This paper develops an economic production quantity (EPQ) inventory model with rework process for a single stage production system with one machine. The existence of a unique machine results in limited production capacity. The aim of this research is to determine both the optimal cycle length and the optimal production quantity for each product to minimize the expected total cost (holding, production, setup, rework costs). The convexity of the inventory model is derived. Also the objective function is proved to be convex. The proposed inventory model is validated with illustrating numerical examples and the optimal period length and the total system cost are analyzed.

This paper considers an inventory control policy for a two-echelon inventory control system with one supplier-one buyer. We consider the case of deteriorating items which lead to shortage in supply chain. Therefore, it is necessary to decrease the deterioration rate by adding some specification to the packaging of these items that is known as active packaging. Although this packaging can reduce the deteriorating rate of products, but may be increases the cost of both supplier and buyer. Because of the complexity of the mathematical model, a genetic algorithm has been developed to determine the best policy of this inventory control system.

This paper deals with the development of an inventory model for Weibull deteriorating items with constant demand when delay in payments is allowed to the retailer to settle the account against the purchases made. Shortages are not allowed and the salvage value is associated with the deteriorated units. In this paper, we consider two cases; those are for the case payment within the permissible time and for payment after the expiry of permissible time with interest. Numerical examples are provided to illustrate our results. Sensitivity analysis are carried out to analyze the effect of changes in the optimal solution with respect to change in one parameter at a time.

A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. In order to efficiently control the inventory items and to determine the suitable ordering policies for them, multi-criteria inventory classification is used. In this paper, fuzzy analytic hierarchy process for multiple criteria ABC inventory classification has been proposed. Fuzzy Analytic Hierarchy process (Fuzzy AHP) is used to determine the relative weights of the attributes or criteria, and to classify inventories into different categories. To accredit the proposed model, it is implemented for the 351 raw materials of switch gear section of Energypac Engineering Limited (EEL), a large power engineering company of Bangladesh. In this approach, at first, related criteria have been selected (Unit price, last year consumption or annual demand, last use date, supplier, criticality, durability) and the weights of these criteria was determined using Fuzzy AHP. Then a score to each item was assigned for each criterion as triangular fuzzy number and the final normalized weighted score of each item using fuzzy set theory is calculate. Finally, Chang’s extent analysis was used for the comparison of fuzzy numbers and the final scores are compared with each other. Then all items were classified into three classes according to their final score.

Now-a-days, the offer of credit period to the retailer for settling the account for the units purchased by the supplier is considered to be the most beneficial policy. In this article, an attempt is made to formulate an economic order quantity model under fuzzy environment where delay in payment for the retailer is permissible. The demand rate, ordering cost and selling price per item are taken as triangular fuzzy numbers. The ?-cut representation method is used to calculate the optimum cycle time and total optimum cost. The optimum cycle time and total optimum cost in fuzzy sense is de-fuzzified using the centre of gravity method.

A central problem of tool management in Versatile Multi-tool machining centres is to decide how to batch the parts to be produced and what tools to allocate to the machine in order to maximize utilization of these expensive machines. Various authors have proposed heuristics and/or mathematical models to minimize the batches of parts to be manufactured in a production period. There is no comprehensive study reported to compare the number of actual batches (stoppages) formed with and without processing time considerations. In this paper, the sequential deterministic heuristics (SDHs) are appropriately adapted to include processing time of operations in the formation of groups. The modified heuristics are more realistic in reducing machine stoppages due to tools. Some stochastic search techniques have also been adapted to compute the number of groups. The results are compared with those obtained from SDHs and standard search techniques. The results indicate that the adapted search techniques are powerful approaches for forming optimum number of batches of parts and tools.

Demand uncertainty obliges all participants through a supply chain to make decisions under uncertainty. These decisions extend across price, investment, production, and inventory quantities. We take account of competition between two supply chains under demand uncertainty. These chains internally are involved in vertical pricing competition; however, they externally participate in horizontal pricing and service level competitions by offering a single-type product to the market. Since firms may have various attitudes against demand uncertainty and its related risks, different risk structures for competitive supply chains are considered. We assume that risk-averse firms are able to decrease demand uncertainty by information gathered from market research. For risk-averse participants in a chain, market research investment is an appropriate ground for vertical coordination, which diminishes risk through a supply chain. Optimal strategies based on game theory are obtained for different risk structures; furthermore, for each structure the effects of risk sensitivity as well as market research efficiency on these optimal strategies are investigated. Finally, we propose two scenarios for information sharing between risk-averse participants.

This work deals with production scheduling problem in an assembly flow shop, having parts machining followed by their subsequent assembly operations. Limited heuristics available on the problem, are based on unrealistic assumption that every part is processed on all machines. In this paper, two heuristics NEH_BB and Disjunctive are proposed to solve assembly flow shop scheduling problem where every part may not be processed on each machine. Exhaustive computational experiments are conducted with 60 trials each. The methods are found to be applicable to large size problems. The objective functions used for comparison are makespan and computational time. Disjunctive method takes very less computational time as compared to NEH_BB and hence claimed to be the better among available approaches for finding solution in assembly flow shop problems.

Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance.

In this paper, a new multi-item inventory system is considered with random demand and random lead time including working capital and space constraints with three decision variables: order quantity, safety factor and backorder rate. The demand rate during lead time is stochastic with unknown distribution function and known mean and variance. Random constraints are transformed to crisp constraints with using the chance-constrained method. The Minimax distribution free procedure has been used to lead proposed model to the optimal solution. The shortage is allowed and the backlogging rate is dependent on the expected shortage quantity at the end of cycle. Two numerical examples are presented to illustrate the proposed solution method.