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After the expiration of each lease, lessees may return leased equipment on schedule, renew the lease or purchase it according to the equipment status and their demands. By considering lessees' uncertain options and equipment status difference, the pricing and inventory decision-makings of leased equipment are explored. A mixed-integer nonlinear programming model by maximising the net present value of lessor's profit is presented with respect to constraints like rental revenue, manufacturing, lessees' credit check, transportation, maintenance, upgrade and inventory costs. The rental price and inventory decisions are obtained by solving the problem with a particle swarm optimisation algorithm. We also analyse the impacts of purchasing cost of old equipment from a third-party supplier on lessor's inventory, renewal price or purchasing price of leased equipment on lessees' options and lessor's profit. The results show: (1) with the extension of lease period, the rental price increases while the growth rate decreases; (2) the maintenance cost accounts for about 20% of total cost, and the preventive maintenance strategy can reduce excess maintenance cost as lease period increases; (3) the lessor shall set moderate renewal price discount coefficient and purchasing price coefficient, and analyse purchasing cost of old equipment to manage inventory timely. [ABSTRACT FROM AUTHOR]

Considering the growing green awareness and increasingly stringent emission regulations, heavy-emitting supply chains are required to re-schedule their operations for environmental responsibility. Although coordination helps the supply chains overcome the decentralised disadvantages to achieve desirable profits, literature considering option contracts under emission constraints, especially combining warehousing contracts, remains scarce. This paper fills this research gap with the novelty in the dual-contract-coordinated decision analysis for achieving profit maximum and emission reduction targets considering customers' green awareness through option and warehousing contracts, based on the originality of using the Lagrange-Stackelberg optimisation method, which overcomes the difficulty in expressing the first-mover's decisions and simplifies the problem-solving process. Analytical and numerical results show that Pareto-efficient coordination can be fully achieved by the option contract if the warehousing contract ensures the same inventory costs before and after coordination. Otherwise, partial coordination also raises insiders' profitability only through the option contract. Purchasing extra emissions with green investment is the best in most cases. The contract-maker should deliberate its contract settings including the option and wholesale prices, as well as warehousing, to develop Pareto-efficient coordination. Sustainability comes at a cost, but coordination raises profitability and emission mitigation in a well-built ETS market. [ABSTRACT FROM AUTHOR]

The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. An effective response to humanitarian emergencies is often associated with excess inventories, which leads to high costs. Henceforth, using accurate demand forecasts to control the inventories of these medical items is shown to be a significant lever to reduce inventory costs while keeping high service levels. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with items characterised with intermittent demand patterns. Moreover, we propose a new hybrid deep learning method that combines the long short-term memory neural network and the support vector regression. Such combination enables to deal with complex and non-linear data. We conduct an empirical investigation by means of data related to 523 medical items managed in two warehouses of a major humanitarian organisation. The forecast accuracy and the inventory efficiency of the methods are analysed. The empirical results show the high performance of the deep learning methods. These insights are valuable not only in the context of medical items in humanitarian operations, but also for any items with intermittent demand patterns. [ABSTRACT FROM AUTHOR]

Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

This study deals with a closed-loop supply chain where inventory levels are controlled by an order-up-to inventory policy. The system under consideration is the cylinder-packaged gas supply chain of Air Liquide company, where empty cylinders used by customers are returned to be filled again by company plants. First, we examine the goodness of fit for demand distributions based on company real data. This enables us to better characterise demands pertaining to different classes of products. Then, we formulate the multi-echelon serial inventory model to be optimised and propose a heuristic to compute the target inventory levels that helps in achieving the desired customer service level while minimising the total inventory cost. The proposed heuristic is easy to implement in the field and gives results close those obtained using a simulation-optimisation approach that is more time-consuming. Finally, we perform a numerical analysis based on company real data and compare several methods that can be used to compute the target inventory levels by varying mainly two assumptions: parameters regarding demand distributions and metrics used to assess customer service levels. [ABSTRACT FROM AUTHOR]

We study a production lot-sizing problem inspired by a veterinary pharmaceutical plant in which demands are uncertain. First, we develop a deterministic capacitated lot-sizing model for the production of animal pesticides, performed in three machine-specific stages. Second, we propose a traditional robust optimisation formulation following the popular budget-of-uncertainty approach. Third, we derive a novel fragility-based approach that circumvents well-known issues with traditional robust optimisation approaches, such as the estimation of budgets of uncertainty, the over-conservatism of robust solutions and the sensitivity of solutions to the decision maker's risk attitude. The fragility-based approach is grounded in the idea of minimising violations, over the full uncertainty support, from a user-specified cost target. It avoids the estimation of budgets of uncertainty and produces less conservative solutions via explicit modelling of constraint violation. We demonstrate the effectiveness of our approach on instances built upon real data provided by our industrial partner, a major player in the Brazilian veterinary pharmaceutical sector. The results show that our fragility-based approach reduces average total costs across all instances and maintains greater model stability under different target estimations. It also preserves cost savings when bottlenecks are introduced in production and when inventory costs and capacities are varied. [ABSTRACT FROM AUTHOR]

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines' performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains' fleet was improved, and (iii) the daily path was minimized. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to enlarge the scope of existing EOQ-type models by incorporating uncertain product returns that may include time or quantity restrictions. In this inventory control problem, the item's demand is deterministic and occurs at a known constant rate over time. Product returns arrive according to a Poisson process. The amount returned to the seller at each return epoch is a random or fixed fraction of the accumulated number of units (net of previous returns) that have been demanded so far within a stock cycle. The ordering policy is a continuous review policy that orders a fixed quantity every time an order is placed. The replenishment lead time is instantaneous, and shortages are not allowed. The objective is to find optimal order quantities by minimising the long-run average total costs per unit time. Costs include a fixed plus proportional cost per order, and a holding cost per unit held in inventory per unit time. A level-crossing approach is used to derive a closed-form expression for the expected ordering and inventory holding costs as a function of the order size and other relevant parameters. [ABSTRACT FROM AUTHOR]

This paper focuses on the impact of return decisions on the bullwhip effect in the online closed-loop supply chain. We investigate the optimal return modes when the inspection system is undertaken by the logistics centre or the remanufacturer. Then, we optimise the online retailers' return decisions of minimising the inventory cost and bullwhip effect under different supply chain circumstances. Distinctive from previous conclusion that the inspection operation is usually undertaken by the remanufacturer to sort the returned products, the analysis results reveal that the optimal decision of mitigating the bullwhip effect in most cases is to set the inspection system on logistics centre. Besides, consumers' return and exchange behaviours in e-commerce have different impacts on the supply chain efficiency. The product return can not necessarily mitigate the information distortion in online closed-loop supply chain. The research provides insights for managers to strategise about return policies and resource allocation in different e-commerce contexts. [ABSTRACT FROM AUTHOR]

In supply systems, maintaining stock levels is a major challenge. The purpose of this article is to determine the number of orders in a continuous review model. The demand rate is erratic, as it is dependent on promotional advertising on an ambiguous basis. This article considers learning and memory in an unpredictable setting. The fundamental crisp model is extended with a fuzzy formulation, and learning is included. Demand promotion is included with the complete backlog in models, which are based on finite-horizon time scales. The memory present in real-world situations is represented using Caputo's style of nonintegral ordered calculus. It focuses on the percentage of each process that is devoid of scarcity as well as the overall cycles of replenishment. After analyzing each model numerically, a parametric sensitivity analysis is performed to show the trade-offs and efficacy of the decision criteria. Longer memory and higher learning rates have been found to result in a cheaper total cost. The number of fuzzy learning orders and the overall cost shift in an inversely proportionate manner as learning increases.The fuzzy learning case's total cost decreases rather than the fuzzy case's if the differential fractional order rises within the range. The crisp model, which derives minimal total cost, is relevant as a mathematical bound for the other two realistic models that incorporate demand volatility and are the main goals of this simulation. These models ameliorate the burden of implausibly perfect demand knowledge. This simulation is used to analyze the increases in the inventory system's output for each model. With learning, the inventory and total cost are less than those of a pure fuzzy system: the fuzzy model's delta over the crisp limiter is reclaimed by 62% for the order size and by 39% for the cost over the considered horizon. This accurately simulates how experience leads to better decision-making. A move to shorter memory has twin manifestations in order size: differential memory index generates linear reduction with a mean rolling fall of 10.5% per 0.1 change but the integral index causes a stronger (20%) quadratic reduction. For the total cost, their roles are reversed: both cause quadratic increase with shorter memory but the differential index has the stronger impact - a mean rolling rise of 11% per 0.1 change compared to 6.5% for the integral index. [ABSTRACT FROM AUTHOR]

In practice, it is common, especially for online retailers, to bundle different products together during procurement to save transportation and handling costs. It is important to understand and theorize how to manage dynamic procurement by taking advantage of joint ordering in the presence of joint and individual setup costs. In this article, we characterize the structure of optimal policy for a periodic review multiproduct inventory system with multiple setup costs, including a joint setup cost and an individual setup cost for each product. By proposing the notion of ( K K , η) -quasi-convexity, we show that an optimal procurement policy for such a system follows the so-called (σ , ω , S) policy when demands increase stochastically over time: order up to S for states in the region σ , do not order for states in the region ω , and order certain quantities for states in neither σ nor ω. To better understand the optimal policy, we provide the bounds for the optimal order-up-to levels and the boundary sets of the optimal policy. Under the convex single-period inventory costs, we also provide a lower bound deterministic system which can be asymptotically optimal as the coefficient of variations decreases to zero. Leveraging these operational insights, we propose five simple heuristic policies: the independent (s , S) policy, vector (s , S) policy, linear interpolation (s , S) policy, the deterministic approximation, and the weighted deterministic approximation policy. Extensive numerical experiments indicate that the last three heuristics perform well. In particular, the weighted deterministic approximation policy, whose average performance gap is < 1%, dominates the others in almost all our numerical experiments. Finally, we show that how our results can be extended to systems with more complex setup cost functions, such as time-varying, set-based, and quantity-dependent setup costs. [ABSTRACT FROM AUTHOR]

Copyright of Light Industry Machinery is the property of Light Industry Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the present paper, a sustainable supply chain model is investigated with a variable production rate and remanufacturing for the production of defective items under the effect of learning fuzzy theory, where the lower and upper variations in fuzzy demand rate are affected by learning parameters and backorders are also allowed. Our proposed model reveals a springy manufacturing inventory organization that makes various types of items, and imperfect items can be created through the method of manufacturing things in a fuzzy environment. When the screening process is completed, defective items are remanufactured immediately, and a limited financial plan and space limitations are assumed concerning the product assembly. We minimized the total fuzzy inventory cost with different distributions (beta, triangular, double triangular, uniform, and χ2 (chi−square)) concerning the production rate, lot size, and backorder under learning in a fuzzy environment where the costs of screening, manufacturing, carrying, carbon emissions, backorders, and remanufacturing are included. The Kuhn–Tucker optimization technique is applied to solve non-linear equations that are based on some distributions. Numerical examples, sensitivity analysis, managerial insights and observations, limitations, future work, and applications are provided for the validation of our proposed model, and the industrial scope of this proposed work is included. [ABSTRACT FROM AUTHOR]

The Economic Production Quantity (EPQ) plays a crucial role in the manufacturing process. The traditional EPQ model operates under the assumption that Non-Conforming (NC) items are not produced, and the process remains under control. However, real-world scenarios often involve the production of NC products, leading to process deviations. This study introduces a model that optimizes the design of a control chart to incorporate NC products into the EPQ model. The model integrates inventory and quality-related costs to minimize the expected total cost by determining EPQ, sample size, and control limit. The solution methodology employs direct search techniques to solve this problem. The sensitivity analysis results demonstrate the significant cost reduction achieved by the proposed model compared to the classical EPQ model. The numerical examples presented in the study show an average cost reduction of approximately 21 percent, highlighting the potential effectiveness of the model in cost reduction efforts. [ABSTRACT FROM AUTHOR]

Inventory has an impact on the manufacturing process as well as supply chain operations. The fundamental goal of this research paper is to optimize the cost associated with inventories and to provide flow less continuous production process in time. Normally, demand rate of any entity in inventory control model are treated as predictable and at the same time constant too, and that the cost associated to unit inventory must be independent and non-variable in nature. Nevertheless, in practical circumstances, the unit price and demand rate of an entity may be interconnected. When the asking for an article is enormous, an entity is manufactured in huge quantities and the static charges of manufacturing being diffused over a multiple component. Henceforth, per unit article cost decreases significantly. i.e., per unit article cot and the demand of an article are related under inverse variation. So, better to consider the demand rate of an article as a variable constraint than to fixed one. In this research article, a mathematical model for multiple articles through permitted and restricted shortage and per article cost based on demand accompanied by upper and lower limits viz restricted storage space and manufacturing expenses has been constructed. Overall, investigating the simultaneous effect of storage space and manufacturing expenses in an inventory model provides valuable insights that enable cost optimization, resource allocation, capacity planning, and risk mitigation. It helps companies make informed decisions and improve their overall operational efficiency and profitability. The Multi-Entity Stock Dependent Model with Capacity and Manufacture Cost Restraints can be customized and used in a variety of sectors that include managing inventory across numerous entities and complicated supply chain networks. Here are a few examples of industries that can benefit from such a model: manufacturing industry, the retail and distributor sector, e-commerce companies, pharmaceutical and healthcare industry, automotive industry and food and beverage industry. The article cost is explored at this juncture in a fuzzy atmosphere and solutions of the model being obtained through KKT condition. Finally, a conclusion is offered in the final portion. [ABSTRACT FROM AUTHOR]

Analyzing Production-Inventory Systems with General Demand: Cost Minimization and Risk Analytics Frequent production rate changes are prohibitive because of high setup costs or setup times in producing such items as sugar, glass, computer displays, and cell-free proteins. Thus, constant production rates are deployed for producing these items even when their demands are random. In "Production Management with General Demands and Lost Sales," Han, Li, Sethi, Siu, and Yam obtain the optimal constant production rate for a production-inventory system with Lévy demand for long-run average and expected discounted cost objectives, explicitly in some cases and numerically in general with a Fourier-cosine scheme they develop. This scheme can help in computing risk analytics of the inventory system, such as stockout probability and expected shortfall. These measures are particularly significant for assessing supply resilience, especially for emergency products or services like medicines and healthcare equipment. This study's analytical and numerical findings contribute to enhancing efficiency and decision making in production management. We consider continuous-review, single-product inventory systems with a constant replenishment rate, Lévy demand, general inventory holding cost, and general lost-sales penalty. The Lévy demand encompasses various demand dynamics used in the inventory literature. We obtain optimal replenishment rates that minimize the time-average cost and expected discounted costs. We can solve this problem explicitly for the optimal replenishment rate by utilizing the renewal theorem for the time-average cost objective. For a more complex expected discounted cost minimization problem, we first obtain the Laplace transform of the cost objective in terms of the unique positive root of the corresponding Lundberg equation. Then, we devise a Fourier-cosine scheme to numerically compute the original cost objective together with a detailed error analysis to determine the optimal production rate. In particular cases, we obtain closed-form expressions of the optimal replenishment rates. The numerical examples further illustrate our numerical method's accuracy, stability, and robustness. Finally, our Fourier-cosine method can be applied to compute risk analytics, including but not limited to the stockout probability and expected shortfall of the production-inventory system. Funding: S. P. Sethi acknowledges financial support from the Eugene McDermott Chair Professorship. C. C. Siu acknowledges financial support from the Research Grants Council of Hong Kong [Grant "Generalized Sethi Advertising Model and Extensions" with Project UGC/FDS14/P02/20]. S. C. P. Yam acknowledges financial support from [Grant HKGRF-14301321 with Project "General Theory for Infinite Dimensional Stochastic Control: Mean Field and Some Classical Problems" and Grant HKGRF-14300123 with Project "Well-Posedness of Some Poisson-Driven Mean Field Learning Models and Their Applications"]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.0191. [ABSTRACT FROM AUTHOR]

Background: Thailand's logistics costs significantly exceed global averages. This study examines the novel application of a triple-helix model in factory logistics improvement projects in Northern Thailand from 2022 to 2024, addressing industrial logistics inefficiencies. Methods: The project involved 30 factories across various sectors, employing a case study methodology to analyze improvement initiatives in transportation, warehouse/inventory management, and logistics administration. Results: Transportation management interventions yielded up to 25% cost savings and improved delivery performance. Warehouse and inventory management upgrades led to inventory cost reductions of up to 55%. Logistics administrative improvements resulted in up to 20% cost savings and enhanced planning capabilities. Conclusions: The project demonstrates the effectiveness of the triple-helix model in facilitating knowledge transfer and practical improvements in industrial logistics. These findings are valuable for Thai industries, policymakers, and logistics managers globally, offering a blueprint for similar initiatives in developing economies. The success of this approach provides practical insights for optimizing logistics operations, potentially benefiting supply chain stakeholders, economic planners, and researchers seeking to enhance logistics efficiency and reduce costs in various industrial contexts. [ABSTRACT FROM AUTHOR]

A location‐allocation problem faced by a company that aims to locate warehouses to supply products to a set of customers is addressed in this paper. The company's objective is to minimize the total cost of locating the warehouses and the cost due to inventory policies. However, these inventory decisions are made by a different decision‐maker. In other words, once the company makes the location decisions, the decision‐maker associated with each warehouse must determine its own order quantity. Warehouses are allowed to have a certain maximum number of backorders, which represents an extra cost for them. This situation can be modeled as a bilevel programming problem, where the upper level is associated with the company that needs to minimize the costs related to location‐allocation and the total orders of each warehouse. Each warehouse is associated with an independent lower level, in which a warehouse manager aims to minimize the total inventory cost. The bilevel problem results in a single‐objective upper‐level problem with non‐linear, multiple independent lower‐level problems, making it generally challenging to find an optimal solution. A population‐based metaheuristic under the Brain Storm Optimization algorithm scheme is proposed. To solve each non‐linear problem associated with the lower level, the Lagrangian method is applied. Both decision levels are solved in a nested manner, leading to obtaining bilevel feasible solutions. To validate the effectiveness of the proposed algorithm, an enumerative algorithm is implemented. A set of benchmark instances has been considered to conduct computational experiments. Results show that optimality is achieved by the proposed algorithm for small‐sized instances. In the case of larger‐sized instances, the proposed algorithm demonstrates the same efficiency and consistent results. Finally, interesting managerial insights deduced from the computational experimentation and some proposals for future research directions are included. [ABSTRACT FROM AUTHOR]

This study aims to develop a sustainable freight transportation network considering capacitated cross-docks for minimising the overall supply chain costs, including carbon emission cost. The problem is inspired by a major retail company based in India, which would like to expand its product portfolio in the new region. A mathematical model is developed to minimise total costs encompassing transportation cost, pipeline and retailers inventory cost, fixed cost of cross-dock and carbon emission costs. The deterministic time dependant demand, multiple products and multiple sourcing and distribution are some of the challenges faced by the retail industry. A two-level self-adaptive variable neighbourhood search algorithm is applied to solve a computationally complex problem. The results based on a two-level self-adaptive variable neighbourhood search algorithm are compared with the variable neighbourhood search algorithm to test the robustness of the developed model. Results reveal that an increase in retailers over suppliers significantly influences the number of open cross-docks. A multiple-case scenario approach captures the implications of varying capacity on the number of open cross-docks; thus, supporting the freight distribution managers in making sustainability-driven decisions. [ABSTRACT FROM AUTHOR]

In this paper, we aim to investigate a model for a single item production system (economic production quantity) under the assumption that the demand rate is triangular fuzzy number. The other parameters, the rate production, fixed cost production and inventory cost are to be constant. Hence, the length cycle production is fuzzy number. Furthermore, the holding cost per cycle is fuzzy. Here, we need fuzzy integral to compute fuzzy holding cost. Accordingly, the annual total cost is also fuzzy. Actually, we can not formulate membership function for fuzzy holding cost and fuzzy annual total cost. The fuzzy ranking cannot be used in this model. However, we will do it numerically by alpha-cut approach to plot the fuzzy costs. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a sustainable inventory model with a learning effect for imperfect quality items under different kinds of fuzzy environments like crisp, general fuzzy, cloudy fuzzy, and monsoon fuzzy. We divided the mathematical model into three parts under the learning effect according to the real-time fuzzy components (crisp, cloudy, and monsoon environments) of the demand rate of the items. We minimized the total inventory cost with respect to cycle length in each environment under the proposed assumptions. The non-linear optimization technique is applied for the algorithm and the solution method to find the decision variable. Finally, we compared the total inventory cost under different fuzzy environments and our finding is that the fuzzy monsoon environment is a more effective fuzzy environment than crisp and cloudy fuzzy environments. We have presented a numerical example for the validation of the proposed model and have shown the impact of the inventory input parameters on the cycle length and total inventory fuzzy cost. The managerial insights and future scope of this proposed study have been shown in the sensitivity analysis and conclusion. The limitations, application, future extension and scope, and social implementation have been shown in this research study. [ABSTRACT FROM AUTHOR]

In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We study maintenance service operations in large facilities portfolios with a focus on modeling logistics of associated spare parts and their storage locations serviced by personnel from several trades who move around the buildings in their vehicles. The key challenge is to reduce the cost of spare parts inventory, storage, and vehicles while providing responsive customer service. To address these challenges, we propose (i) inventory models to determine stock levels and (ii) storage space in personnel vehicles to allocate the mix and location of spare parts by trade. The two models can be readily integrated, and conceptually, technician vehicles serve as “forward-reserve storage” that improves service responsiveness. The proposed model is applied to a large facility of a non-profit organization with 330 buildings, 17,000 spare parts, and 254 vehicles. Compared to prevailing practice, the proposed model can potentially reduce $1.27 million worth of inventory costs, a reduction of ~77%. [ABSTRACT FROM AUTHOR]

The choice of inventory cost flow assumption is a key financial reporting decision. The resurgence of inflation has renewed interest in this option. We derive algebraic models of the LIFO‐FIFO difference in cost of goods sold and the LIFO tax savings as a function of inflation, turnover, inventory increments, and tax rates. Managers and researchers can use these models to estimate the likely financial and tax impacts of LIFO adoption. Historic trends in public company LIFO usage since 1971 are consistent with the changing impacts estimated by our model. We infer an approximate cost level of using LIFO based on historic public company behavior. In the 1971–2022 period, LIFO adoptions were only common when the tax benefit in our model exceeded about 0.35% of COGS. Even when inflation rose in 2021 and 2022, the tax savings remained below this threshold, and there were far more LIFO abandonments than adoptions. [ABSTRACT FROM AUTHOR]

Bike-sharing system has become an indispensable element of sustainable urban transportation, effectively resolving the "last mile" transportation challenge for city dwellers. A major daily operational task in these systems is planning a fleet to rebalance the bikes over time, ensuring the optimal availability of bikes and docks to users. Recycling is also a daily work with the an increase in the number of broken bikes. However, rebalancing or recycling operation is always regarded as an independent tasks. They are separately studied in existing papers. Thus, this paper develops an operational strategy for recycling broken bikes during the rebalancing process, and studies the combination of the station inventory and vehicle routing problems. First, an inventory routing model is constructed with the aim of minimizing the total costs including procurement, expected user loss, inventory and transportation costs. Then, a two-stage iterative algorithm is developed with both exact and heuristic algorithms. We use real-world data from Capital Bikeshare to test our proposed model and approach, which shows the two-stage iterative algorithm is efficient and outperforms existing solutions in reducing total costs. Finally, the sensitivity analysis is performed on key parameters such as the vehicle's capacity, unit penalty costs for customer dissatisfaction events, unit inventory holding costs and the observation period of rebalancing. It shows that enterprises can reduce the total cost by altering vehicle's capacity, reducing the unit inventory holding costs or changing the observation period of rebalancing. [ABSTRACT FROM AUTHOR]

Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company's (STAR) current "number of days" method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company's current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology's effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency. [ABSTRACT FROM AUTHOR]

Micro, Small and Medium Enterprises (MSMEs) have an important role in economic growth in Indonesia. Inventory management is one of the key aspects in the operational sustainability of MSMEs, especially raw materials used in the production process. One of them is XYZ MSMEs which is a producer of meatball pentol and meatball tofu products which uses several types of raw materials in its production process. The problem currently being faced is that there are often shortages and excesses in purchasing raw materials during procurement. This research aims to analyze the cost of raw material inventory at XYZ MSMEs using five different Lot Sizing methods, namely Economic Order Quantity (EOQ), Period Order Quantity (POQ), Silver Meal (SM), Least Unit Cost (LUC), and Part Period Balancing (PPB). This research uses historical data from MSME XYZ to identify differences in raw material inventory costs between the five MRP methods used. The results of this research show that the Silver Meal (SM) method produces the lowest total inventory costs for beef and tapioca starch as raw materials. Meanwhile, the Part Period Balancing (PPB) method produces the lowest total inventory costs for MSG raw materials. [ABSTRACT FROM AUTHOR]

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We study an online distribution problem in which a producer has to send a load from an origin to a destination. At each time period before the deadline, they ask for transportation price quotes and have to decide to either accept or not accept the minimum offered price. If this price is not accepted, they have to pay a penalty cost, which may be the cost to ask for new quotes, the penalty cost for a late delivery, or the inventory cost to store the load for a certain duration. The aim is to minimize the sum of the transportation and the penalty costs. This problem has interesting real-world applications, given that transportation quotes can be obtained from professional websites nowadays. We show that the classical online algorithm used to solve the well-known Secretary problem is not able to provide, on average, effective solutions to our problem, given the trade-off between the transportation and the penalty costs. Therefore, we design two classes of online algorithms. The first class is based on a given time of acceptance, while the second is based on a given threshold price. We formally prove the competitive ratio of each algorithm, i.e., the worst-case performance of the online algorithm with respect to the optimal solution of the offline problem, in which all transportation prices are known at the beginning, rather than being revealed over time. The computational results show the algorithms' performance on average and in the worst-case scenario when the transportation prices are generated on the basis of given probability distributions. [ABSTRACT FROM AUTHOR]

This paper presents a profit optimization model for substitute products in a competitive, time-sensitive market with scarcity and shifting user preferences. The model maximizes profit, considering production costs and inventory maintenance. It uses a discrete choice model to represent demand, sensitivity to price, availability, and changing preferences. A two-phase PSO-type metaheuristic solution tackles the nonlinear, recursive model, efficiently managing inventories and evolving consumer preferences. The model integrates production decisions, inventories, and sales prices, considering scarcity conditions and user preferences. It uses a multinomial logit for the consumers' demand function with soft exogenous constraints, which influence utility and change consumption preferences and choices. This research offers a tool for companies to manage stock, production, and pricing in a context where goods are substitutes, providing a new perspective on business strategy. [ABSTRACT FROM AUTHOR]

The efficient use of the production-inventory control system is of great importance in the industry. In this paper, an investigation study of the impact of an optimal Integral minus Proportional Derivative (I-PD) controller on the dynamic behavior of the production-inventory system is given. The differential equations of the system are first formulated. Then, the I-PD controller is proposed to enhance the responsiveness of the inventory performance. Two swarm optimization techniques, Cuckoo Search Optimization (CSO) and an Improved CSO (ICSO), are employed to tune the adjustable design parameters of the I-PD controller. In order to represent a realistic environment, two simulations are conducted. The first one is when the system is subject to a unit step change in the demand and second is when the demand is randomly. In a comparative performance study for both tuning schemes, the Integral of Time Absolute Error (ITAE) and the Integral of Square Error (ISE) indices were used as evaluation measures. The simulation outcomes using MATLAB show the superiority of the ICSO to tune the I-PD controller in terms of reducing the ITAE and ISE indices in comparison with the result obtained from traditional CSO. The result of this improvement indicates that the I-PD controller tuned by ICSO looks likely to better in terms of improving the performance of the system, particularly, by significantly reducing the inventory cost. [ABSTRACT FROM AUTHOR]

Every industry on average spends 70 percent on raw materials (inventory). Therefore, there is a need to know the raw material cost and also there is great importance to understand the inventory management system of this industry. The study helps a log to various departments to take steps to control the inventory process. In this competitive business world, each and every business organization need inventory management system for determining what to order, when to order, where and how much to order so that purchasing and storing costs are the lowest possible without affecting production and sales. Thus, inventory management control incorporates the determination of the optimum size of the inventory-how much to be ordered and when after taking into consideration the minimum inventory cost. The overall inventory management includes design and inventory control organization with proper accountability establishing procedure for inventory handling disposal of scrap, simplification, standardization and codification of inventories, determining the size of inventory holdings, maintaining record points and safety stocks, economic order quantity, Economic Order Quantity analysis and VALUE analysis and finally framing an inventory manual. [ABSTRACT FROM AUTHOR]

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost, consisting of the price of raw materials, purchasing set-up costs, and inventory costs, and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch and bound algorithm while solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution. [ABSTRACT FROM AUTHOR]

This paper deals with the more realistic version of the lot sizing and scheduling problem where we have a single machine that process a number of different products and for each product we have a set of delivery dates and the quantities to deliver at each of these dates. The objective is to minimise the sum of setup costs and inventory holding costs including the holding cost of products from the finish time of each production lot to the next delivery date. In addition, a setup can be carried over from a production run to the immediately succeeding one if the same product is processed during these two runs. A mathematical formulation of this problem is given as well as two specially designed solution heuristics. As the literature does not provide any other method to solve this problem, only the results obtained by these two heuristics are compared. [ABSTRACT FROM AUTHOR]

Electricity demand-response programmes, such as the incentive-based and price-based programmes, have been used by utilities to induce customers to reduce their electricity consumption during peak periods. This study investigates the production decisions of a two-stage production system under these programmes in a situation where peak periods arrive randomly in the manufacturing cycle. Analytical results show that under demand-response programmes, the manufacturer, who aims at minimising the total operational cost, usually selects a lower production rate during peak periods and a higher one during non-peak periods. Notably, the uncertainty of the peak periods also has a significant influence on the manufacturer's production plans under these programmes. This paper further investigates the efficiency of different demand-response programmes in reducing the inventory holding cost and electricity cost. The results indicate that participating in the demand-response programmes does not always result in a higher inventory holding cost, which goes against the manufacturer's intuition about these programmes. In addition, this paper evaluates the manufacturers' preference for these demand-response programmes by comparing the operational cost savings generated from participating in the two programmes. It turns out that both the demand-response signals and the inventory holding cost substantially influence the manufacturer's willingness to participate in the programmes. [ABSTRACT FROM AUTHOR]

The primary objective of this article is to develop a mathematical model and determine the optimal policies of an inventory system involving power demand and controlled deterioration through preservation technology. This model comes in handy in a power demand-oriented inventory system with demand high at the end of the period. The model incorporates backlogged shortages and linear holding cost. The triangular neutrosophic numbers (TNN's) are used for a nuanced representation of uncertain and imprecise inventory-related expenses. An efficient algorithm is constructed to minimize the total cost, and obtain optimal positive inventory time, optimum cycle time and minimum preservation technology investment. Few numerical examples are used to illustrate and validate the model. The comparative study conducted between models with and without preservation technology investment reveals a significant reduction in total inventory costs facilitated by the preservation facility. Also, the numerical results obtained in crisp and neutrosophic environment are compared. Specific previously obtained results are discussed to illustrate the theoretical findings. Sensitivity analysis of the model provides managerial insights replicating reality. [ABSTRACT FROM AUTHOR]

Currently, there are many mathematical models and tools for inventory management and optimization. Based on the literature review, the authors conclude that inventory management tools and models are mostly one-sided and focused on the cost aspect of the inventory management process. The research goal was to propose a set of criteria for inventory optimization as a basis for the development of an inventory management model whose structure would consider both the economic aspect and the stochasticity of demand and the reliability of delivery. Optimization criteria are based on extensive analyses of influences and correlations between logistic chain subsystems in relation to inventory. The interactions of the subsystems are then mathematically expressed, which represents the main output of the created set of optimization criteria. The defined criteria can be used as a basis for creating a new optimization model of inventory management. [ABSTRACT FROM AUTHOR]

Problem definition: Medical operations require a large volume and variety of consumable supplies that are kept in hospital inventory and replenished on a regular basis. Stringent requirements on the availability of these supplies, together with high variability in their daily usage, contribute to the high inventory costs of the surgical departments in hospitals. We investigate the value of utilizing Advance Booking Information (ABI) on elective surgeries—which are often booked up to months in advance—in reducing inventory costs. Methodology/results: We study a single-item, periodic-review, stochastic inventory control problem, where the item demand in each period is driven by the number and type of surgeries requiring the item, and with the available information on elective surgeries integrated into the ordering decisions. Given that item usage from each case is uncertain and only realized after the surgery, ABI provides imperfect information on future demand. Through exact analysis of a simplified version of the problem, as well as extensive numerical experiments using synthetic and real data, enabled using a state aggregation technique, we provide insights on and quantify the value of using ABI as a function of the number of periods of ABI integrated into the ordering decisions. We identify a relevant parameter regime—namely, high backlog (relative to holding) costs and when surgeries are booked sufficiently in advance—where the value of using ABI could be significant and the majority of the benefits can be gained through incorporating only one period of ABI beyond the order lead time. In a case study conducted using real data, we observe up to 26% reduction in average inventory levels, without violating the service levels. Managerial implications: By incorporating readily available elective surgery schedules into replenishment decisions of surgical supplies, hospitals could significantly reduce inventory costs without compromising the availability of the supplies. Funding: This work was partially funded by The Ontario Ministry of Government and Consumer Services (MGCS). The views expressed in the paper are the views of the authors and do not necessarily reflect those of the Province. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0063. [ABSTRACT FROM AUTHOR]

This study examines the inventory management (IM) practices, reasons, influencing factors and impact of IM on the performance of micro, small and medium enterprises (MSMEs). Primary data was collected using a survey questionnaire among 40+ MSMEs in Baddi Industrial Area, Himachal Pradesh, India. The study reveals that MSMEs generally follow poor IM practices and non-scientific forecasting methods, leading to capital being tied up due to excess inventory or stockouts, delayed delivery, customer dissatisfaction, reduced profits, operational inefficiencies, and diminished competitiveness. Factors such as limited awareness, lack of access to technology and finances, and a shortage of skilled manpower contribute to inadequate implementation of scientific IM practices. The study establishes that poor IM practices and unscientific forecasting practices have an impact on the inventory cost, which in tun impacts the firm's performance. To address these challenges, the study makes many recommendations. [ABSTRACT FROM AUTHOR]

We study a shipment consolidation problem commonly faced by companies that outsource logistics operations and operate in a commit-to-delivery mode. It involves delivering a given set of orders to their destinations by their committed due times using multiple shipping methods at the minimum total shipping and inventory cost. The shipping cost is generally nonlinear in shipping quantity and can be represented by a subadditive piecewise linear function. We investigate two shipping scenarios, one involving long-haul shipping only and the other involving joint long-haul and short-haul shipping. We develop analytical results and solution algorithms for the shipment consolidation problem under each shipping scenario. The problem under the first shipping scenario is shown to be strongly NP -hard. We find that a simple policy, called the First-Due-First-Delivered (FDFD) policy, which assigns orders with earlier delivery due times to shipping methods with earlier destination arrival times, is very effective. This policy enables us to develop a polynomial time algorithm, which not only solves the problem under the concave shipping cost structure optimally but also achieves a performance guarantee of 2 for the problem under the general subadditive shipping cost structure. For the problem under the second shipping scenario, we extend the FDFD policy for long-haul shipping and derive another policy, called the No-Wait policy, for short-haul shipping. We use these policies to develop a polynomial time algorithm and analyze its performance guarantee. Our computational experiments show that the algorithm significantly outperforms a commercial optimization solver, and its performance is robust across different parameter settings that reflect various practical situations. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data and e-companion are available at https://doi.org/10.1287/mnsc.2023.4835. [ABSTRACT FROM AUTHOR]