نشریه بین المللی مهندسی صنایع و مدیریت تولید
سال بیستم شماره 1 (بهار 1388)

During the last three decades, the integrated optimization approach to logistics systems has become one of the most important aspects of the supply chain optimization. This approach simultaneously considers the interdependence of the location of the facilities, suppliers/customers allocation to the facilities, the structure of transportation routes, and inventory planning and control. Location-routing problem is one of the most important classes of location problems for considering this approach. In this problem, the number and location of facilities, size of the transportation fleet, and the route structures are to be found with respect to the location and characteristics of suppliers and customers. In this study, a mathematical model, an efficient and fast heuristic algorithm, an effective metaheuristic algorithm based on simulated annealing, and a new lower bound for the two-echelon location-routing problem with vehicle fleet capacity and maximum route length constraints are presented. At the end, the computational results show the efficiency of the proposed algorithms using the proposed obtained lower bound.

Trip distribution is a very important step in transportation modeling context. Many decent researches have been dedicated to the importance of the models for this third step of transportation modeling. Entropy maximization model is one of thermodynamic models and is implemented in modeling various scientific Phenomenons. As the name implies, entropy maximization model tries to find the maximum entropy under which a system is supposed to be stable. Making pretty little changes to the thermodynamic nature, we can very easily use this phenomenon to distribute trips. As the problem here is an NP-hard one, simulated annealing as a meta-heuristic algorithm is used for solving the problem. At the end, a real case model of trip distribution for Iran states as an application of this algorithm is examined.

There are multivariate processes in which two or more quality characteristics must be controlled simultaneously. In controlling such processes, two goals must be achieved. The first one is to identify an out of control situation and the second is to determine the quality features caused the out of control signal. In this paper, both goals are investigated. In addition to the current methods used to diagnose an out of control situation, for the purpose of making the Hotelling T 2 more sensitive, the warning limits for T 2 are also defined. In determining the quality characteristics caused the out of control situation, current methods are investigated and a new procedure is suggested. Not only the new approach does not have some of the deficiencies with the current methods, but also its application is much simpler in practice. The results of simulation using the warning limits for very small shifts in process mean vector indicate that in 81% of the time, shifts are being detected. While in similar conditions, when the regular T 2 is used, only 19% of the time true signals are observed. In comparison with similar techniques, use of new procedure in detecting the quality characteristics responsible for an out of control situation identifies the shifted quality features in 76% of the time. While in existing methods at most in 64% of the time the shifted quality characteristics are detected.

One of the efforts in a pull system to meet the final goal of Just-In-Time is reducing Work-In-Process (WIP) without reducing throughput rate an service level. This paper proposes a new hybird control policy using extended Kanban, Generalized Kanban and CONWIP to form the final construction of production plan. Simulation of a 3-stage case study with probabilistic parameters showed the reduction of inventory level with acceptable service quality. Data Envelopment Analysis (DEA) is implemented for the evaluation and verification of the proposed model. The result obtained indicates that the proposed model is quite efficient and applicable to solve real world problems.

Product support and after sales services are among the important areas which have attracted the attention of managers and decision makers, especially in the field of supply chain and logistics management. Supplying the spare parts of products to guarantee the desired operation of product during its life time is in the focus of attention of logistics and supply chain managers. What makes the demand forecasting possible and real is the correct identification of demand affecting factors and their relation. So, the best model of spare part's demand forecasting is one which incorporates all factors influencing the failure rate of the parts. This article presents a model which incorporates the environmental covariates influencing the failure rate as well as the reliability characteristics of parts. In addition, a portion of spare parts demand is due to the interaction of different parts in a system which is known as failure interaction. This factor is regarded in the model as another factor affecting the failure rate of the parts and, the demand estimation as a result. This model uses the reliability models and renewal process to forecast spare parts demand, based on reliability, operational environment and failure interaction.

Working in noisy job shops and doing hard works reduce the efficiency of workers and the resulted injuries impose cost on such an industries. Since, there are different jobs and working environment within a factory, it is possible to reduce job related injuries through a suitable job rotation program. In this paper, we propose a model for determining the job rotation program that is a bi-objective model applied to minimize daily noise exposure index as well as minimizing job severity index due to lost working days. The Global criterion “Lp- Metric” Method is proposed as the solution methodology. Finally, the model is applied in different cases and the results indicate better utilization compared to single objective models.

Statistical process control charts are generally designed assuming that when the process is in control the observations are independent and identically distributed (i.i.d.) over time. However, the assumption of independence is easily violated when a process inherently generates auto correlated observations. When traditional control charts are applied to such processes then the false alarm rate experienced would be higher than what is expected. Machining process, due to the tool wear out, usually generates auto correlated observations. If such phenomenon is not incorporated in the chart design then one should expect a pattern in the plotted observations that will eventually lead to false alarms from time to time. This paper discusses the application of logistic regression to model and eliminate patterns that appear on fraction nonconforming items chart because of tool wear. Numerical results indicate significant improvements.