This article discusses the ecological vehicle routing problem with a stop at a refueling station titled Green-Vehicle Routing Problem. In this problem, the refueling stations and the limit of fuel tank capacity are considered for the construction of a tour. We propose a variable neighborhood search to solve the problem. We tested and compared the performance of our algorithm intensively on datasets existing in the literature.

Financial management faces 3 important decisions, namely funding, investment and dividends. The purpose of this study is to determine the effects of the fundamental factors such as leverage in financial management. Leverage is one of the factors of funding, investment and dividends that interdependently affect the value of the company. The method used in this study uses 2SLS (two-stage least square). There are 4 (four) equations in this study, namely the equation of leverage, investment, dividend and company value. Interdependence is shown based on leverage, investment and dividends. In the interdependence equation leverage, investment and dividends not only on one side act as endogenous variables but also act as exogenous variables on the equation of the company values. The results of the research on leverage equations show that investment affects leverage but dividends do not affect leverage. The investment equation shows the results of both leverage and dividend variables would not affect investment decision. Dividend equation also shows that leverage and investment variables also do not affect dividends. Moreover, the results of the company value equation show that the leverage variable would not affect the value of the company. But in-vestment and dividend variables affect the value of the company. The conclusion of this study is that there was a mutually influential relationship between the three variables of financial management before influencing company value.

Decision making in supply chains is influenced by demand variations, and hence sales, purchase orders and inventory levels are therefore concerned. This paper presents a non-linear optimization model for a two-echelon supply chain, for a unique product. In addition, the model includes the consumers’ maximum willingness to pay, taking socioeconomic differences into account. To do so, the constrained multinomial logit for discrete choices is used to estimate demand levels. Then, a metaheuristic approach based on particle swarm optimization is proposed to determine the optimal product sales price and inventory coordination variables. To validate the proposed model, a supply chain of a technological product was chosen and three scenarios are analyzed: discounts, demand segmentation and demand overestimation. Results are analyzed on the basis of profits, lotsizing and inventory turnover and market share. It can be concluded that the maximum willingness to pay must be taken into consideration, otherwise fictitious profits may mislead decision making, and although the market share would seem to improve, overall profits are not in fact necessarily better.

The purpose of this research is to develop an early warning system model that can anticipate the occurrence of delisting of Islamic stocks (ISSI) using Support Vector Machines (SVM). Financial variables used consist of debt to equity, return on invested capital, asset turn over, quick ratio, current ratio, return on assets, return on equity, leverage, long term debt, and interest coverage. The population of this study is 335 sharia shares registered at ISSI in the period 2012-2017, with a total sample of 102 companies. The results show that the financial variables had a predictive power to the occurrence of delisting of Islamic stocks in the ISSI index. The effect of the independent variable or predictor variable is the financial ratio to the target variable or the dependent variable that is the potential for delisting of Islamic stocks in the ISSI index. With the development of 4 SVM models with different levels of prediction accuracy, SVM Model 1 with an accuracy rate of 71.57%, SVM Model 2 with an accuracy rate of 72.55%, SVM Model 3 with an accuracy rate of 82.35% and SVM Model 4 with an accuracy rate of 100%, it can be concluded that the SVM Model 4 is the best model.

This paper presents a new method for selection of optimal options portfolios. The problem of defining optimal portfolios of real options is formulated as integer programming. The algorithm of generating an optimal portfolio of real options is also presented. The incremental benefit of portfolio of real options is valued using Monte Carlo simulation and modeling the prices and demand as Geometric Brownian Motion. The presented method allows to select optimal portfolios of real options with consideration of statistical and qualitative dependences of options. The results show that real options can generate a significant increase in the net present value (NPV).

Management towards control of risk issues appeared as an important area in E-supply chain for researchers in the present fast growing market. Extensive research has been accomplished in this area, but still there are several risks and uncertainties. The present research aims to identify and consolidate various e-supply risk factors for developing a constructive measurement model. The study also assesses the influence of risk factors over e-supply chain operation in Indian mechanical industries. Thus, after a thorough research and detailed discussion, 38 Risks factors are identified through literature to prepare the questionnaire. A questionnaire based survey is carried out for collecting the primary data from 148 experts of mechanical manufacturing industries located in the national capital region of India. The research methodology is combined with descriptive statistics and factor analysis. SPSS 21 software tools is used for investigating the reliability and Amos Graphics 21 software is used for the fitment validation of the theoretical construct. The results suggest that all risk issues create significant negative influence e-supply chain process. The results also show negative effects of risks over e-supply chain performance. The current research outcome develops a stochastic model based on the e-supply chain risk factors used for reducing risks in e-supply chain operation.

This study applied the Constructal Design Method (CDM) associated with the Finite Element Method (FEM) through computational models to perform a geometric analysis on rectangular stiffened plates of steel subjected to a uniform transverse loading, in order to minimize its maximum and central out-of-plane deflections. Considering a non-stiffened plate as reference and maintaining the total volume of steel constant, a portion of material volume deducted from its thickness was transformed into stiffeners through the ϕ parameter, which represents the ratio between the material volume of the stiffeners and the reference plate. Adopting ϕ = 0.30, 27 geometric arrangements of stiffened plates were established, being 9 arrangements for each 3 different stiffeners' thicknesses adopted: t_s = 6.35 mm, t_s = 12.70 mm and t_s = 25.40 mm. For each ts value, the number of longitudinal (N_ls) and transverse (N_ts) stiffeners were varied from 2 to 4. Thus, in each plate arrangement configured, the influence of the ratio between the height of the transverse and longitudinal stiffeners (h_ts/h_ls) was analyzed, taking into account the values 0.50; 0.75; 1.00; 1.25; 1.50; 1.75 and 2.00, regarding to the maximum and central deflections. The results have shown that transforming a portion of steel from a non-stiffened reference plate into stiffeners can reduce the maximum and central deflections by more than 90%. Moreover, it was observed that to reduce the deflections it is more effective consider h_ts > h_ls, once the ratio hts/h_ls = 2.00 was the one that led to the better mechanical behavior among the analyzed cases.

In the present work, a recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of fabric finishing system of a textile industry. Fabric Finishing System has four main subsystems, arranged in hybrid configuration. For performance modeling and analysis of availability, a performance evaluating model of fabric finishing system has been developed with the help of mathematical formulation based on Markov-Birth-Death process using Probabilistic Approach. Then, the overall performance of the concerned system has first analyzed and then, optimized by using teaching–learning-based optimization (TLBO). The results of optimization using the proposed algorithm are validated by comparing with those obtained by using the genetic algorithm (GA) on the same system. Improvement in the results is obtained by the proposed algorithm. The results of effect of variation of the algorithm parameters on fitness values of the objective function are reported.

The performance of sustainable supply chain management today, especially for palm oil, continues to experience a drastic decline from the social, economic, and environmental perspectives. Both the supply and demand sides are undergoing severe disruption due to the COVID-19 pandemic. To survive the COVID-19 situation and afterward, the palm oil industry needs to focus on priority indicators for immediate improvement. For that reason, our study aims to determine the primary indicators used to assess the performance of sustainable supply chain management to improve the palm oil industry's performance immediately. The F-AHP method is used to rank which indicators are focused on the COVID-19 situation and thereafter. The findings of this study designate that there are three main indicators, namely from the economic side (adaptability), the social side (improving employee health and safety), and the environmental side (sustainable supplier management). This finding is beneficial for the industry and for supply chain actors such as suppliers, customers, and the government in taking attitudes and setting policies related to sustainable supply chain management in the face of pandemic.

Work engagement has gained much prominence over the recent decades. With the aim of address-ing the global engagement crisis, the current study examined how prominent job resources such as supervisor support, coworker support and meaningful work perceptions can influence work engagement. The study also tested the moderation of meaningful work on these relationships. 537 questionnaires were distributed among the employees of six large banks of Pakistan. Structural equation modeling using Smart PLS 2.0 resulted in strong positive relationship of supervisor support and coworker support with work engagement. The findings also concluded strong positive relationship between meaningful work and work engagement. Notably, the bootstrapping results also played significant moderation of meaningful work on the relationship between supervisor support and coworker support with work engagement. The study forwards theoretical and practical implications for future.