Inventory Problem Research Articles

Sustainable solutions analysis of a bi-objective green inventory routing problem with heterogeneous fleet and different types of fuels

Sustainable solutions analysis of a bi-objective green inventory routing problem with heterogeneous fleet and different types of fuels

Optimizing omnichannel retailer inventory replenishment using vehicle capacity-sharing with demand uncertainties and service level requirements

This study explores an inventory replenishment problem for an omnichannel retailer selling a product with demand uncertainties and service level requirements in different channels through a capacity-sharing strategy. The omnichannel retailer allows customers to order products online and then pick them up in retail stores. The capacity-sharing strategy is considered to reduce travel costs when the omnichannel retailer delivers products in different channels. With the capacity-sharing strategy, some products can be transferred from the online delivery vehicle to the retail store delivery vehicle. The expectation and the bounds on the online route travel cost of the capacity-sharing strategy are determined, based on which the capacity-sharing rules are established. Optimization models and a solution procedure are developed to solve the omnichannel retailer inventory replenishment problem. To ensure adequate performance, service level requirements are considered when making delivery decisions. Numerical experiments using randomly generated and artificial instances are performed to verify the effectiveness and practicality of the proposed optimization models and the solution procedure. The results show that the proposed models and solution procedure can effectively reduce travel costs, and can provide effective schedule and delivery quantity decision support for omnichannel retailers.

Examination of the Relationship between Metacognitive Awareness and Problem Solving Strategies

In order to effectively learn physics, students need to be actively involved in problem solving. The purpose of this study was to evaluate the relationship between students’ metacognitive awareness and their problem solving strategies in physics. Conceptual framework was based on Polya’s problem solving strategies and Shraw and Dennison’s metacognitive awareness theory. Correlational design including both quantitative and qualitative data collection methods was carried out for this research. The participants of the research consisted of 30 high school students. Data were collected by using Metacognitive Awareness Inventory and Physics Problem Solving Assessment. The first conclusion drawn from this research is that high school students have high metacognitive awareness. Second, students are not very good at in physics problem solving strategies. Third, metacognitive awareness can have impact on problem solving strategies in physics. Fourth, looking back-reflection stage is the problem solving strategy that is affected by the dimensions of metacognitive awareness most. Finally, procedural knowledge and monitoring dimensions of metacognitive awareness have critical roles in physics problem solving strategies. Reflection of metacognitive awareness to problem solving strategies and especially using procedural knowledge and monitoring during problem solving are important strategies that need to be emphasized and encouraged in teaching and learning science.

Two-Warehouse Inventory Model: Interval Valued Costs, Advanced Payment, Stock-Dependent Demand, and Partial Backlogging

A great deal of inventory costs are typically unpredictable because of the unpredictability of a competitive market. The fluctating demand from customer's and the unpredictability of the market economy make it difficult for researchers and operation research practitioners to appropriately replicate an inventory problem. In order to prevail this type of unpredictability circumstances, in this paper, we have represented the inventory parameters as interval. Using this concept, we progressed a two-warehouse inventory model for deteriorating items with a fixed Shelf life, partially backlogging shortages and interval-valued deterioration rate. In addition to this the parameters like ordering cost, purchase cost, shortage cost, deterioration cost for both the rented and owned warehouse except the backlogging parameter have been considered as interval-valued. The uncertainty in the inventory parameters motivated us to view them as interval-valued in the present research. Based on the assumptions, the cost function of this problem is a highly nonlinear constraint optimization problem. Mathematica is used to tackle this nonlinear optimization problem. A numerical example has been presented to demonstrate the computational result. Then, a sensitivity analysis has been performed to study the effect of changes of different parameters of the model on the optimal policy.

A Probabilistic Inventory Problem for Imperfect Quality with Partial Shortage Backordering, Carbon Emission, and Its Computation Using Genetic Algorithm in Python

A Probabilistic Inventory Problem for Imperfect Quality with Partial Shortage Backordering, Carbon Emission, and Its Computation Using Genetic Algorithm in Python

Data Farming the Parameters of Simulation-Optimization Solvers

The performance of a simulation-optimization algorithm, a.k.a. a solver, depends on its parameter settings. Much of the research to date has focused on how a solver’s parameters affect its convergence and other asymptotic behavior. While these results are important for providing a theoretical understanding of a solver, they can be of limited utility to a user who must set up and run the solver on a particular problem. When running a solver in practice, good finite-time performance is paramount. In this paper, we explore the relationship between a solver’s parameter settings and its finite-time performance by adopting a data farming approach. The approach involves conducting and analyzing the outputs of a designed experiment wherein the factors are the solver’s parameters and the responses are assorted performance metrics measuring the solver’s speed and solution quality over time. We demonstrate this approach with a study of the ASTRO-DF solver when solving a stochastic activity network problem and an inventory control problem. Through these examples, we show that how some of the solver’s parameters are set greatly affects its ability to achieve rapid, reliable progress and gain insights into the solver’s inner workings. We discuss the implications of using this framework for tuning solver parameters, as well as for addressing related questions of interest to solver specialists and generalists.

Rancang Bangun Sistem Inventory (Studi Kasus: UD. Asia Pratama Pekanbaru)

UD Asia Pratama Pekanbaru, a trading company in the building industry, is facing problems in managing inventory which is still manual, causing inefficiencies and inaccurate data. This research aims to design and implement an efficient and effective web-based inventory management system. The system development method uses the Waterfall model, including requirements analysis, design, implementation and testing. Data was collected through interviews with employees and analysis of documents related to inventory. The research results show that the system developed can increase inventory management efficiency, with real-time stock management features, recording of incoming and outgoing goods, and accurate reporting. User testing and evaluation shows that the system is acceptable and meets UD Asia Pratama's needs in overcoming inventory problems.

Tackling inventory losses of small accessories in engineering manufacturing: a case study of Euro Manufacturing

Learning outcomes After completion of the case study, the students will be able to understand ABC analysis and develop a systematic approach using PDCA, analyze processes, technology, employee training and supplier relationships when analyzing shrink and developing solutions, evaluate how technology improves production inventory control and visibility and recognize the importance of fostering a culture of employee accountability and ownership to minimize inventory loss and improve overall operational efficiency. Case overview/synopsis On June 2, 2023, sitting in his office in Karachi, Pakistan, Khan Aamir, the manager of store and inventory at Euro Manufacturing, found himself immersed in a cloud of confusion. The incessant loss of inventory items, particularly the nut bolts and small accessories, had become a perplexing challenge. To address these losses and provide a cycle count report to the director of supply chain, Aamir, manager of store and inventory, was given the responsibility to take action. He was looking for a comprehensive approach to address the current problems and prevent further losses in the future. This case study examines the various reasons for the losses, including theft, inadequate inventory control methods, human error and problems with suppliers. It highlights the importance of established procedures, the use of technology (such as barcode scanning, radio-frequency identification tagging and inventory management software) and the cultivation of a culture of accountability among employees. Complexity academic level This case study is developed for class discussion in the course of operations management or supply chain management. This case study is suitable for use with undergrad students. This case study can be taught in a module on operations management or supply chain management, as part of a broader course in business management or industrial engineering. Supplementary materials Teaching notes are available for educators only. Subject code CSS: 9: Operations and logistics.

Optimizing Vendor-Managed Inventory in Multi-Tier Distribution Systems

This paper addresses the concept of the vendor-managed inventory (VMI) problem, focusing on the replenishment policy and the vehicle routing problem (VRP) model. These components are integrated to tackle a three-echelon distribution issue comprising a single plant, multiple depots, and multiple retailers, with the primary objective of minimizing transportation and inventory costs within this complex distribution network. A three-phase methodology is proposed to optimize the entire supply chain, from the plant to the final retailer, and its performance is evaluated through computational experiments. This research is motivated by a real-life supply network, highlighting its practical relevance and applicability. To extend the capabilities of existing methods for solving the combined inventory and routing problem, an insertion heuristic is incorporated to enhance vehicle utilization, thereby reducing total costs. Computational results demonstrate the effectiveness of the improved algorithm, indicating that it is sufficiently robust for practical application. Significant cost savings can be achieved with the proposed approach, making it a valuable contribution to the field of supply chain optimization.

Supervised learning for integrated forecasting and inventory control

We explore the use of supervised learning with custom loss functions for multi-period inventory control with feature-driven demand. This method directly considers feature information such as promotions and trends to make periodic order decisions, does not require distributional assumptions on demand, and is sample efficient. The application of supervised learning in inventory control has thus far been limited to problems for which the optimal policy structure is known and takes the form of a simple decision rule, such as the newsvendor problem. We present an approximation approach to expand its use to inventory problems where the optimal policy structure is unknown. We test our approach on lost sales, perishable goods, and dual-sourcing inventory problems. It performs on par with state-of-the-art heuristics under stationary demand. It outperforms them for non-stationary perishable goods settings where demand is driven by features, and for non-stationary lost sales and dual-sourcing settings where demand is smooth and feature-driven.

Data‐driven inventory control involving fixed setup costs and discrete censored demand

AbstractWe investigate a data‐driven dynamic inventory control problem involving fixed setup costs and lost sales. Random demand arrivals stem from a demand distribution that is only known to come out of a vast ambiguity set. Lost sales and demand ambiguity would together complicate the problem through censoring, namely, the inability of the firm to observe the lost portion of the demand data. Our main policy idea advocates periodically ordering up to high levels for learning purposes and, in intervening periods, cleverly exploiting the information gained in learning periods. By regret, we mean the price paid for ambiguity in long‐run average performances. When demand has a finite support, we can accomplish a regret bound in the order of which almost matches a known lower bound as long as inventory costs are genuinely convex. Major policy adjustments are warranted for the more complex case involving an unbounded demand support. The resulting regret could range between and depending on the nature of moment‐related bounds that help characterize the degree of ambiguity. These are improvable to when distributions are light‐tailed. Our simulation demonstrates the merits of various policy ideas.

Cost Allocation for Inventory Problem with Transportation Discount under Carbon Tax Policy

Cost Allocation for Inventory Problem with Transportation Discount under Carbon Tax Policy

Inventory pricing model under different carbon emission policies

Considering carbon emission in inventory management model is a hot topic in current academic circles. Studying inventory pricing can provide managers with better managerial insights. Economic order quantity model mainly solves the inventory management problem of demand determination. However, due to the complex market environment in real life, it is difficult to meet the conditions of constant demand. In addition, with the enhancement of low-carbon awareness of enterprises, the study of pricing model based on low-carbon policies has also attracted the attention of a large number of scholars. The robust optimization theory is applied, the inventory pricing model of demand fluctuation under carbon cap, carbon tax and carbon cap-and-trade policies is considered, the robust equivalence form of the problem is studied. The profit comparison under different policies is given. Numerical experiments show that the carbon cap-and-trade policy can better control the carbon emission of enterprises.

An analysis of multi-agent reinforcement learning for decentralized inventory control systems

Most solutions to the inventory management problem assume a centralization of information that is incompatible with organizational constraints in supply chain networks. The problem can be naturally decomposed into sub-problems, each associated with an independent entity, turning it into a multi-agent system. A decentralized solution to inventory management using multi-agent reinforcement learning (MARL) is proposed where each entity is controlled by an agent. Three multi-agent variations of the proximal policy optimization algorithm are investigated through simulations of different supply chain networks and levels of uncertainty. A framework is deployed, which relies on offline centralization during simulation-based policy identification but enables decentralization when the policies are deployed online to the real system. Results show that reducing information sharing constraints in training enables MARL to perform comparatively to a centralized learning-based solution when deployed, and to outperform a distributed model-based solution in most cases, whilst respecting the information constraints of the system.

Pengoptimalan Persediaan Bahan Baku Utama Untuk Mengatasi Tantangan Industri Menggunakan Metode Just In Time (JIT) Dan Silver Meal Heuristic Pada PT. Indonesia Plafon Semesta

The inventory problem that still occurs at PT Indonesia Plafon Semesta is the accumulation of resin and calcium raw materials which results in considerable losses. PT Indofon began to experience a buildup of resin raw materials in August amounting to 16,100Kg and for calcium amounting to 15,200Kg so that each month it soared until its peak in October, namely a buildup of 23,500Kg of resin, and 23,200Kg of calcium. The use of the right method for inventory control problems is Just In Time , which is a management strategy used to optimize the production process and product delivery by buying and producing only when needed and at the right time. Silver Meal Heuristic, a heuristic approach used in inventory control to determine the optimal lot size. The quantity of raw material orders using JIT is relatively more than the silver meal, but with JIT the supplier only needs to send 4-5 times, meaning that the company can save costs. The quantity and frequency obtained from data processing using silver meal is more optimal. However, the cost of raw material inventory incurred is quite large. For a decrease in efficiency costs by implementing a policy of making purchases with Just In Time (JIT) and is expected to save on raw material inventory costs. So that these cost savings can be allocated by the company to other costs.

An extension of Pontryagin Maximum principle in interval environment and its application to inventory problem

The control theory is one of the most fundamental branches of engineering as it implicates solving abilities of numerous non-linear engineering design problems efficiently. Again, Pontryagin’s maximum principle is one of the most salient topics of control theory as it is involved in solving various important problems. However, in the current highly complex situation, most of such real-life problems appear to be highly uncertain/imprecise in nature. Consequently, in order to analyse such problems accurately, uncertainty/flexibility of such problems cannot be overestimated. Motivating from this fact and as a necessity, in this study, the Pontryagin’s maximum principles are extended in interval environment for interval valued control problems (IVCPs). In this context, an IVCP is defined along with different formal terminologies. Further, the necessary and sufficient optimality conditions (i.e., Pontryagin’s maximum principles) are extended for IVCPs using existing interval ranking proposed by Bhunia and Samanta (2014). Further, in the second part of this work, in order to test the effectiveness of the proposed theories, an economic order quantity (EOQ) model is developed by considering dynamic servicing strategies in interval environment. With the help of numerical example, the proposed extension of Pontryagin’s maximum principles for IVCPs is well validated.

Modeling a closed-loop inventory routing problem for returnable transport items under horizontal logistics collaborations and dynamic demand structure

ABSTRACT This paper addresses a closed-loop inventory routing problem with multiple suppliers, products, and periods under horizontal collaboration assumptions. Our problem encompasses various decision aspects, including routing, inventory management, product delivery, returnable transport item collection and cleaning. We analyze various logistics collaboration scenarios. The effects of demand dynamicity are also assessed. The problem has been mathematically defined as a Mixed Integer Linear Programming model. A rolling horizon approach and a hybrid heuristic algorithm are proposed for instances that exceed the computational requirements of solving the exact MILP model. The applicability and potential benefits of the MILP model and the proposed solution methodologies are demonstrated through a base case and additional numerical analyses on larger-sized instances and networks. The results show that supplier collaboration significantly reduces routing costs, while customer collaboration reduces inventory costs. Numerical comparisons reveal that the proposed algorithms outperform the MILP model for large-scale problem instances.

Economic order quantity for substitutable growing items

ABSTRACT In the realm of inventory management, the role of substitution in managing growing items is crucial. While several studies have examined growing and substitutable items separately, none have addressed replenishment policies for inventory systems with substitutable growing items. This paper introduces a multi-item inventory management problem for growing items with potential demand substitution. Two cases are defined and mathematically modeled. Due to the nonlinearity of the constrained models, a grid search heuristic algorithm is proposed as the solution methodology. The algorithm’s performance is compared with genetic and simulated annealing algorithms, two state-of-the-art metaheuristics. The models and solution approach are evaluated through numerous numerical examples, demonstrating the grid search heuristic algorithm’s superiority in quality and computational time. Sensitivity analyses are conducted to assess the impact of variations in input parameters on the objective function. Managerial insights are derived from the results, and the paper concludes with directions for future research on both the problem and solution methodology.

Uncertain random optimal control model for deteriorating inventory with the finite horizon

This paper examines a deteriorating inventory model with combined pricing and production during a limited selling time in an uncertain random setting. Demand is influenced by market size and price, which are portrayed in terms of random variables. The uncertainty factors affecting the inventory are represented by multiple Liu processes. Inventories deteriorate by a certain percentage and back-orders for inventories are permitted. This study aims to determine the optimal level of production rate and pricing that maximizes the producer’s total expected profit through the application of uncertain random optimal control theory, which is based on uncertain theory and probability theory. Numerical examples are used to compare joint pricing dynamic production model with dynamic production model under an optimal static price, highlighting the benefits of implementing joint pricing dynamic production model. Furthermore, it is evident that an increase in deterioration rate results in decreased on-hand inventory, yet higher production rate and price. It should also be noted that different levels of probability expectation of demand can have an impact on production inventory problem.

A Bayesian approach to data-driven multi-stage stochastic optimization

Aimed at sufficiently utilizing available data and prior distribution information, we introduce a data-driven Bayesian-type approach to solve multi-stage convex stochastic optimization, which can easily cope with the uncertainty about data process’s distributions and their inter-stage dependence. To unravel the properties of the proposed multi-stage Bayesian expectation optimization (BEO) problem, we establish the consistency of optimal value functions and solutions. Two kinds of algorithms are designed for the numerical solution of single-stage and multi-stage BEO problems, respectively. A queuing system and a multi-stage inventory problem are adopted to numerically demonstrate the advantages and practicality of the new framework and corresponding solution methods, compared with the usual formulations and solution methods for stochastic optimization problems.