An optimal approach for the joint problem of level of repair analysis and spare parts stocking

We propose a method that can be used when deciding on how to maintain capital goods, given a product design and the layout of a repair network. Capital goods are physical systems that are used to produce products or services. They are expensive and technically complex and have high downtime costs. Examples are manufacturing equipment, defense systems, and medical devices

Production and inventory management under multiple resource constraints

In this paper we present a model and solution methodology for production and inventory management problems that involve multiple resource constraints. The model formulation is quite general, allowing organizations to handle a variety of multi-item decisions such as determining order quantities, production batch sizes, number of production runs, or cycle times. Resource constraints become necessary to handle interaction among the multiple items. Common types of resource constraints include limits on raw materials, machine capacity, workforce capacity, inventory investment, storage space, or the total number of orders placed. For example, in a production environment, there may be limited workforce capacity and limits on machine capacities for manufacturing various product families. In a purchasing environment where a firm has multiple suppliers, there are often constraints for each supplier, such as the total order from each supplier cannot exceed the volume of the truck. We present efficient algorithms for solving both continuous and integer variable versions of the resource constrained production and inventory management model. The algorithms require the solution of a series of two types of subproblems: one is a nonlinear knapsack problem and the other is a nonlinear problem where the only constraints are lower and upper bounds on the variables. Computational testing of the algorithms is reported and indicates that they are effective for solving large-scale problems

Approximation algorithms and hardness results for the joint replenishment Problepm with constant demands

19th Annual European Symposium, Saarbrücken, Germany, September 5-9, 2011. ProceedingsIn the Joint Replenishment Problem (JRP), the goal is to coordinate the replenishments of a collection of goods over time so that continuous demands are satisfied with minimum overall ordering and holding costs. We consider the case when demand rates are constant. Our main contribution is the first hardness result for any variant of JRP with constant demands. When replenishments per commodity are required to be periodic and the time horizon is infinite (which corresponds to the so-called general integer model with correction factor), we show that finding an optimal replenishment policy is at least as hard as integer factorization. This result provides the first theoretical evidence that the JRP with constant demands may have no polynomial-time algorithm and that relaxations and heuristics are called for. We then show that a simple modification of an algorithm by Wildeman et al. (1997) for the JRP gives a fully polynomial-time approximation scheme for the general integer model (without correction factor). We also extend their algorithm to the finite horizon case, achieving an approximation guarantee asymptotically equal to √9/8

DYNAMIC LOT-SIZING PROBLEMS: A Review on Model and Efficient Algorithm

Due to their importance in industry, dynamic demand lot-sizing problems are frequently studied.This study consider dynamic lot-sizing problems with recent advances in problem and modelformulation, and algorithms that enable large-scale problems to be effectively solved.Comprehensive review is given on model formulation of dynamic lot-sizing problems, especiallyon capacitated lot-sizing (CLS) problem and the coordinated lot-sizing problem. Bothapproaches have their intercorrelated, where CLS can be employed for single or multilevel/stage, item, and some restrictions. When a need for joint setup replenishment exists, thenthe coordinated lot-sizing is the choice. Furthermore, both algorithmics and heuristics solutionin the research of dynamic lot sizing are considered, followed by an illustration to provide anefficient algorithm

Applications of remote sensing to estuarine management

There are no author-identified significant results in this report

an evolutionary approach for the offsetting inventory cycle problem

AbstractIn inventory management, a fundamental issue is the rational use of required space. Among the numerous techniques adopted, an important role is played by the determination of the replenishment cycle offsetting which minimizes the warehouse space within a considered time horizon. The NP-completeness of the Offsetting Inventory Cycle Problem (OICP) has led the researchers towards the development and the comparison of specific heuristics. We propose and implement a genetic algorithm for the OICP, whose effectiveness is validated by comparing its solutions with those found by a mixed integer programming model. The algorithm, tested on realistic instances, shows a high reduction of the maximum space and a more regular warehouse saturation with negligible increase of the total cost. This paper, unlike other papers currently available in literature, provides instances data and results necessary for reproducibility, aiming to become a benchmark for future comparisons with other OICP algorithms

Assessment of joint inventory replenishment: a cooperative games approach

This research deals with the design of a logistics strategy with a collaborative approach between non-competing companies, who through joint coordination of the replenishment of their inventories reduce their costs thanks to the exploitation of economies of scale. The collaboration scope includes sharing logistic resources with limited capacities; transport units, warehouses, and management processes. These elements conform a novel extension of the Joint Replenishment Problem (JRP) named the Schochastic Collaborative Joint replenishment Problem (S-CJRP). The introduction of this model helps to increase practical elements into the inventory replenishment problem and to assess to what extent collaboration in inventory replenishment and logistics resources sharing might reduce the inventory costs. Overall, results showed that the proposed model could be a viable alternative to reduce logistics costs and demonstrated how the model can be a financially preferred alternative than individual investments to leverage resources capacity expansions. Furthermore, for a practical instance, the work shows the potential of JRP models to help decision-makers to better understand the impacts of fleet renewal and inventory replenishment decisions over the cost and CO2 emissions.DoctoradoDoctor en Ingeniería Industria

Optimizing replenishment order quantities in uncoordinated supply chains

Many modern supply chains can be described as a series of uncoordinated suppliers. That is each supplier establishes their individual inventory and production policies on both the input and output sides. In these supply links there is minimal coordination between suppliers, and typically only prices and delivery guarantees are contracted. As a consequence, the inventory behavior and associated costs do not exhibit standard patterns. This makes it difficult to model and optimize these chains using classical inventory models. The common approach, therefore, for evaluating uncoordinated supply chains is to use Supply Chain Analytics software. These retrieve operational data from Enterprise Resource Planning (ERP) systems and then characterize the historical inventory performance behavior. Nearier (2008) developed a joint production inventory model for estimating inventory costs in uncoordinated chains as an alternative to supply chain analytics. They proposed a (Q, R, δ)2 relationship between each pair of sequential suppliers, where Q is the order quantity, R is the reorder level, and δ is the production or consumption rate. In this arrangement each part has two inventory locations: (i) on the output side of the seller, and (ii) on the input side of the buyer. hi this dissertation, the (Q, R, δ)2 model was extended. Three specific research tasks were accomplished in this regard. First, the inventory estimation accuracy of the original (Q, R, δ)2 model was improved. This was accomplished by deriving a more reliable estimate of the residual inventory at the end of each supply cycle. Further, a more accurate model of the inventory behavior in supply cycles where the seller has no production was developed. A discrete inventory simulation was used to demonstrate a significant improvement in the estimation accuracy, from a 10-30 % error range to within 5% error on average. Second, a prescriptive model for deriving the optimal Q when reducing inventory costs in a (Q, R, δ)2supply relationship was developed. From simulation studies, it was found that due to differences in production batch sizes, production rates, and replenishment order quantities, the inventory cost function exhibits a non-differentiable step-wise convex behavior. Further, the steps are observed to occur at integer ratios of Q and the buyer\u27s production batch. This behavior makes it difficult to analytically derive the optimal Q, which could occur at one of the step points or any intermediate point. A golden section based search heuristic for efficiently deriving the optimal Q was developed. Third, the robustness of Q to demand shifts was studied. A demand shift occurs wherever the mean demand jumps to a higher or lower level, similar to a moving average forecast. The demand shift range beyond, which there is significant deterioration in inventory costs and a change in the supply policy Q is justified, was determined Two supply policies were studied: (i) fixed delivery batch and (ii) fixed production period. For each stochastic demand shift behavior, a delivery batch size or production period that minimizes the total cost of both suppliers is selected