JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL
Year:2015 | Volume:11 | Issue:2|Papers Count:10

In today’s fast marketing over the Internet or online, many retailers want to trade at the same time and change their marketing strategy to attract more customers. Some of the customers may decide to cancel their orders partially with a retailer due to various reasons such as increase in customer’s waiting time, loss of customer’s goodwill on retailer’s business, attractive promotional schemes offered by other retailers etc. Even though there is a lag in trading and order cancelation, this paper attempts to develop the retailer’s inventory model with the effect of order cancelations during advance sales period. The retailer announces a price discount program during advance sales period to promote his sales and also he offers trade credit financing during the sales periods. The retailer availing trade credit period from his supplier offers a permissible delay period to his customers. The customer who gets an item has allowed paying on or before the permissible delay period which is accounted from the buying time rather than the start period of inventory sales. This accounts for significant changes in the calculations of interest payable and interest earned by the retailer. The retailer’s total cost is minimized so as to find out the optimal replenishment cycle time and price discount policies through a solution procedure. The results derived in mathematical theorems are implemented in numerical examples and sensitivity analyses on several inventory parameters are obtained.

This paper deals with multi-component machine repair model having provision of warm standby units and repair facility consisting of two heterogeneous servers (primary and secondary) to provide repair to the failed units. The failure of operating and standby units may occur individually or due to some common cause. The primary server may fail partially following full failure whereas secondary server faces complete failure only. The life times of servers and operating/standby units and their repair times follow exponential distribution. The successive over relaxation (SOR) technique has been used to obtain the steady state queue size distribution of the number of failed units in the system. To explore the system characteristics, various performance indices such as expected number of failed units in the queue, throughput, etc. have been obtained. Numerical results have been provided to illustrate the computational tractability of the proposed SOR technique. To examine the effect of system descriptors on the performance indices, the sensitivity analysis is also performed.

The demands for automobiles increased drastically in last two and half decades in India. Many global automobile manufacturers and Tier-1 suppliers have already set up research, development and manufacturing facilities in India. The Indian automotive component industry started implementing Lean practices to fulfill the demand of these customers. United Nations Industrial Development Organization (UNIDO) has taken proactive approach in association with Automotive Component Manufacturers Association of India (ACMA) and the Government of India to assist Indian SMEs in various clusters since 1999 to make them globally competitive. The primary objectives of this research are to study the UNIDO– ACMA Model as well as ISM Model of Lean implementation and validate the ISM Model by comparing with UNIDO–ACMA Model. It also aims at presenting a roadmap for Lean implementation in Indian automotive component industry. This paper is based on secondary data which include the research articles, web articles, doctoral thesis, survey reports and books on automotive industry in the field of Lean, JIT and ISM. ISM Model for Lean practice bundles was developed by authors in consultation with Lean practitioners. The UNIDO–ACMA Model has six stages whereas ISM Model has eight phases for Lean implementation. The ISM-based Lean implementation model is validated through high degree of similarity with UNIDO–ACMA Model. The major contribution of this paper is the proposed ISM Model for sustainable Lean implementation. The ISM-based Lean implementation framework presents greater insight of implementation process at more microlevel as compared to UNIDO– ACMA Model.

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the real-world problems often deal with imprecise or ambiguous data. In this paper, we propose a novel robust data envelopment model (RDEA) to investigate the efficiencies of decision-making units (DMU) when there are discrete uncertain input and output data. The method is based upon the discrete robust optimization approaches proposed by Mulvey et al. (1995) that utilizes probable scenarios to capture the effect of ambiguous data in the case study. Our primary concern in this research is evaluating electricity distribution companies under uncertainty about input/output data. To illustrate the ability of proposed model, a numerical example of 38 Iranian electricity distribution companies is investigated. There are a large amount ambiguous data about these companies. Some electricity distribution companies may not report clear and real statistics to the government. Thus, it is needed to utilize a prominent approach to deal with this uncertainty. The results reveal that the RDEA model is suitable and reliable for target setting based on decision makers (DM’s) preferences when there are uncertain input/output data.

Coordination and harmony between different departments of a company can be an important factor in achieving competitive advantage if the company corrects alignment between strategies of different departments. This paper presents an integrated decision model based on recent advances of geometric programming technique. The demand of a product considers as a power function of factors such as product’s price, marketing expenditures, and consumer service expenditures. Furthermore, production cost considers as a cubic power function of outputs. The model will be solved by recent advances in convex optimization tools. Finally, the solution procedure is illustrated by numerical example.

Cross-docking is a logistic concept, which plays an important role in supply chain management by decreasing inventory holding, order packing, transportation costs and delivery time. Paying attention to these concerns, and importance of the congestion in cross docks, we present a mixed-integer model to optimize the location and design of cross docks at the same time to minimize the total transportation and operating costs. The model combines queuing theory for design aspects, for that matter, we consider a network of cross docks and customers where two M/M/c queues have been represented to describe operations of indoor trucks and outdoor trucks in each cross dock. To prepare a perfect illustration for performance of the model, a real case also has been examined that indicated effectiveness of the proposed model.

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location–allocation problem, which more closely approximates real-world conditions. A multiobjective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

The main research intent of this paper is to introduce the use of fault tree analysis (FTA) and failure mode and effects analysis (FMEA) in conjunction to analyse the risk and reliability of a complex mechatronic system in both qualitative and quantitative manner. The major focus is on handling imprecise and vague information with the help of fuzzy synthesis of information. A complex mechatronic system, i.e. modular automated production system (MAPS), composed of mechanical, electronic, and embedded software subsystems is considered to study the reliability aspects using hybrid FTA. From the results, it is found that the proposed approach models both subjective issues such as human errors along with hardware failures. The in-depth analysis and prioritizing of failure modes using a risk ranking approach based on fuzzy inference system and grey relation approach not only integrate expert judgment, experience, and expertise in more flexible and realistic manner, but also address the limitations associated with traditional procedure of FMEA.

In the present economic context, the operating theater is considered as a critical activity in health care management. This paper describes a model for operating room (OR) planning under constraint of a unique equipment. At first level we schedule elective surgeries under the uncertainty of using a unique equipment. At the second level we consider emergency surgeries, and at the third level a coefficient factor for surgeons is introduced in using this unique equipment. The planning problem consists in scheduling a unique equipment and assigning elective cases to different periods over a planning horizon to minimize the sum of elective patient related costs and overtime costs of ORs. The most important factor that we have focused on this paper is equipment resource constraint. Anew mathematical programming model is first proposed and at the second and third level, a new stochastic mathematical programming model is proposed. Then sample average approximation is presented to approximate the problem with sample size N and then Lingo is used as an exact approach. Because of NP-hardness, exact method does not work for large size problems, so a Metaheuristic approach (differential evolution) is proposed for large size problems. Numerical results show that important gains (approximately 3.5 % in overall cost) can be realized by this stochastic OR planning model.

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.