M.I.M. Wahab

Short Communication: Economic order quantity model for items with imperfect quality, different holding costs, and learning effects: A note

In this note, we present the optimal lot sizes for an item with imperfect quality based on Salameh and Jaber [Salameh, M. K., & Jaber, M. Y. (2000). Economic production quantity model for items with imperfect quality. International Journal of Production Economics, 64, 59-64], Maddah and Jaber [Maddah, B., & Jaber, M. Y. (2008). Economic order quantity for items with imperfect quality: Revisited. International Journal of Production Economics, 112, 808-815], and Jaber et al. [Jaber, M. Y., Goyal, S. K., & Imran, M. (2008). Economic production quantity for items with imperfect quality subjected to learning effects. International Journal of Production Economics, 115, 143-150] when different holding costs for the good and defective items are considered. When there is no learning effects, the results show that the lot size with different holding costs for good and defective items increases when the percentage of defective increases, as one would expect in the real manufacturing environment. When there is learning effects, the lot size with different holding costs for the good and defective items is more than the one with same holding costs for the good and defective items. However, as the learning takes place in the system, the difference between the lot sizes with and without different holding costs for the good and defective items disappears.

Joint replenishment with imperfect items and price discount

A joint replenishment problem is presented to determine the ordering policy for multiple items having a certain percentage of defective units. The purpose of this paper is to study the impact of the percentage of defective units on the ordering policy. Two different scenarios are presented for joint replenishment problem: (1) without price discount and (2) with price discount. For each scenario, the total expected cost per unit time is derived and algorithms are presented to determine the family cycle length and the integer number of intervals that the replenishment quantity of each item will last. Numerical examples are presented and the results are discussed.

Approximate dynamic programming modeling for a typical blood platelet bank

It addresses some of the literature gaps and overcome curse of dimension problem.The model considers eight blood types with stochastic demand, stochastic supply.It uses news vendor model and the current inventory to control the supply uncertainty.It uses linear programing to solve the single-period inventory model optimally.It uses ADP to solve the multi-period model without any downsizing. This paper introduces a workable model for the establishment of an inventory bank holding perishable blood platelets with a short shelf life. The model considers a blood platelet bank with eight blood types, stochastic demand, stochastic supply, and deterministic lead time. The model is formulated using approximate dynamic programming. The model is evaluated in terms of four measures of effectiveness: blood platelet shortage, outdating, inventory level, and reward gained. Moreover, several alternative inventory control policies are analyzed. The order quantity decision is taken using a news-vendor model. In addition, the variation of the O- percentage is studied. This study confirms that the blood platelet bank reward can be maximized by operating at the optimal inventory level, thereby minimizing the number of outdated units as well as shortages. In addition, the suitable O- percentage within the blood platelet bank inventory was studied. As the O- blood type inventory levels increase to 40%, shortages drop from 3.9% to 1.5%. Outdated units drop from 4.6% to 1.8%. Furthermore, when the order quantity is received twice a day, shortages drop to 1.8% and outdated units drop to 2.1%.

A joint replenishment problem considering multiple trucks with shipment and resource constraints

A joint replenishment problem (JRP) is presented to determine the optimal reordering policy for multi-items with a percentage of defective items. This JRP also has several constraints, such as shipment constraint, budget constraint, and transportation capacity constraint. At the meantime, multiple trucks, each with a fixed transportation cost, are considered and also order quantities of restricted items are not shared among the trucks during the shipment. The objective is to minimize the total expected cost per unit time. A two-dimensional genetic algorithm (GA) is provided to determine an optimal family cycle length and the reorder frequencies. A numerical example is presented and the results are discussed. Extensive computational experiments are performed to test the performance of the GA. The JRP is also solved by using an evolutionary algorithm (EA) and the results obtained from GA and EA are compared. HighlightsA joint replacement problem (JRP) is solved considering multiple trucks.JRP also includes shipment, budget, and transportation capacity constraints.The JRP is solved by a genetic algorithm (GA) and an evolutionary algorithm (EA).GA outperforms EA in terms of computational time and the total expected cost.

The effect of working environment-ill health aspects on the carbon emission level of a manufacturing system

Abstract Carbon emission has been identified as a major environmental metrics which is threatening earth’s ecology and climate. Governments in developed countries usually get the benefits of carbon policies such as carbon cap and carbon tax to limit carbon emission produced by companies. Organizations also have focused on sustainable development initiatives to reduce the total amount of their carbon emission levels. However, none of them have considered the effects of working environment risk factors (ergonomic conditions) on the carbon emission level at the operational level. To this end, a modeling approach is proposed in this paper to investigate the effects of different ergonomic conditions on the carbon emission level of a serial production system. This modeling procedure includes three-health state Markov chains and Logistic functions that connect physical and psychosocial risk factors to an employee’s health states and their associated performance levels. The outputs of the Markov chains are integrated in a cost optimization model, which is including the serial system’s carbon emission parameters. Carbon cap and carbon tax are the policies which are set in this study to control the system’s emission level. The numerical results show that providing incentives to improve the ergonomic conditions of the system helps reduce the amount of carbon emission by the maximum of 13%. It is also concluded that organizations which are focusing on optimizing their financial indexes (e.g., total cost) must get the advantages of combined carbon reduction solutions by including the simultaneous effects of a carbon policy and ergonomic improvements.