Periodic Inventory Control Research Articles

Joint Optimization of Empty Container Repositioning and Inventory Control Applying Dynamic Programming and Simulated Annealing

Based on the division of public and exclusive hinterlands, this paper studies the joint optimization problem of empty container repositioning and inventory control under non-stationary demand within the port cluster. This paper established a stochastic mixed integer programming model using distributed robust optimization methods, combined with quantitative and periodic inventory control strategies. After conducting a deterministic transformation, this paper designed a hybrid algorithm of dynamic programming and simulated annealing to solve the model, and compared the various costs under stationary and non-stationary scenarios. The results show that the joint optimization of empty container inventory control and repositioning can always reduce the total cost of empty container management for shipping companies. Periodic inventory control strategies are more suitable for shipping companies to apply to empty container management in non-stationary situations. Sensitivity analysis shows that there is a positive correlation between uncertain demand parameters and the total cost of shipping companies. The superiority of the empty container repositioning mode proposed in this paper under sea-land coordination has also been demonstrated by changing the accessibility parameters between terminals.

Penyimpanan Sarana dan Prasarana Pendidikan

The storage of educational facilities and infrastructure involves the action of storing school equipment in designated storage places. This research aims to understand the methods of storing these facilities. The research methodology employs a literature review by collecting and combining sources related to the storage of educational facilities and infrastructure. The research findings reveal various storage methods, including receiving, screening, and inspecting incoming goods. Subsequent steps involve examining items for storage, organizing them into specific groups, recording in acceptance books, issuing receipts, and warehouse notes. Plans are made for the storage location, ensuring quick retrieval when needed. Existing items must be received, registered, stored, sorted, maintained, and preserved in an orderly, fast, and secure manner. The research emphasizes managing the storage and use of all items in rooms and warehouses, periodic inventory control, reporting on the preservation conditions of items according to applicable regulations, and issuing items in accordance with the instructions for item issuance.

Optimization on the multi-period empty container repositioning problem in regional port cluster based upon inventory control strategies

Within the area of regional port clusters, this paper establishes a multi-period mixed integer programming model to optimize the empty container repositioning between public hinterlands and ports, comprehensively considering the quantitative and periodic inventory control strategy. At the same time, this paper dynamically and heuristically determines the empty container inventory threshold (D, U) under quantitative strategy and S under periodical strategy at each port within the regional port clusters. On this basis, the empty container repositioning schemes between public hinterlands and ports are optimized by using Markov decision process combined with dynamic programming method. In addition, Liaoning coastal regional port cluster and its northeast hinterland are selected as the objects to solve this model and the results show that the total cost of shipping company can be saved by 14.16% and 11.92%, respectively, by the quantitative and periodical inventory control strategy. Selecting the quantity of public hinterland terminals, the empty container demand of public hinterland terminals and ports, the inventory threshold of empty containers and other factors, this paper carries on the sensitivity analysis. This paper validates inventory control strategy can weaken the shipping company in the influence of the external environment changes. And the quantitative inventory control strategy can reduce the total cost value to a greater extent and more effective in cost control than periodical strategy.

Learning in Structured MDPs with Convex Cost Functions: Improved Regret Bounds for Inventory Management

A fundamental yet notoriously difficult problem in operations management is the periodic inventory control problem under positive lead time and lost sales. More recently, there has been interest in the problem setting where the demand distribution is not known a priori and must be learned from the observations made during the decision-making process. In “Learning in Structured MDPs with Convex Cost Functions: Improved Regret Bounds for Inventory Management,” Agrawal and Jia present a reinforcement learning algorithm that uses the observed outcomes of past decisions to implicitly learn the underlying dynamics and adaptively improve the decision-making strategy over time. They show that, compared with the best base-stock policy, their algorithm achieves an optimal regret bound in terms of the time horizon and scales linearly with the lead time of the inventory ordering process. Furthermore, they demonstrate that their approach is not restricted to the inventory problem and can be applied in an almost black box manner to more general reinforcement learning problems with convex cost functions.

An Order-Up-to Inventory Model with Sustainability Consideration

Along with growing interest in environmental concerns these days, significant academic efforts have been exerted to incorporate sustainability issues into the existing inventory models except for fixed-review interval (i.e., order-up-to models). In this study, we develop an order-up-to model considering environment-related costs and investigate the value of this new policy over the naïve one. Results of an extensive simulation study reveal that sustainability consideration reduces the total costs and that its value is higher when the mean demand is higher, when demand is more variable, when the costs of transshipment or inventory holding are lower, or when an ordering setup cost or an additional indirect cost of having inventory are higher. These findings fill the research gap in existing literature and contribute to managerial implications for periodic inventory control in practice.

Efficient Algorithms for a Large-Scale Supplier Selection and Order Allocation Problem Considering Carbon Emissions and Quantity Discounts

This paper considers a multi-period supplier selection and order allocation problem for a green supply chain system that consists of a single buyer and multiple heterogeneous suppliers. The buyer sells multiple products to end customers and periodically replenishes each item’s inventory using a periodic inventory control policy. The periodic inventory control policy used by the buyer starts every period with an order size determination of each item and the subsequent supplier selection to fulfill the orders. Because each supplier in the system is different from other suppliers in the types of carrying items, delivery distance, item price, and quantity discount schedule, the buyer’s problem becomes a complicated optimization problem. For the described order size and supplier selection problem of the buyer, we propose a nonlinear integer programming model and develop two different algorithms to enhance the usability of the model in a real business environment with a large amount of data. The algorithms are developed to considerably cut computational time and at the same time to generate a good feasible solution to a given supplier selection and order allocation problem. Computational experiments that were conducted to test the efficiency of the algorithms showed that they can cut as much as 99% of the computational time and successfully find feasible solutions, deviating not more than 3.4% from the optimal solutions.

A centralized stochastic inventory control model for perishable products considering age-dependent purchase price and lead time

In this paper, we present a stochastic multi-period multi-echelon $$ \left( {s, S} \right) $$ periodic inventory control model for perishable products in a healthcare environment. We consider age-dependent purchase price, order lead time, and allow for backorder. The model is inspired by a real case where a regional health system operates multiple hospitals in addition to managing a central warehouse. The warehouse centralizes the purchasing of the products and then distributes them among hospitals while demand for products in each hospital is uncertain. We formulate the problem as a stochastic mixed-integer linear programming model and study two purchasing strategies. The first and traditional strategy assumes that the warehouse only orders fresh products, i.e., products with the latest expiry date and most expensive, referred to as purchasing constant remaining life (CRL). The second strategy considers a discount function with a lower purchase price for products with a shorter remaining life, referred to as different remaining life (DRL). The designed models are naturally complex and intractable to obtain optimal solutions by an analytical approach for large instances of the problem. Thus, we developed a genetic algorithm to solve the problem and benchmarked its performance against an exact method. We also verified the results of the stochastic models through simulation. Extensive sensitivity analysis is also conducted to investigate the behaviors of both CRL and DRL strategies about the risk of product shortage, product expiration and the total cost imposed on the system.

A periodic inventory system of intermittent demand items with fixed lifetimes

Perishable items with a limited lifespan and intermittent/erratic consumption are found in a variety of industrial settings: dealing with such items is challenging for inventory managers. In this study, a periodic inventory control system is analysed, in which items are characterised by intermittent demand and known expiration dates. We propose a new inventory management method, considering both perishability and intermittency constraints. The new method is a modification of a method proposed in the literature, which uses a periodic order-up-to-level inventory policy and a compound Bernoulli demand. We derive the analytical expression of the fill rate and propose a computational procedure to calculate the optimal solution. A comparative numerical analysis is conducted to evaluate the performance of the proposed solution against the standard inventory control method, which does not take into account perishability. The proposed method leads to a bias that is only affected by demand size, in contrast to the standard method which is impacted by more severe biases driven by intermittence and periods before expiration.

Lagrangian Relaxation for an Inventory Location Problem with Periodic Inventory Control and Stochastic Capacity Constraints

We studied a joint inventory location problem assuming a periodic review for inventory control. A single plant supplies a set of products to multiple warehouses and they serve a set of customers or retailers. The problem consists in determining which potential warehouses should be opened and which retailers should be served by the selected warehouses as well as their reorder points and order sizes while minimizing the total costs. The problem is a Mixed Integer Nonlinear Programming (MINLP) model, which is nonconvex in terms of stochastic capacity constraints and the objective function. We propose a solution approach based on a Lagrangian relaxation and the subgradient method. The decomposition approach considers the relaxation of different sets of constraints, including customer assignment, warehouse demand, and variance constraints. In addition, we develop a Lagrangian heuristic to determine a feasible solution at each iteration of the subgradient method. The proposed Lagrangian relaxation algorithm provides low duality gaps and near-optimal solutions with competitive computational times. It also shows significant impacts of the selected inventory control policy into total system costs and network configuration, when it is compared with different review period values.

INVENTORY CONTROL SYSTEM FOR INTERMITTENT ITEMS WITH PERISHABILITY

Perishable items, with a limited lifespan and a known expiration date, are found in a variety of industrial settings. From the food to the pharmaceutical industries, the supply chains specialize their inventory control systems to handle the added complexity. These efforts are enhanced when the items present also an intermittent consumption, characterized by frequent periods without demand mixed to highly variable positive demand events. In this paper, a novel periodic inventory control system aims at bridging the gap between these two product features, managing intermittent items with expiration dates. The proposed system performs a combinatorial analysis evaluating all the demand scenarios before and after an expiration date to measure the expected fill rate. An optimization algorithm then sets the order quantity, using mathematical properties of the system to define efficient search boundaries.

Development and simulation analysis of a new perishable inventory model with a closing days constraint under non-stationary stochastic demand

Abstract The food waste in grocery retail is a worldwide problem. Many mathematical inventory models for perishable items do not have a closing days constraint, although the age of perishable items also increases on closing days in grocery stores. We develop a new age-based inventory model with a closing days constraint. This stochastic multi-item inventory model includes total stock capacity constraints, a positive lead time, a periodic inventory control, a target customer service level and mixed FIFO and LIFO issuing policies for perishable items with a fixed lifetime under a non-stationary random demand. We show in a comparative simulation study under a rolling planning that the closing days constraint improves order decisions and reduces waste quantities and costs in grocery stores.

Publisher Correction: Approaches for periodic inventory control under random production yield and fixed setup cost

In the original version of the article, operator symbols “⌊ ⌋” “⌈ ⌉” have been inadvertently processed as square brackets in equations 17, 20, the first line of the third paragraph and second line of the fourteenth paragraph in Sect. 4.1.

Approaches for periodic inventory control under random production yield and fixed setup cost

In this paper, we study a multi-period inventory control problem with random demand and stochastically proportional production yield. The model includes nonzero processing lead time as well as fixed setup cost for a replenishment order. From prior research, it is evident that the optimal control rule must have a highly complex structure so that only simple policies are reasonable candidates for practical problem solving. In this paper, we propose a periodic review (s, S) policy with simple order inflation and compare different heuristic approaches for determining the two policy parameters. Two of these approaches are taken from the literature and, partly, adjusted to fit into the periodic-review planning context. A comprehensive numerical study reveals that both methods perform insufficiently, mainly because they do not take into account the yield risk from open orders during lead time. Therefore, three new approaches for parameter determination are developed, which consider this risk but follow very different concepts in their design. Two of these approaches follow simple-to-implement ideas for parameter adjustment to demand and yield risks and can be applied as spreadsheet applications, while the third one is based on an approximation of the objective value as function of the parameters s and S, which then must be computed numerically. From the experimental study, it turns out that all three approaches have a similarly high performance, not only concerning their average but also their worst-case behavior. The numerical study also provides insights into how yield randomness affects the policy parameters and elements of total expected cost.

Solving a Novel Inventory Location Model with Stochastic Constraints and(R,s,S)Inventory Control Policy

We solve a novel inventory-location model with a stochastic capacity constraint based on a periodic inventory control (ILM-PR) policy. The ILM-PR policy implies several changes with regard to other previous models proposed in the literature, which consider continuous review as their inventory policy. One of these changes is the inclusion of the undershoot concept, which has not been considered in previous ILM models in the literature. Based on our model, we are able to design a distribution network for a two-level supply chain, addressing both warehouse location and customer assignment decisions, whilst taking into consideration several aspects of inventory planning, in particular, evaluating the impact of the inventory control review period on the network configuration and system costs. Because the model is a very hard-to solve combinatorial nonlinear optimisation problem, we implemented two heuristics to solve it, namely, Tabu Search and Particle Swarm Optimisation. These approaches were tested over small instances in which they were able to find the optimal solution in just a few seconds. Because the model is a new one, a set of medium-size instances is provided that can be useful as a benchmark in future research. The heuristics showed a good convergence rate when applied to those instances. The results confirm that decision making over the inventory control policy has effects on the distribution network design.

Selecting the best periodic inventory control and demand forecasting methods for low demand items

Selecting the best periodic inventory control and demand forecasting methods for low demand items

Selecting the Best Periodic Inventory Control and Demand Forecasting Methods for Low Demand Items

Selecting the Best Periodic Inventory Control and Demand Forecasting Methods for Low Demand Items

Selecting the best periodic inventory control and demand forecasting methods for low demand items

The (s,S) form of the periodic review inventory control system has been claimed theoretically to be the best for the management of items of low and intermittent demand. Various heuristic procedures have been put forward, usually justified on the basis of generated data with known properties. Some stock controllers also have other simple rules which they employ and which are rarely seen in the literature. Determining how to forecast future demands is also a major problem in the area. The research described in this paper compares various periodic inventory policies as well as some forecasting methods and attempts to determine which are best for low and intermittent demand items. It evaluates the alternative methods on some long series of daily demands for low demand items for a typical spare parts depot.