A two-storage model for deteriorating items with holding cost under inflation and Genetic Algorithms

A deterministic inventory model has been developed for deteriorating items and Genetic Algorithms (GA) having a ramp type demands with the effects of inflation with two-storage facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Genetic Algorithms (GA) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Genetic Algorithms (GA). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters

Optimal Deteriorating Inventory Models for Varies Supply Life Cycles

Agriculture items, such as fruits and vegetables, have different supply and demand characteristics during a harvest period. Fruits supply in the first and end of harvest time are not reliable so sometimes supply are not available when needed. Fruits demand is different during harvest season. In the first harvest season, demand depends on price and at the end of harvest time, the demand depends on presentation of the items. In this study, inventory deteriorating items models for the first and the end of the harvest season are developed. Since closed-form solutions cannot be derived from the models, a Genetic Algorithm and a heuristic method are used to solve the problems. A numerical example and sensitivity analysis are conducted to illustrate the model and get insights. The sensitivity analysis shows that the supplier will increase his price when supply is not reliable at the early harvest period.  The results show that the unreliable supply is susceptible to the total cost at the end of the harvest period

Static and dynamic inventory models under inflation, time value of money and permissible delay in payment

In this research a number of mathematical models were developed for static and dynamic deterministic single-item inventory systems. Economic factors such as inflation, time value of money and permissible delay in payment were considered in developing the models. Nonlinear optimization techniques were used to obtain the optimal policies for the systems.;First, a static single-item inventory model was considered in which shortages are allowed and a delay is permitted in payment. In this case, suppliers allow the customers to settle their accounts after a fixed delay period during which no interest is charged.;An extension of the model was then considered in which all cost components of the model are subject to inflation and discounting, with constant rates over the planning horizon. The mathematical model of the system was developed and a nonlinear optimization technique, Hooke and Jeeves search method, was used to obtain the optimal policies for the system.;A dynamic deterministic single-item inventory model was also considered in which the demand was assumed to be a linear function of time. Suppliers allow for a delay in payment and the cost components are subject to inflation and discounting with constant rates and continuous compounding. The Golden search technique was used to obtain the optimum length of replenishment cycle such that the total cost is minimized.;Computer applications using Visual Basic and Mathematics were developed and several numerical example were solved

Optimal production and delivery scheduling models for a supply chain system of deteriorating items

The market is varying from minute to minute nowadays. Increase cooperation and pursue the optimal interest of the integrated supply chain become a more effective way than act alone in the competition. In this research, an integrated inventory policy between singleproducer and multi-buyer is developed and two inventory models are built. The first model extends the research of Lin and Lin (2007) by changing the single-buyer system to the multibuyers one. Both backorder of buyers and deteriorating items of each party (producer’s level, buyers’ level, and during transport) are considered herein. The second model is based on the research of Woo et al.(2001) and Model 1 by takes raw material cost and remanufacturing proceeds into account additional. In both model, the producer and buyers collaboratively work at minimizing their total operation cost and the problems are solved under an assumption of equal replenishments and production cycles. The algorithms to find the optimal solutions are given, and numerical examples are presented. Sensitivity for systems parameters is also analyzed and all calculations are completed by software Matlab and Maple

ONE-TIME ORDER INVENTORY MODEL FOR DETERIORATING AND SHORT MARKET LIFE ITEMS WITH TRAPEZOIDAL TYPE DEMAND RATE

Determining the end of the sales period for a one-time order inventory policy for technology products that see rapid innovation and improvement, such as smartphones, is a vital decision. While the market life cycle is short, with long lead times and expensive deliveries. Such situations can force the number of orders to be few or even only once. Products with the latest technology consist of many components that allow for deterioration from the start. This study discusses the effect of the market life cycle, as indicated by the trapezoidal demand rate, on deteriorating item inventory policies. This study will provide new insights into inventory policy. Mathematical models with a non-linear generalized reduced gradient approach can find the optimal end of the selling period and the order size to achieve maximum profit. A sensitivity analysis showed several findings that provide insight for management

Beyond LIFO and FIFO: Exploring an Allocation-In-Fraction-Out (AIFO) policy in a two-warehouse inventory model

The classical formulation of a two-warehouse inventory model is often based on the Last-In-First-Out (LIFO) or First-In-First-Out (FIFO) dispatching policy. The LIFO policy relies upon inventory stored in a rented warehouse (RW), with an ample capacity, being consumed first, before depleting inventory of an owned warehouse (OW) that has a limited capacity. Consumption works the other way around for the FIFO policy. In this paper, a new policy entitled “Allocation-In-Fraction-Out (AIFO)” is proposed. Unlike LIFO and FIFO, AIFO implies simultaneous consumption fractions associated with RW and OW. That said, the goods at both warehouses are depleted by the end of the same cycle. This necessitates the introduction of a key performance indicator to trade-off the costs associated with AIFO, LIFO and FIFO. Consequently, three general two-warehouse inventory models for items that are subject to inspection for imperfect quality are developed and compared – each underlying one of the dispatching policies considered. Each sub-replenishment that is delivered to OW and RW incurs a distinct transportation cost and undertakes a 100 per cent screening. The mathematical formulation reflects a diverse range of time-varying forms. The paper provides illustrative examples that analyse the behaviour of deterioration, value of information and perishability in different settings. For perishable products, we demonstrate that LIFO and FIFO may not be the right dispatching policies. Further, relaxing the inherent determinism of the maximum capacity associated with OW, not only produces better results and implies comprehensive learning, but may also suggest outsourcing the inventory holding through vendor managed inventory

A forward with backward inventory policy algorithm for nonlinear increasing demand and shortage backorders

The traditional inventory policies have been developed for constant demand processes. In reality, demand is not always stable; it might have an increasing pattern. In this paper, a forward with backward inventory policy algorithm is developed to determine the operational parameters of an inventory system with a nonlinear increasing demand rate, shortage backorders and a finite planning horizon. Numerical experiments are also conducted to compare the results with the existing techniques and to illustrate the applicability of the proposed technique