Scientia Iranica
Volume:18 Issue: 3, 2011

This paper presents a new mixed integer nonlinear stochastic staff scheduling model, where the workforce demands are under uncertainty, with a general probability distribution. To validate the proposed model, a simulation technique is employed and an optimization technique is used to solve the resulted model. As the problem is combinatorial, a meta-heuristic approach, i.e. a genetic algorithm, is implemented with tuned parameters, using the Taguchi design of experiment method. The preliminary results indicate that the proposed method of this paper can be effectively used to manage staff schedules for many real-world applications.

Make-to-order firms use different strategies, such as dynamic pricing and due date management, to influence their performance. In these strategies, orders are segmented into classes based on their sensitivity to lead time and price. Quoting different prices and lead times to different classes of customer can increase a firm’s profit and its capacity utilization. Most research in this area does not consider the effects of production constraints on price and lead time decisions. In this paper, we consider the role of flexibility in dynamically choosing the price, lead time and segmentation of customers in make-to-order environments with limited production capacity and multi-period horizon under a stochastic demand function. To reflect the dynamic variations of a system’s conditions, we propose a Multi-stage Stochastic Programming (MSP) method to jointly determine prices, lead time and production. Furthermore, we assume that demand is a linear function of price, lead time and time. Through numerical analyses, we indicate the benefits of dynamic pricing and lead time decisions, based on different customer classes in various environments.

An integrated approach is presented for predicting granule particle size using Partial Correlation (PC) analysis and Artificial Neural Networks (ANNs). In this approach, the proposed model is an abstract form from the ANN model, which intends to reduce model complexity via reducing the dimension of the input set and consequently improving the generalization capability of the model. This study involves comparing the capability of the proposed model in predicting granule particle size with those obtained from ANN and Multi Linear Regression models, with respect to some indicators. The numerical results confirm the superiority of the proposed model over the others in the prediction of granule particle size. In order to develop a predictive-control strategy, by employing the proposed model, several scenarios are developed to identify the most suitable process settings with respect to the desired process response. Utilization of these scenarios paves the way for decisions about spray drying to be made consistently and correctly without any need for judgmental speculations or expensive trial-and-error tests.

Cooperation among supply chain members, both horizontally and vertically, has become the norm in practice. Unlike traditional supply chains with members competing to reduce their individual costs, the overall cost of the entire supply chain is minimized in a cooperative supply chain. The savings from cooperation may be shared among the members, while a lower average cost and a lower cost variation is materialized for individual members. The problem is formulated as an integrated flow network and expanded to multi-period and multi-product, with the possibility of holding inventories in a multi-stage, multi-member cooperative supply chain. Simulation results indicate an approximately 26% reduction in total costs of the supply chain, utilizing this formulation over competitive setups. In a multi-period chain, members may hold an inventory or use an inventory policy. As the holding costs increase, the problem decomposes into a single period (just-in-time) again. The disturbing bullwhip effect disappears in cooperative supply chains.

Quality management system implementation has become a must for construction companies in some countries to be able to enter tenders. One of the most common quality standards is the ISO 9000 quality management standard and many companies seek ISO 9000 certification in today’s highly competitive market. However, in getting this certification, most companies face difficulties, such as the high amount of paperwork, improper documentation, poor communication among employees and project participants, and low employee morale as a result of lack of motivation. In this study, a web-based office automation system was developed. Web facilities and the database management capabilities of Microsoft Visual Studio 2008 were applied to create a data warehouse that was aimed to reduce paperwork, create a proper documentation system, improve communication, and calculate employee performances, in order to create a motivation system for company personnel. Short-term feedback of the practical implementation of the system demonstrated its practicality and advantages, and the positive view of the managers. Also, it is anticipated that long-term feedbacks would also prove its appropriateness and ease of use.

This paper develops an inventory model with non-instantaneous delivery under trade credit and logistics risk. The objective is to determine the optimal replenishment policy for a retailer, given uncertainty in a supply chain due to unforeseeable disruption or various types of defect (e.g. shipping damage, missing parts, misplaced products and/or disasters such as earthquake or hurricane). We provide two solution procedures from the perspective of risk-neutral and risk-averse, respectively. For the risk-neutral solution, the retailer determines the cycle time to minimize the expected total cost. For the risk-averse solution, the model limits the solution space to the set of cycle times, which guarantees an upper bound of defective products under contingency. This risk management operations research technology is very useful in the case of a low-probability high-consequence contingency event. We conclude with computational examples that lead to a comparison of these two solution procedures.

A Particle Swarm Optimization (PSO) algorithm for a Flow Shop Sequence Dependent Group Scheduling (FSDGS) problem, with minimization of total flow time as the criterion, is proposed in this research. An encoding scheme based on Ranked Order Value (ROV) is developed, which converts the continuous position value of particles in PSO to job and group permutations. A neighborhood search strategy, called Individual Enhancement (IE), is fused to enhance the search and to balance the exploration and exploitation. The performance of the algorithm is compared with the best available meta-heuristic algorithm in literature, i.e. the Ant Colony Optimization (ACO) algorithm, based on available test problems. The results show that the proposed algorithm has a superior performance to the ACO algorithm.