This study develops an empirical investigation of trust antecedents and consequences in creating a collaborative business relationship between distribution companies and retailers in the cosmetics market. A conceptual framework based on trust antecedents as inputs and trust consequences as outputs is designed for both parties. In order to evaluate the performance and effectiveness of each considered trust factor for each party, a fuzzy data envelopment analysis (FDEA) based approach is proposed. In order to demonstrate the applicability of the proposed model, a real-life case study is considered. The required data are collected using interview and questionnaires, and the reliability of the collected data is examined using the Cronbach’s alpha. The obtained results indicate that there is no significant difference between both parties’ tendency towards building a collaborative business relationship based on trust. The results also indicate that information sharing is not an effective trust antecedent for both parties. The “product quality” and “product price” are the most effective trust antecedents for retailers, while the “retailer’s financial conflicts records” along with “length of partnership” are the most effective trust antecedents for distribution companies. Finally, the most effective trust consequences for distribution companies and retailers are “information sharing” and “brand advertising”, respectively.

Closed-loop supply chain management is an effective and efficient solution for a set of activities to retrieve a product from a customer and improve its value or to dispose it. Today, designing and planning a closed-loop chain is an inevitable but difficult task. In this research, a scenario-based modeling approach is presented by considering both forward and reverse flows as a closed-loop supply chains in steel industry. The proposed study also develops a multi-product and multi-period model based on a mixed integer linear programming (MILP) approach for profit maximization. The study also considers uncertainty in the amount of raw material, processing, storage and distribution of several products flow. Uncertainty is associated with the quantity and quality of the products in the reverse flow, which are directly affected by customers and sorting centers, respectively. Finally, the model is deployed in Steel industry with real data. The results show that by increasing the quality level of returned products the need for raw materials is reduced and the total profit of the supply chain is increased.

This paper proposes a two-phase heuristic algorithm to solve the crew scheduling problem of the Megabus Bus Rapid Transit System. In the first stage, a division of the original schedules is performed using a recursive algorithm based on dynamic scheduling. In the second stage, work-shift construction based on graph theory is performed using a pairing algorithm (i.e., matching). The method is validated by applying it to the mass transit system of the Central Western Metropolitan Area (AMCO), operated by Integra SA, which serves 11 routes for a daily total of 2899 trips.

Supplier selection is one of the most essential activities in purchase management and plays a crucial role in the production phase. Supplier selection as a vital step of supply chain management is a multi-criteria decision-making issue. For any organization, the process of selecting the best supplier holds variable multilayered complications involving quantitative and qualitative criteria. This paper tackles the supplier selection problem in a Turkish Textile Company. The present study carries out a novel grey integrated multi-criteria approach for enhancing the supplier procedure within Textile Company with the help of the grey analytical hierarchy process G-AHP model for weighting the set of criteria, and the grey weighted aggregated sum product assessment WASPAS-G model for prioritizing the suppliers. The study starts with reviewing the previous works of multi-criteria decision-making MCDM methods and the list of existing criteria evaluation in supplier selection. Then, the range of criteria is selected based on the company requirements and the experts’ interview. In the case study, the consistency rate of the models is tested in order to verify the quality of experts’ judgments. The final results affirm that Grey integrated approach could be efficient and far more precise than the existing models for overcoming the supplier selection and evaluation obstacles in the supply chain management.

The ownership - efficiency relationship in a firm has always been an important topic. In this paper, we focus on the state owned versus non-state-owned status of Vietnamese manufacturing firms, to shed light into the relationship between these two variables when using a more state-of-the-art method as a stochastic meta-frontier method. Applying the method for two periods: one during the global economic crisis and the other after the end of the crisis, the study determines that in both periods, there was a strong evidence that non-state-owned firms performed much better than state owned counterpart. We also found that the difference became even larger during the harsh time and sub-industries with non-state-owned firms could outperform the state-owned firms, significantly.

This study presents a new hybrid framework based on the multi-criteria decision making in order to rank the potential site layout locations by consideration of the cost and safety criteria in the Mehr Construction Project in Tehran, Iran. To this end, all of the criteria in selecting suitable potential locations are extracted from the research literature and the most effective ones, which are matched with existing conditions in Tehran are considered based on the opinion of experts,. Then, the proper locations for site layout are determined as the potential alternatives and ranked by experts based on the structure. According to the data collected from the questionnaires, the weights of the selected criteria are calculated using Best Worst Method (BWM) and the final ranking of the locations is performed using two Gray Relational Analysis and VIKOR methods. The computational results indicate that both VIKOR and GRA methods yield the same ranking. However, a method with higher reliability should be used to select the best potential location of construction site layout. Therefore, the sensitivity analysis of final outputs on the parameters existing in VIKOR and GRA methods is used in order to rank the alternatives and select the best approach. According to the computational results, the GRA method provides higher robustness compared with the VIKOR method. Accordingly, the ranking obtained from the GRA method is employed as the final solution in implementing the case study.

Nowadays, Saudi government has established several strategic tactics such as Saudi Vision 2030 to predict the future of the country. In order to accomplish a superior growth in the economy of the country, mathematical model and forecasting techniques are important tools. In this study, total annual exports and imports of the Kingdom of Saudi Arabia are forecasted using Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models. This paper tries to predict a time series data using ANN and ARIMA models on total annual exports and imports of Kingdom of Saudi Arabia from the year 1968 to the year 2017 with the help of statistical software XLSTAT. The applied models are used to predict some future values of total annual exports and imports of the Kingdom of Saudi Arabia. It is found that the ANN and ARIMA (1, 1, 2) and ARIMA (0, 1, 1) models are suitable for predicting the total annual exports and imports of the Kingdom of Saudi Arabia.

The current paper deals with incomplete Pairwise Comparisons (‘PWs’) when a large number of alternatives is evaluated. PWs are used to quantify decision maker's preferences, both ordinal and cardinal, in multi-criteria decision-making settings for eliciting the priorities of alternative options or weights of criteria. We use additive PWs with a different scale and show how 2-diagonal samples are used to deduce the implied weights thus prioritizing the alternatives. As a consequence, the number of PWs in incomplete judgment decision matrices is greatly reduced while preserving consistency and quality of the results. Computational results are provided and an example from the literature is applied to demonstrate the effectiveness of this method.

Road accidents as a global challenge, imposing irreparable financial and human life losses in almost all countries, especially in developing countries, annually. According to world health organization (WHO), if this trend continues, road accidents will become the 7th cause of human death by 2030. Thus, road safety policy makers have been trying to use safety promotion and preventative actions. In this paper, the road safety performance of Iranian provinces is studied. To evaluate road safety efficiency scores, data envelopment analysis based on road safety (DEA-RS) method in two deterministic and non-deterministic situations is used. To consider the uncertainty in input and output data, this paper develops credibility DEA-RS (CreDEA-RS) model. In fact, the constraints of DEA-RS model are considered as credibility constraints and a counterpart credibility DEA-RS (CreDEA-RS) model is proposed for evaluating road safety of provinces of Iran. According to the results, provinces located in mountainous and forest areas such as Gilan had a much weaker performance than provinces in desert areas such as Yazd.

The main objective of the present study is to investigate the risk factors influencing on construction firm’s project success in Pakistan. The study uses the partial least square structural equation modelling technique to analyse the data collected from 61 construction firms. The study finds positive impacts of design risk, financial risk, technical risk and labour risk on the success of the project. Alternatively, the current study finds no relationship between external risk factors and the success of the project. Furthermore, the current study suggests government and construction firms to consider the risk factors during the decision making for project implementation.