Leagile supply chain management has emerged as a proactive approach for improving business value of companies. The companies that face volatile and unpredictable market demand of their products must pioneer in leagile supply chain strategy for competition and various demands of customers. There are literally many approaches for performance metrics of supply chain in general, yet little investigation has identified the reliability and validity of such approaches particularly in leagile supply chains. This study examines the consistency approaches by confirmatory factor analysis that determines the adoption of performance dimensions. The prioritization of performance enablers under these dimensions of leagile supply chain in small and medium enterprises are determined through fuzzy logarithmic least square method (LLSM). The study developed a generic hierarchy model for decision-makers who can prioritize the supply chain metrics under performance dimensions of leagile supply chain.

Many supplier selection problems are involved with various criteria such as quality of supplier, price, delivery time, etc. This paper presents a survey on the implementation of using different multi criteria decision making (MCDM) methods for supplier selection problems. The reviews covers recent advances of MCDM techniques such as data envelopment analysis (DEA), analytical hierarchy process, etc. over the period 2000-2012. The review also reveals that nearly 60% of the applications are associated with business unit, 15% is related to economy, 9% is devoted to service and development and 8% is dedicated to research and development. In our survey, DEA has become the most popular technique for supplier selection problem followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP).

Safety is one of the main reasons for choosing railway to other transportation modes and improvement of transportation safety has attracted many researchers in recent years. In this paper, we aim to investigate the influence of safety measures on railways performance evaluation, empirically. The proposed model of this paper uses data envelopment analysis (DEA) to estimate the railways efficiency scores in the presence of safety measure. According to three proposed factors, the most appropriate model is selected to compare its result with output-oriented DEA model. The results of the survey are surprising since inefficient railroads become efficient through adding undesirable outputs in evaluation model.

This paper presents a strategic multi segment, multi period and multi-product supply chain management to meet reliable networks for handling disruptions strike. We present a mixed-integer programming model whose objective is to minimize the expected cost composed of probability and cost of occurrence in each scenario. The proposed model of this paper considers time value of money for each operation and transportation cost. We attempt to minimize expected costs by considering the levels of inventory, back-ordering, the available machine capacity and labor levels for each source, transportation capacity at each transshipment node and available warehouse space at each destination. The problem is generalized by taking into account backup supplier with reserved capacity and backup transshipment node that, which satisfies demands at higher price without disruption facility. We use a priority-based genetic algorithms encoding to solve the proposed problem under multi period and multi product conditions. The performance of the proposed model is examined using some instances.

In this paper, a technique has been developed to determine the optimum mix of logistic service providers of a make-to-order (MTO) supply chain. A serial MTO supply chain with different stages/ processes has been considered. For each stage different logistic service providers with different mean processing lead times, but same lead time variances are available. A realistic assumption that for each stage, the logistic service provider who charges more for his service consumes less processing lead time and vice-versa has been made in our study. Thus for each stage, for each service provider, a combination of cost and mean processing lead time is available. Using these combinations, for each stage, a polynomial curve, expressing cost of that stage as a function of mean processing lead time is fit. Cumulating all such expressions of cost for the different stages along with incorporation of suitable constraints arising out of timely delivery, results in the formulation of a constrained nonlinear cost optimization problem. On solving the problem using mathematica, optimum processing lead time for each stage is obtained. Using these optimum processing lead times and by employing a simple technique the optimum logistic service provider mix of the supply chain along with the corresponding total cost of processing is determined. Finally to examine the effect of changes in different parameters on the optimum total processing cost of the supply chain, sensitivity analysis has been carried out graphically.

Renewable energy technologies (RET) have faced a number of constraints that have affected their rate of adoption. This can be attributed to the presence of complex dynamic factors associated with the technology adoption process. This paper simulates the dynamic behaviour of the RET adoption process, from a systems dynamics point of view. Complex dynamic interactions between technology adopters, policy makers and policies are captured based on systems thinking concepts. Based on a set of input policy parameters and variables, the behaviour of RET adoption is investigated and analyzed. Sensitivity experiments and further “what-if” experiments are carried out to obtain in-depth understanding of RET adoption process. Useful managerial insights are drawn from the simulation results, relevant to decision makers concerned with renewable energy technology innovations and their adoption.

This study employs the generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology to investigate the return generating process of Jordan, Kingdom of Saudi Arabia (KSA), Kuwait, and Morocco stock market indices. The tradeoff between returns and the conditional variance is found to be positive in all markets. In other words, the empirical findings show that investors are rewarded for their exposure to more risk in these financial markets. This result is consistent with both financial theory and empirical finance.

Today global warming is on the rise and the natural resources are getting consumed at a faster rate. Power consumption has increased many folds to cater the human need. Thus renewable energy resources are the only option available at this juncture. Wind energy is one of the renewable energy. Location selection for wind farm takes an important role on power generation. However, the location selection is a complex multicriteria problem due to the criteria factors which are conflicting in nature as well as uncertain. The process becomes more complex when a group of decision makers are involved in decision making. In the present study, a COPRAS (COmplex PRoportional ASsessment) based multi-criteria decision-making (MCDM) methodology is done under fuzzy environment with the help of multiple decision makers. More specifically, this study is aimed to focus the applicability of COPRAS-F as a strategic decision making tools to handle the group decision-making problems.

In this paper, we examine advanced optimization approach for portfolio problem introduced by Black and Litterman to consider the shortcomings of Markowitz standard Mean-Variance optimization. Black and Litterman propose a new approach to estimate asset return. They present a way to incorporate the investor’s views into asset pricing process. Since the investor’s view about future asset return is always subjective and imprecise, we can represent it by using fuzzy numbers and the resulting model is multi-objective linear programming. Therefore, the proposed model is analyzed through fuzzy compromise programming approach using appropriate membership function. For this purpose, we introduce the fuzzy ideal solution concept based on investor preference and indifference relationships using canonical representation of proposed fuzzy numbers by means of their corresponding?-cuts. A real world numerical example is presented in which MSCI (Morgan Stanley Capital International Index) is chosen as the target index. The results are reported for a portfolio consisting of the six national indices. The performance of the proposed models is compared using several financial criteria.

Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces. This paper presents an improved variant of TLBO algorithm, called Improved Teaching–Learning-Based Optimization (ITLBO). A performance comparison of the proposed method is provided against the original TLBO and some other algorithms. The improved TLBO algorithm shows a marked improvement in performance over the traditional TLBO on several benchmark optimization problems.