which requires fixing or replacement to continue the process. This article presentsan investigation of a sustainable model with stochastic machine breakdown. To reduce the risk of disruption,a smart manufacturing system is used. Considering the environmental issues faced by people,the model is developed under a circular economy through end-of-life treatment to recapture the value of the product,labor and resources. The used buyback products are collected,out of which items in good condition are remanufactured and sold in another market while the rest are salvaged. As the production process is not perfectly reliable,the serviceable products go through an automated inspection process,and imperfect items are reworked. A mathematical model is developed for deterioration of items to analyze the optimal replenishment policies,and the results have been illustrated with numerical verification. Based on the analysis,some managerial insights have been provided for decision-makers.</p> </abstract>

Breaking truck dominance in supply chains: Proactive freight consolidation and modal split transport

Environmental concerns and a shortage of truck drivers motivate a paradigm shift in truck-dominated supply chains. To alleviate the pressure on truck transport, one can consolidate freight with other companies. Freight consolidation can be facilitated by third-party logistics service providers, who combine shipments that require delivery to the same destination on the same day. More gains can be realized through “proactive freight consolidation”, by proactively synchronizing the timing of shipments prior to placing orders. This is facilitated by a joint replenishment policy. The truck intensity of supply chains can be further reduced by shifting freight from road towards alternative transport modes, such as train. Modal split transport combines two complementary transport modes by using both modes in parallel. We analyze how proactive freight consolidation can be combined with modal split transport. We propose a heuristic that combines a can-order joint replenishment policy to consolidate freight orders proactively via truck, with a tailored base-surge policy to coordinate shipments via train. We develop a lower bound on the optimal cost to validate our heuristic. By comparing the truck usage and cost performance of our policy against alternative replenishment strategies, we show how the combination of proactive freight consolidation and modal split transport can shift freight towards alternative transport modes, without negatively impacting costs or service.

Optimizing inventory control through a data-driven and model-independent framework

Machine learning has shown great potential in various domains, but its appearance in inventory control optimization settings remains rather limited. We propose a novel inventory cost minimization framework that exploits advanced decision-tree based models to approximate inventory performance at an item level, considering demand patterns and key replenishment policy parameters as input. The suggested approach enables data-driven approximations that are faster to perform compared to standard inventory simulations, while being flexible in terms of the methods used for forecasting demand or estimating inventory level, lost sales, and number of orders, among others. Moreover, such approximations can be based on knowledge extracted from different sets of items than the ones being optimized, thus providing more accurate proposals in cases where historical data are scarce or highly affected by stock-outs. The framework was evaluated using part of the M5 competition’s data. Our results suggest that the proposed framework, and especially its transfer learning variant, can result in significant improvements, both in terms of total inventory cost and realized service level.

Designing a resilient supply chain through a robust adaptive model predictive control policy under perishable goods and uncertain forecast information

We consider the inventory control problem for Supply Chains (SC) with deteriorating items and an uncertain future customer demand freely varying inside a given compact set. The problem is to define a resilient Replenishment Policy (RP) keeping the actual inventory level as close as possible to a desired reference trajectory. This requirement should be satisfied despite uncertainties on the decay factor of stocked goods and unexpected customer demand patterns outside the bounds of the compact set. We propose a method based on a Resilient Robust Model Predictive Control (RRMPC) approach. This requires dealing with a Min-Max Constrained Optimization Problem (MMCOP). To reduce the numerical complexity of the algorithm, the control signal is parametrized using B-spline functions.