Journal of Optimization in Industrial Engineering
Volume:2 Issue: 4, Summer and Autumn 2009

Model building is a fragile and complex process especially in the context of real cases. Each real case problem has its own characteristics with new concepts and conditions. A correct model should have some essential characteristics such as: being compatible with real conditions، being of sufficient accuracy، being logically traceable and etc. This paper discusses how to build an efficient model for a real case production planning problem. This process is reinforced by providing the proofs confirming the special characteristics of the final model such as proving its NP-Completeness. Also، the extremes of both objective functions – production smoothing versus cost minimizing – are calculated analytically. Finally، the case study and its solution methods are discussed briefly.

Most of data in a multi-attribute decision making (MADM) problem are unstable and changeable, then sensitivity analysis after problem solving can effectively contribute to making accurate decisions. This paper provides a new method for sensitivity analysis of MADM problems so that by using it and changing the weights of attributes, one can determine changes in the final results of a decision making problem. This analysis applied for SAW technique, one of the most used multi-attribute decision making techniques, and the formulas are obtained.

This paper aims to develop a multi-objective model for scheduling cargo trains faced by the costs of tardiness and earliness, time limitations, queue priority and limited station lines. Based upon the Islamic Republic of Iran Railway Corporation (IRIRC) regulations, passenger trains enjoy priority over other trains for departure. Therefore, the timetable of cargo trains must be determined based on certain passenger trains. In addition, the introduced model considers extra platforms in each station through the travel route. This model has been run in IRIRC and the results have illustrated a great improvement in comparison to status quo. The model has been verified and validated against the real system by conducting t-tests. Furthermore, sensitivity analysis of the model reveals a set of optimization alternatives for scheduling cargo trains. Reduced routing traffic, optimum number of cargo trains, enhanced customer lead times, maximum trains capacity are retrieved from the model in order to obtain an integrated scheduling for cargo and passenger trains.

The purpose of this study is to introduce an application of fuzzy centroid-based approach to ranking the customer requirements using QFD with competition considerations for Diba Fiberglass, an Iranian Company. The illustrated approach, not only focuses on the normal fuzzy numbers, but also considers the non-normal fuzzy numbers to capture the true customer requirements. To this end, first, we provide a concise and operational description of the fuzzy centroid-based approach to ranking the customer requirements in QFD. Then, we focus on the first steps of house of quality (HOQ), which are essentially the customer inputs in QFD with fuzzy considerations including: customer direct ratings, fuzzy representation of customers’ assessments, company performance ratings, competitive priority ratings and final importance ratings. The QFD technique can help Diba Fiberglass Company to make the right decision, which will result in higher improvement. According to the 10 customers’ assessments of the relative performance of the three companies and seven WHATs, a customer comparison matrix is obtained.

Data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data stored in repositories, corporate databases, and data warehouses. Industrial procedures with the help of engineers, managers, and other specialists, comprise a broad field and have many tools and techniques in their problem-solving arsenal. The purpose of this study is to improve the effectiveness of these solutions through the application of data mining. To achieve this objective, an adaptation of the engineering design process is used to develop a methodology, specifically designed for industrial procedures’ operations. This paper concludes by describing some of the advantages and disadvantages of the application of data mining techniques and tools to industrial procedures; it mentions some possible problems or issues in its implementation; and finally, it provides recommendations for future research in the application of data mining to facilitate decisions relevant to industrial procedures.

Hybrid flow-shop or flexible flow shop problems have remained subject of intensive research over several years. Hybrid flow-shop problems overcome one of the limitations of the classical flow-shop model by allowing parallel processors at each stage of task processing. In many papers the assumptions are generally made that there is unlimited storage available between stages and the setup times are neglected or considered independent from sequences of jobs. In this paper we study the hybrid flow shop problems with sequence dependent setup times and processor blocking. We present an effective hybrid genetic algorithm with some state-of-the-art procedures for these NP-hard problems to minimize total completion time or makespan. We established a benchmark to draw an analogy between the performance of our algorithm and RKGA. The obtaining results clearly show the superiority performance of our algorithm.

Supply Chain Operations Reference (SCOR) model is developed and maintained by the Supply Chain Council (SCC). The model is a reference model which can be utilized to map benchmark and improve the supply chain operations. SCOR model provides companies with a basic process modeling tool، an extensive benchmark database by defining a set of supply chain metrics. This paper explains the process and results obtained by applying SCOR model to analyze the supply chain of Iranol Oil Company (IOC). Making numerous interviews with the managers and considering the documents regarding the supply chain processes and comparing the current situation of the supply chain with SCOR best practices، some improvement projects were proposed to improve the supply chain performance. The projects were prioritized using TOPSIS، a well-known multi attribute decision making technique.

Job shop scheduling problem (JSSP), as one of the NP-Hard combinatorial optimization problems, has attracted the attention of many researchers during the last four decades. The overall purpose regarding this problem is to minimize maximum completion time of jobs, known as makespan. This paper addresses an approach to evolving Cellular Learning Automata (CLA) in order to enable it to solve the JSSP by minimizing the makespan. This approach is applied to several instances of a variety of benchmarks and the experimental results show that it produces nearly optimal solutions, compared with other approaches.