Journal of Industrial Engineering and Management Studies
Volume:1 Issue: 1, Summer-Autumn 2014

Make-to-order is a production strategy in which manufacturing starts only after a customer''s order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted tardiness penalties in permutation flow shop scheduling with MTO production strategy, the objective function of which is to maximize the total net profit of the accepted orders. The problem is formulated as an integer-programming (IP) model, and a cloud-based simulated annealing (CSA) algorithm is developed to solve the problem. Based on the number of candidate orders the firm receives, fifteen problems are generated. Each problem is regarded as an experiment, which is conducted five times to compare the efficiency of the proposed CSA algorithm to the one of simulated annealing (SA) algorithm previously suggested for the problem. The experimental results testify to the improvement in objective function values yielded by CSA algorithm in comparison with the ones produced by the formerly proposed SA algorithm.

Long-term planning is a challenging process for dealing with problems in big industries. Quick and flexible process of responding to the existing variable requirements are considered in such problems. Some of important strategic decisions which should be made in this field are, namely the way that manufacturing facilities should be applied as well as assignment and design the system of delivery of orders. On the other hand, by using the small core and big network viewpoint in planning, such decisions should be made in a concentrated way. In this paper, a robust multi criteria group decision making model based on TOPSIS method is proposed, which evaluates the requirements of a real case study. In this regard, firstly important criteria in such environments would be determined. Secondly, using expert’s opinions and statistical analysis methods the group multi criteria decision making model would be constructed.

Supply chain network design (SCND) problem has recently been gaining attention because an integrated management of the supply chain (SC) can reduce the unexpected/undesirable events and can affect definitely the efficiency of all the chain. A critical component of the planning activities of a manufacturing firm is the efficient design of its SC. Hence, SCND affords a sensitive platform for efficient and effective supply chain management and is an important and strategic decision in one. This paper presents a supply chain network model considering both strategic and tactical decisions. The model determines location of plants and distribution centers regarding single sourcing and capacity of plants and distribution centers (strategic level) while the shipments have to wait in the queue for transporting from plants to distribution centers (tactical level), which lead to the lead time is incorporated in model. Because of high-impact decision of a supply chain network design, we extend the model in an uncertain environment. To deal with uncertainty where the uncertain parameters are described by a finite set of possible scenarios, the two-stage stochastic programming approach is applied. Finally, a numerical example is given to demonstrate the significance of problem.

Companies’ managers are very enthusiastic to extract the hidden and valuable knowledge from their organization data. Data mining is a new and well-known technique, which can be implemented on customers data and discover the hidden knowledge and information from customer's behaviors. Organizations use data mining to improve their customer relationship management processes. In this paper R, F, and M variables for each customer are defined and extracted. Customers are clustered by using K-mean algorithm based on their calculated R, F and M values. The best number of clusters is calculated by Davies Bouldin index. The clusters are ranked based on their eligibility values. By analyzing the clustering results, we propose some offers to the company to calculate the premiums and insurance charges.

In this paper, Economic Order Quantity () based model for non-instantaneous deteriorating items with imperfect quality, permissible delay in payments and inflation is proposed. We adopt a time-dependent demand function. Also, the effects of time value of money are studied using the Discounted Cash Flow approach. Moreover, we assume that orders may contain a random proportion of defective items, which follow a known distribution and an inspection process is utilized to describe the defective proportion of the received lot. The mathematical model have been derived for obtaining the optimal number of cycle and the optimal inspection time so that the present value of total cost in a finite time horizon is minimized. An algorithm has been presented to find the optimal solution. Finally, numerical examples are provided to illustrate the solution procedure.

In this article, two different systems subject to shocks occurring based on a non-homogeneous Poisson process (NHPP) are analyzed. Type –I system is consisted of a single unit and type –II system is consisted of two parallel units in which both units operate identically and simultaneously. In type –I system occurrence of a shock causes system stopping and consequently will be received minimal repairs. Also this system is replaced preventively at time Ψ, or at time less than Ψ due to probable failure. In type –II system a shock to each units leads to unit stopping and accordingly the unit receives minimal repairs and other unit receives preventive maintenance services with no system stop. Simultaneously, this system is replaced at time Ψ or at times less than Ψ preventively, due to failure of both units. Systems will be replaced with new and the same types when minimizes total expected cost.