Last-in-first-out Research Articles

A comprehensive inventory management model with weibull distribution deterioration, ramp-type demand, carbon emission reduction, and shortages

This study examines the challenges of two warehouses operating under Last-In-First-Out (LIFO) order policies in inventory management, including unpredictable demand patterns, the decay of Weibull distribution, and the need to reduce carbon emissions by adopting green technology. The research addresses various shortage circumstances using advanced inventory modeling techniques to manage ramp-type demand and Weibull distribution deterioration. Additionally, it aims to reduce carbon emissions by incorporating environmentally friendly technologies. By combining advanced inventory modeling with green technology, businesses can effectively manage unpredictable market situations while actively contributing to the global initiative of reducing carbon emissions. It also takes into account various potential backlog scenarios. The proposed model also considers no, partial, and complete shortages and their combinations. This research aims to determine the optimal cycle duration for retailers to increase their total profit while simultaneously investing in green technology. A numerical illustration is provided, and a sensitivity analysis is performed in MATLAB on the optimal solutions concerning parameters to demonstrate applicability.

Managing Replenishment and Clearance of Perishables: Last-In, First-Out (LIFO) Issuing Policy and Age-Sensitive Demand

In supermarkets, customers choose the unit(s) they purchase, which leads to inventory being sold in a last-in, first-out (LIFO) order for perishable products. Furthermore, for such products, the demand for inventory may depend on its age since customers may choose to walk away if the freshest available inventory is too old for them. Despite the widespread occurrence of LIFO-based inventory systems, few studies have analyzed them. In this study, we contribute by developing insights on joint replenishment and clearance policy for a perishable product with a general, finite lifetime using a periodic review model such that the inventory is sold in a LIFO order and demand is age-dependent. The model seeks to optimize two decisions every period: how much of fresh inventory to order and how much of existing inventory to clear. A key objective of the model is to understand the effect of age-dependence of demand on the optimal replenishment and clearance policy. We find that the optimal clearance policy for all but the oldest inventory is a multiindex policy such that between any pair of adjacent indexes, either no inventory is cleared or all the inventory is cleared till the lower index. For the oldest inventory, we show that the optimal policy may have multiple forms depending on how sensitive demand is to inventory’s age. This is a notable result in inventory theory since parameters usually affect only the policy value(s) and not the policy structure. Since the optimal policy has a complex structure in general, which makes it difficult to compute, we develop an efficient heuristic to compute the replenishment and clearance quantities. The heuristic underperforms the optimal policy by [Formula: see text]0.92% on average.

Inventory Information System Using LIFO Method

At one of the goods distribution organizations in Bandung, manual recording problems such as data mistakes, delayed information, and challenges with inventory analysis must be addressed, and here is where the inventory information system comes in. An inventory system must be implemented to improve accuracy and efficiency in inventory management. With the correct technology, the business can reduce data errors, speed up information access, do practical inventory analysis, and increase productivity when taking and maintaining inventory. This study develops a web-based inventory information system using the MySQL database from the XAMPP application, the Laravel framework, and the PHP programming language. Representative data modeling and the creation of user-friendly interfaces are critical components of the design focus, which also considers proper technology selection. In order to better understand user needs and provide answers, system design will use the Object-Oriented Software Engineering (OOSE) model. In order to help with more precise decision-making in inventory management, the Last-In-First-Out (LIFO) method is used in inventory data analysis. Inventory may be easily monitored by using LIFO to offer information on nearly depleted stock and out-of-stock items. One of the Bandung-based goods delivery companies has seen notable gains due to the adoption of the new inventory system. With this new technology, the business may get past problems with manual recording, like data inaccuracies, delayed information, and challenges with inventory analysis. By streamlining the recording process and facilitating more efficient inventory management, this system facilitates better access to inventory information and increases the efficiency of inventory management. In summary, using an inventory system is an excellent way to get around problems with manual goods recording. This solution increases inventory management efficiency and speeds up the recording process for one of the Bandung-based commodities delivery enterprises. Its deployment offers significant advantages, improving the company's overall operating performance and facilitating more effective inventory management.

Dynamic matching with transfers

I study a two-sided market of dynamic matching with sequential arrivals. Agents trade off waiting to match with a more desirable agent with immediately matching with a less desirable agent. I study the consequences of transfer availability for two protocols: first-in-first-out (FIFO) and last-in-first-out (LIFO). For FIFO, welfare is weakly greater with transfers than without. For LIFO, there is a range of waiting costs such that welfare is weakly lower with transfers than without. For sufficiently low waiting costs, the equilibrium welfare for FIFO with transfers remains strictly lower than socially optimal. Thus, unlike in a static matching setting, transfers in my dynamic matching setting do not generate utilitarian efficient outcomes.

Inventory Cost Flow Assumptions and Limitations of Lifo: A Case Study of a Manufacturing Firm in Albania

Valuing inventory at cost is crucial for understanding a firm's expenses, gross profit, taxes, net income, and ending inventory during a specific accounting period. This study focuses on inventory cost flow assumptions under IFRS and U.S. GAAP standards. In the Albanian accounting system, Specific Identification (SI), First In, First Out (FIFO), and Weighted Average Cost (WAC) methods are accepted. Among these, FIFO and WAC are the most commonly used inventory cost flow assumptions. The study also examines the reasons for the ban on Last In, First Out (LIFO) cost flow assumption by IFRS and Albanian accounting standards. An empirical analysis using a case study of a manufacturing firm reveals that the WAC assumption is more favorable, as it results in lower cost of goods sold (COGS) and an ending inventory cost that closely aligns with current market prices.

A hierarchical optimization approach for dynamic pickup and delivery problem with LIFO constraints

We consider a dynamic pickup and delivery problem (DPDP) where loading and unloading operations must follow a last in first out (LIFO) sequence. A fleet of vehicles will pick up orders in pickup points and deliver them to destinations. The objective is to minimize the total over-time (that is the amount of time that exceeds the committed delivery time) and total travel distance. Given the dynamics of orders and vehicles, this paper proposes a hierarchical optimization approach based on multiple intuitive yet often-neglected strategies, namely what we term as the urgent strategy, hitchhike strategy and packing-bags strategy. These multiple strategies can dynamically adapt to dispatch orders to vehicles according to the status of orders and by considering the travel distance and overtime. To account for the LIFO constraints, block-based operators are designed to schedule the delivery routes, thereby enhancing the search efficiency. The result on real-world instances shows that our proposed hierarchical optimization approach outperforms the current practice and the winning approach in an international competition. Finally, the insights gained from generated instances shows the hierarchical optimization approach has broader applicability.

AT-specific DNA visualization revisits the directionality of bacteriophage λ DNA ejection.

In this study, we specifically visualized DNA molecules at their AT base pairs after in vitro phage ejection. Our AT-specific visualization revealed that either end of the DNA molecule could be ejected first with a nearly 50% probability. This observation challenges the generally accepted theory of Last In First Out (LIFO), which states that the end of the phage λ DNA that enters the capsid last during phage packaging is the first to be ejected, and that both ends of the DNA are unable to move within the extremely condensed phage capsid. To support our observations, we conducted computer simulations that revealed that both ends of the DNA molecule are randomized, resulting in the observed near 50% probability. Additionally, we found that the length of the ejected DNA by LIFO was consistently longer than that by First In First Out (FIFO) during in vitro phage ejection. Our simulations attributed this difference in length to the stiffness difference of the remaining DNA within the phage capsid. In conclusion, this study demonstrates that a DNA molecule within an extremely dense phage capsid exhibits a degree of mobility, allowing it to switch ends during ejection.

A TWO-WAREHOUSE INVENTORY MODEL WITH REWORK PROCESS AND TIME-VARYING DEMAND

A two-warehouse inventory model with deteriorating items and rework process with time varying demand rate is presented. The Last-In-First-Out (LIFO) and First-In-First-Out (FIFO) policies are considered with the assumption that the holding cost is higher in the rented warehouse (RW) compared to the owned warehouse (OW). The aim of the proposed model is to determine the optimum values of time in a production cycle that will minimise the total relevant cost, TRC*. We have utilised Microsoft Excel Solver as a solution tool, in which the generalised reduced gradient (GRG Nonlinear) method has been chosen as the solving method. The result is further verified using the built-in function in the Mathematica software. We observed that given same changes are made to the parameters in both the LIFO and FIFO systems, a lower total relevant cost, TRC* is obtained in the LIFO system. This shall mean that the LIFO system is less expensive than the FIFO system, provided that the holding cost in RW is higher than the holding cost in OW. The flow of inventory in the LIFO system suggests that items stored last in the owned warehouse will be dispatched first. This is an important factor for manufacturers in ensuring that items are distributed at optimal freshness.

Inventory Management Analysis in Creating Corporate profits

This research was conducted at PT. Ade Sultra Persada which is located on Jl. Y. Wayong ByPass Lepo-lepo, Kendari, Southeast Sulawesi. The object of this research is inventory management in creating profit that uses in addition to the period order quantity method, it also uses the last-in-first-out (LIFO) method. Sources of data used are observations and interviews of tax and accounting staff. Secondary data sources are income statements and cash flow statements, lists of assets, depreciation, and annual tax returns. The analytical method used in this research is descriptive analysis. inventory management PT. Ade Sultra Persada The gas station unit is not in accordance with the applicable Organizational Work Procedures (TKO). The fuel supply storage depot must be strategically located, in order to avoid flooding like several years ago, to anticipate the occurrence of losses caused by the fuel distribution pipe not being managed properly, it is still necessary to check periodically to avoid fuel evaporation caused by the pipe, when to fill up supplies. The fuel in the depot has not been controlled properly, resulting in the company experiencing losses or decreasing profits due to evaporation

Sustainable inventory management based on environmental policies for the perishable products under first or last in and first out policy

<p style='text-indent:20px;'>Holistic, quality, and sustainability-based inventory policy for perishable items may prove to be a game-changer amid great concern over the carbon footprint and global economic crises. In this paper, first-in-first-out (FIFO) and last-in-first-out (LIFO) dispatching policies have been used to examine the effect of quality on fresh products' sales mind the sustainability concern. The quality of these products worsens with age. Its deterioration rate follows two parameters, Weibull distribution, as it gives better flexibility for various items subjected to deterioration and demonstrates fitness for a range of shape and scale parameters. An adequate preservation effort such as controlled atmosphere (CA) storage and modified atmosphere packaging (MAP) techniques must be applied during storage and handling to maintain such products' quality. Consumers' purchasing behavior and curiosity towards quality and selling price drive these products' demand variability. The demand has been considered a function of quality and unit selling price of the item. The effect of inflation viz. time value of money has also been taken into account, affecting Authoritarian governments' norms on carbon emission control in taxation have also been considered essential for modern sustainable inventory policies. The present models determine the unit price and lot size for the maximum average profit of the system. The behaviors of models have been investigated using a comprehensive sensitivity analysis that offers important operative implications. It has been found that the first-in-first-out model is more profitable as compared to the last-in-first-out model, the price is very rigid, and little flexibility in price drastically decreases the profit. Backlogging has no impact on the selling price of products and order quantity but reduces the average yield because of carbon emission taxation.</p>

Dynamic pickup and delivery problem with transshipments and LIFO constraints

Dynamic pickup and delivery problem which involves random orders, transshipment locations selection and last-in-first-out (LIFO) constraints, brings more challenges for distribution optimization and routing planning. Therefore, we first construct a multi-objective mathematical model for dynamic pickup and delivery problem with transshipments and LIFO constraints (DPDPTL) aiming at the shortest driving distance and the highest order satisfaction. Especially to get a feasible scheme at transshipment locations, the synchronization time of vehicles arriving is considered to obtain the overlapping time windows of order transshipment. Furthermore, in terms of LIFO constraint which easily causes the obtained scheme infeasible and the solution falling into local optimum, an improved heuristic algorithm is proposed to improve the solution quality, where the initial solution generated by Clarke-Wright (CW) saving algorithm as the input is incorporated into adaptive large neighborhood search (ALNS), and Q-learning is adjusted the operator weights to improve solving efficiency. Finally, the experiments are operated to verify the validity of model and the superiority of algorithm. The results show that there is a dilemma between driving distance and order satisfaction, longer driving distance often leads to higher satisfaction, while shorter driving distance leads to lower satisfaction. DPDPTL proposed in this paper is widespread in large-scale distribution, by combining or splitting similar orders, transshipment can improve the delivery efficiency and get better solutions.

FPGA architecture to perform symmetric extension on signals for handling border discontinuities in FIR filtering

This paper proposes a generalized hardware architecture for performing the half-sample and whole-sample symmetric/anti-symmetric extension of a finite-length signal to handle the effect of border discontinuities. The symmetric extension is performed by duplicating the required number of data samples at the boundary followed by reflection about the symmetry axis. The proposed architecture comprises of a shift register that directly streams the data samples, and last-in-first-out (LIFO) buffers to duplicate the data samples at the boundaries. The symmetrically extended signal is obtained by selectively passing the outputs of shift register and LIFO buffers at required instants. Further, a two’s complement circuit is used to invert the data samples at the boundary to perform anti-symmetric extension. The proposed architecture is implemented on field-programmable gate-array (FPGA) to perform the half-sample symmetric extension to favor the low pass Daubechies-8 finite-impulse response (FIR) filter and whole-sample extension for bi-orthogonal 9 × 7 FIR filter with 9 low-pass and 7 high-pass coefficients. The implementation results show that the proposed architecture achieves a maximum operating clock frequency of 275 MHz and 300 MHz for half-sample and whole-sample extension, respectively. The resource utilization of proposed router architecture is approximately reduced by a factor of L (filter length) compared to the conventional router architectures that performs the signal extension by dynamically routing the data samples to the computational part of the FIR filter. The proposed architecture is compatible to various filter architectures without any speed penalty on the performance of FIR filters.

A metaheuristic for the double traveling salesman problem with partial last‐in‐first‐out loading constraints

AbstractThis paper addresses the double traveling salesman problem with partial last‐in‐first‐out (LIFO) loading constraints. A vehicle picks up items in the pickup area and loads them into its container, a horizontal stack. Once all the pickup operations are done, the vehicle can deliver the items to the delivery area because pickup‐and‐delivery areas are geographically separated. Additionally, the loading and unloading operations also follow a partial LIFO policy. The aim is to find a Hamiltonian cycle that satisfies the loading and unloading policy described above in order to minimize the distance traveled by the vehicle in both areas and the cost of the rearrangements that are performed across the routes. The state‐of‐the‐art algorithm finds good solutions to the existing instances. However, finding good‐quality solutions in large instances requires longer computing times. Therefore, we propose an iterated local search that considers two additional components to the classical components, the intensification and restart procedure. The first helps in a more thorough examination of promising neighborhoods, while the second helps in the complete diversity of the solutions in the hopes of escaping the local optima. Moreover, a new unique decoding approach for the solution representation is proposed to effectively generate feasible solutions. The proposed algorithm's performance is evaluated on an existing set of instances and in two new additional sets of larger instances that are proposed. The computational results obtained on the three test sets show that the proposed algorithm outperforms the state‐of‐the‐art algorithm in terms of computing time required to find good‐quality solutions.

Product Life Cycle Lengths and Real Activities Management

We examine how short product life cycle lengths constrain real activities management among a group of manufacturing firms. Companies with short-cycle products face fast product obsolescence and constant pressure to develop and market new products. Fast product obsolescence increases the cost of overproduction, while constant pressure for new product development increases the cost of opportunistic spending cuts. We predict and find evidence that in general, short-cycle firms do not beat earnings thresholds through overproduction and discretionary spending cuts as extensively as long-cycle firms do. However, since the Sarbanes-Oxley Act came into effect, short-cycle firms have increasingly overproduced, and there is no longer a difference in overproduction between short- and long-cycle firms. Moreover, the presence of major customers attenuates the relationship between product life cycle lengths and real activities management, while a large market share and high fixed-assets intensity both accentuate the relationship. Finally, short-cycle firms are also less likely to use LIFO (last-in, first out) liquidation to inflate earnings than long-cycle firms.

Stationary workload and service times for some nonwork-conserving M/G/1 preemptive LIFO queues

We analyze two nonwork-conserving variations of the M/G/1 preemptive last-in first-out (LIFO) queue with emphasis on deriving explicit expressions for the limiting (stationary) distributions of service times found in service by an arrival, workload and a variety of related quantities of interest. Workload is also used as a tool to derive the proportion of time that the system is busy, and stability conditions. In the first model, known as preemptive-repeat different (PRD), preempted customers are returned to the front of the queue with a new independent and identically distributed service time. In the second, known as preemptive-repeat identical (PRI), they are returned to the front of the queue with their original service time. Our analysis is based on queueing theory methods such as the Rate Conservation Law, PASTA, regenerative process theory and Little’s Law (). For the second model we even derive the joint distribution of age and excess of the service time found in service by an arrival, and find they are quite different from what is found in standard work-conserving models. We also give heavy-traffic limits and tail asymptotics for stationary workload for both models, as well as deriving an implicit representation for the distribution of sojourn time by introducing an alternative effective service time distribution.

Demystifying LIFO: Towards Simplification of Inflation-Adjusted Inventory Valuation

The last in, first out (LIFO) method of inventory accounting has been available to taxpayers for more than 50 years. The basic concept of LIFO is relatively simple: it reverses the normal assumed flow of costs (first-in, first-out or FIFO) by matching the costs of the latest purchases or production against current-year sales. During periods of rising costs, LIFO generally results in lower taxable income than the FIFO method because current-year increases in inventory costs are charged to cost of goods sold, rather than accumulated in ending inventory. Thus, LIFO provides protection from the effects on taxable income of rising inventory costs (hereinafter "inflation").Like many other aspects of federal income tax law, the practical implementation of LIFO has often proved to be a complex task. Particularly difficult issues have arisen over the fundamental concept of measuring costs on a comparable basis over time for inventory affected by stylistic, technological, or other changes. The tax law currently lacks objective, determinate standards for ascertaining the proper LIFO cost of inventory items that change. Current standards rely on concepts of similarity, involving fine distinctions that are difficult to apply consistently and resulting in costly and burdensome controversies, with little promise of more determinate results. Moreover, efforts to simplify LIFO have not freed taxpayers from mystifying complexity in this area, as the available simplified methods are far from simple and are not viable alternatives for many taxpayers.

A branch‐and‐cut algorithm for the pickup‐and‐delivery traveling salesman problem with handling costs

AbstractIn the Pickup‐and‐Delivery Traveling Salesman Problem with Handling Costs (PDTSPH), a single vehicle has to satisfy multiple customer requests, each defined by a pickup location and a delivery location. Cargo handling is performed at the rear end of the vehicle, in a Last‐In‐First‐Out (LIFO) order for PDTSPH. However, additional handling operations are permitted with a penalty if other loads that block the access to the delivery have to be unloaded and reloaded. The objective of PDTSPH is to minimize the total transportation and handling cost. In this paper, we present a new Mixed Integer Programming (MIP) model and a branch‐and‐cut algorithm to solve PDTSPH. We also present new integral separation procedures to effectively handle the exponential number of constraints in our MIP model. A family of inequalities are introduced to enhance the scalability of our implementation. The performance of our approach is compared with a compact formulation from the literature (Veenstra et al. [21]) in instances ranging from 9 to 21 customer requests. Computational results show our algorithm outperforming the compact formulation in 69% of instances with an average runtime improvement of 57%.

The ground handler dock capacitated pickup and delivery problem with time windows: A collaborative framework for air cargo operations

We study a typical problem within the air cargo supply chain, concerning the transportation of standard Unit Load Devices (ULDs) from freight forwarders’ to ground handlers’ warehouses. First, ULDs are picked up by a set of available trucks at the freight forwarders’ premises within a time window. Next, they are delivered to the ground handlers, also within a time window, and discharged according to a Last In First Out (LIFO) policy. Due to space constraints, ground handlers have limited capacity to serve the trucks and waiting times may arise, especially in case freight forwarders do not coordinate their operations. Therefore, in this paper we consider a cooperative framework where this transportation is coordinated by a central planner. The goal of the planner is to find a proper routing and scheduling that minimizes the sum of the transportation and waiting times at the ground handlers’ warehouses, while satisfying the capacity of the trucks. We propose two mathematical formulations, one based on the routing and the other based on the packing aspect of the problem. To solve large instances of the problem, an Adaptive Large Neighborhood Search algorithm is also developed. With numerical experiments, we compare the performances of the two models and the metaheuristic, and we quantify the benefits of the proposed framework to reduce waiting times.

The pickup and delivery problem with time windows, multiple stacks, and handling operations

In this paper, we introduce, model and solve the pickup and delivery problem with time windows, multiple stacks, and handling operations (PDPTWMS-H). In the PDPTWMS-H, a fleet of vehicles based at a depot is used to complete a set of requests which consist of transporting items from a pickup location to a delivery location. The vehicles have multiple stacks operated using last-in-first-out (LIFO) loading which requires the vehicle to be rear-loaded and items can only be unloaded if they are closest to the back door. In the PDPTWMS-H, additional handling operations, referred to as rehandling, are allowed and an additional handling time might be incurred when rehandling items (by unloading and reloading items). The problem consists of determining the number of vehicles and the vehicle routes needed to complete the set of requests at minimal cost while respecting the possible handling operations. We model the PDPTWMS-H with a set-partitioning formulation and resort to branch-price-and-cut (BPC) for its solution. To solve the pricing problem, we derive a unified labeling algorithm that is able to cope with the different rehandling possibilities. The labeling algorithm keeps track about the information of on-board items such that symmetries with respect to both stacks and item positions are reduced. Extensive tests are performed on benchmark instances to assess the performance of the proposed BPC methodology and to provide insights on the impact of the rehandling flexibility on solution quality and time.

Instability of LIFO queueing networks

Under the last-in, first-out (LIFO) discipline, jobs arriving later at a class always receive priority of service over earlier arrivals at any class belonging to the same station. Subcritical LIFO queueing networks with Poisson external arrivals are known to be stable, but an open problem has been whether this is also the case when external arrivals are given by renewal processes. Here, we show that this weaker assumption is not sufficient for stability by constructing a family of examples where the number of jobs in the network increases to infinity over time. This behavior contrasts with that for the other classical disciplines: processor sharing (PS), infinite server (IS) and first-in, first-out (FIFO), which are stable under general conditions on the renewals of external arrivals. Together with LIFO, PS and IS constitute the classical symmetric disciplines; with the inclusion of FIFO, these disciplines constitute the classical homogeneous disciplines. Our examples show that a general theory for stability of either family is doubtful.