Gede Agus Widyadana

Economic order quantity model for deteriorating items with planned backorder level

In this study, a deteriorating inventory problem with and without backorders is developed. From the literature search, this study is one of the first attempts by researchers to solve a deteriorating inventory problem with a simplified approach. The optimal solutions are compared with the classical methods for solving deteriorating inventory model. The total cost of the simplified model is almost identical to the original model.

Economic production quantity model for deteriorating inventory with random machine unavailability and shortage

Both random machine unavailability and shortage are common occurrences in manufacturing industries. In this paper, machine unavailability is considered in deriving an economic production quantity (EPQ) model for deteriorating items. We develop four EPQ models for deteriorating inventory with random machine unavailability and shortage. The study considers lost sales, backorder and two kinds of machine unavailability distributions. Numerical examples are provided to illustrate the theory. Key parameter changes that affect costs are shown in the sensitivity analysis. From the results of the sensitivity analysis, it is shown that the random machine unavailability parameter and holding cost have significant effects on the optimal total cost and production down-time.

Economic production quantity models for deteriorating items with rework and stochastic preventive maintenance time

Economic production quantity (EPQ) models for deteriorating inventory have been investigated by many researchers in recent years. In this paper, we develop EPQ models for deteriorating items with rework and stochastic preventive maintenance time. The models are solved using a search method, since a closed form solution cannot be derived. A numerical example and sensitivity analysis are provided to evaluate the models. The sensitivity analysis shows that the deteriorating cost has a significant effect on the optimal inventory cost; however, the production and the demand rate have the most significant effect on the optimal total cost and the optimal production up-time period.

Revisiting lot sizing for an inventory system with product recovery

This note investigates the study by Teunter (2004) [1] on lot sizing for inventory systems with product recovery where lot sizing formulae for two recovery policies ((1,R) and (P,1)) are derived. Instead of applying the classical optimization technique, we develop an integrated solution procedure for each of the two policies using algebraic approaches. Numerical analysis show that our examples result in a lower total cost for both policies.

A constrained multi-products EPQ inventory model with discrete delivery order and lot size

Abstract This paper proposes an alternative heuristic algorithm for a multi-product Economic Production Quantity (EPQ) vendor–buyer integrated model with Just in Time (JIT) philosophy and a budget constraint. This type of problem is usually solved by a Mixed Integer Non Linear Problem (MINLP), which is complex and computationally expensive. The proposed heuristic algorithm involves less computation and therefore it is less expensive than the previously published algorithms. Furthermore, the heuristic algorithm is simpler as it derives the integer values for all discrete variables in a straightforward manner. Through empirical experimentation, it is demonstrated that the heuristic algorithm provides solutions closer to the lower bound in a very short time.

Determining the optimal number of Kanban in multi-products supply chain system

Kanban, a key element of just-in-time system, is a re-order card or signboard giving instruction or triggering the pull system to manufacture or supply a component based on actual usage of material. There are two types of Kanban: production Kanban and withdrawal Kanban. This study uses optimal and meta-heuristic methods to determine the Kanban quantity and withdrawal lot sizes in a supply chain system. Although the mix integer programming method gives an optimal solution, it is not time efficient. For this reason, the meta-heuristic methods are suggested. In this study, a genetic algorithm (GA) and a hybrid of genetic algorithm and simulated annealing (GASA) are used. The study compares the performance of GA and GASA with that of the optimal method using MIP. The given problems show that both GA and GASA result in a near optimal solution, and they outdo the optimal method in term of run time. In addition, the GASA heuristic method gives a better performance than the GA heuristic method.

Stackelberg game for two-level supply chain with price markdown option

Vendor–retailer collaboration has an important role in supply chain management. Although vendor–retailer collaboration results in better supply chain profit, collaboration is difficult to realize. This is because most vendors and retailers try to optimize their own profit. This paper applies the Stackelberg game with stochastic demand for the vendor–retailer system. The vendor as a leader determines the product price, and the retailer decides order quantity and frequency of price markdown. This study develops example and sensitivity analyses to illustrate the theory. Results show that the price markdown option has a better total supply chain profit than without a price markdown policy, and the vendor receives more benefit. For different demand variances, the retailer profit is more sensitive than the vendor profit.

THE EFFECTS OF INFLATION AND TIME VALUE OF MONEY ON A PRODUCTION MODEL WITH A RANDOM PRODUCT LIFE CYCLE

This paper extends Moon and Lees EOQ model to consider a production system with a time-value of money and inflation under a random life cycle. Two conditions are discussed: the first is when the product life cycle ends in the production stage and the second is when the product life cycle ends in the non-production stage. We develop an algorithm to find the optimal period time and the lowest expected total cost. Numerical examples and sensitivity analyses are given to validate the results of the production model.

Optimal coordinated supply chain strategy with price and time sensitive demand

In this study, inventory models are developed for coordinated supply chain using Stackelberg game framework. In the proposed model, customer demand is assumed to be price and time sensitive. The buyer attempts to adjust retail selling prices by charging premium or offering discount to the floor selling price depending upon the optimistic or declining market conditions. The aim of this research paper is to analyse the optimal pricing policy that maximises total profit for both the players under the principles of coordination, and competition. Some numerical examples are given to study the model. The results show that the vendor gets more advantage when the collaborative model is applied and the buyers profit is bigger in the Stackelberg game model than the collaborative model.

Production Inventory Models for Deteriorating Items with Stochastic Machine Unavailability Time, Lost Sales and Price-Dependent Demand

This paper investigates a development of single machine batch scheduling for multi items with dependent processing time. The batch scheduling problem is to determine simultaneously number of batch ( ), which item and its size allocated for each batch, and processing sequences of resulting batches. We use total actual flow time as the objective of schedule performance. The multi item batch scheduling problem could be formulated into a biner-integer nonlinear programming model because the number of batch should be in integer value, the allocation of items to resulting batch need binary values, and also there are some nonlinearity on objective function and constraint due to the dependent processing time. By applying relaxation on the decision variable of number of batch ( ) as parameter, a heuristic procedure could be applied to find solution of the single machine batch scheduling problem for multi items.