We propose and develop a new G-optimality criterion using the concept of weighted optimality criteria and certain additional generalizations. The goal of the weighted G-optimality is to minimize a weighted average of the maximum scaled prediction variance in the design region over a set of reduced models. A genetic algorithm (GA) is used for generating the weighted G-optimal exact designs in an experimental region for mixtures. The performance of the proposed GA designs is evaluated and compared to the performance of the designs produced by our genetic algorithm and the PROC OPTEX exchange algorithm of SAS/QC. The evaluation demonstrates the advantages of GA designs over the designs generated using exchange algorithm, showing that the proposed GA designs have better model-robust properties and perform better than the designs generated by the PROC OPTEX exchange algorithm.

This paper proposes a new hybrid algorithm to solve the multi-compartment vehicle routing problem (MCVRP) with a heterogeneous fleet of vehicles for the fuel delivery problem of a previous study of twenty petrol stations in northeastern Thailand. The proposed heuristic is called the Fisher and Jaikumar Algorithm with Adaptive Large Neighborhood Search (FJA-ALNS algorithm). The objective of this case is to minimize the total distance, while using a minimum number of multi-compartment vehicles. In the first phase, we used the FJA to solve the MCVRP for the fuel delivery problem. The results from solving the FJA were utilized to be the initial solutions in the second phase. In the second phase, a hybrid algorithm, namely the FJA-ALNS algorithm, has been developed to improve the initial solutions of the individual FJA. The results from the FJA-ALNS algorithm are compared with the exact method (LINGO software), individual FJA and individual ALNS. For small-sized problems (N=5), the results of the proposed FJA-ALNS and all methods provided no different results from the global optimal solution, but the proposed FJA-ALNS algorithm required less computational time. For larger-sized problems, LINGO software could not find the optimal solution within the limited period of computational time, while the FJA-ALNS algorithm provided better results with much less computational time. In solving the four numerical examples using the FJA-ALNS algorithm, the result shows that the proposed FJA-ALNS algorithm is effective for solving the MCVRP in this case. Undoubtedly, future work can apply the proposed FJA-ALNS algorithm to other practical cases and other variants of the VRP in real-world situations.

The objective of the current study is to examine the role of Industry 4.0 in supply chain sustainability (SCS). To examine the effect of Industry 4.0 on SCS, the big data technology is considered. As the supply chain process requires a significant data handling system among the companies, however, companies are lacking in this area. Therefore, the relationship between data storage, data transformation, data utilization, order management and SCS were examined. Data was collected from the rubber industry. For the purpose of data collection, questionnaires were used, and data were collected from the rubber companies of Indonesia. Results of this study shows that Industry 4.0 has a vital role in SCS. Implementation of Industry 4.0 among the rubber companies shows a positive effect on SCS. Particularly, the applications of big data technology have a vital role in order management and SCS. Big data technology has a significant positive effect on SCS. Big data technology has a positive role to promote order management which further influences positively on SCS.

The objectives of this research are to find the proper measures to improve efficiency of Thai-Lao cross border transportation (R9 route) for Thai shippers and study their performance after implementing the developed measures. This research implements both quantitative and qualitative research, where the population of this research consists of shipper’s groups which transport products via the Thai Lao border located along R9 route. The research tools are questionnaire and structured interview form passed variability and reliability tests. Then the questionnaires are distributed to 3 large size firms (sample size of 316 people) and an in-depth interview with 18 people. This research analyzes gathered data using descriptive analysis, construct validity, structural equation modeling (SEM), path analysis, and content analysis for qualitative data. The analysis results reveal important findings according to each research objective as follows. For the first objective, to improve cross-border transportation, the case study firms should concentrate on three measures as: 1) transportation safety in terms of sound safety control and monitor, increasing rest areas and truck stops including those on pavement or roadside, safety monitoring during transportation, and weather conditions checking; 2) documentation procedure in terms of reducing the procedure steps, IT implementation, and procedure improvement both inbound and outbound; and 3) vehicles management in terms of appropriate resources allocation, facilities availability, and appropriate resources selection. For the second objective, the study of the case study firms’ performance after implementing the developed measures, it reveals benefits in several aspects of 1) cheaper (they can deliver items with lower costs, lower expenses, or lower price than before implementation as well as other competitors); 2) faster (better customers responsiveness, faster delivery and operating than other competitors, and lead time reduction), and 3) better (in operations with less bottlenecks, mistakes, and disruptions manifested in delivery; higher competitiveness and better service quality). To summarize, the implementation of the developed model can help the shippers to increase their capability as well as their competitiveness.

The objective of the current study is to examine the effect of fast delivery and accidental management on sustainable logistics growth. The study examined the relationship between fast delivery, accidental management, top management and sustainable logistics growth. Top management is used as a moderating variable. In this study, the Thai logistic companies are investigated, therefore, the population of the study is on logistics companies of Thailand. Data were collected from the employees of logistics companies to examine the effect of fast delivery, accidental management and top management on sustainable logistics growth. A survey was carried out and 450 questionnaires were distributed among the employees. Results of the study show that sustainable logistics growth is the most important for the logistics companies influenced by the fast delivery, accidental management and top management. Fast delivery has a positive effect on sustainable logistics growth. Increase in fast delivery increases the sustainable logistics growth. Moreover, accidental management also has a positive effect on sustainable logistics growth. Better accidental management has a positive role to enhance sustainable logistics growth. Additionally, top management also shows a positive role in sustainable logistics growth.

The objective of this study is to examine the role of employee communication skills in sustainable export performance. Furthermore, this study also examined the mediating role of client understanding, client satisfaction and client attraction. Therefore, the relationship between employee communication skills, client understanding, client satisfaction, attraction and sustainable export performance was examined. Population of the study is logistic companies of Thailand. The logistic company employees involved in export activities were selected as respondents. Therefore, data were collected from the export related employees of Thai logistic companies. Finally, data were analyzed and found that communication skills are playing a major role in export performance. Export performance is majorly influenced by the communication skills of employees working in logistic companies. Furthermore, communication skills have a positive role in client understanding. Client understanding has a positive effect on client satisfaction and client attraction. This whole mechanism increases the sustainable export performance.

In this study, under the carbon cap-and-trade mechanism, the ordering cost presents a stepwise function for ordering quantity, and the optimal economic ordering quantity model aims to explore the manufacturer's total cost minimization in the finite planning horizon, in combination with the actual situation that the product will produce carbon emissions during transportation and storage. The economic order quantity (EOQ) model with stepwise ordering cost is applicable to the decision environment in which goods are utilized by sea, by rail or by air (e.g., the order cost is charged in addition to the basic fixed cost, the importer of raw materials will pay an additional freight related to delivery, such as the rent for the use of container numbers.). A heuristic algorithm is also proposed to analyze the relevant properties of the optimal solution of the model and to solve the optimal order times and quantities of the manufacturer under the constraint of carbon policy.We further compared the optimal order times with the case without carbon constraint and the order times corresponding to the manufacturer's realization of the minimum carbon emission, and obtained the conditions for the manufacturer to achieve low cost and low emission under the carbon policy.Finally, the theoretical results of the model are verified by numerical examples,and the influence of relevant parameters on the inventory strategy of manufacturers is discussed. The results show that under the carbon cap-and-trade policy, there is an optimal ordering strategy that minimizes the total cost of the manufacturer in the finite horizon. When the demand of the manufacturer is under finite horizon and the carbon policy is equal to the specific multiplier of orders, the manufacturer can achieve a win-win result of low cost and low emissions.

A new approach is applied in the process of measuring the efficiency of decision-making units (DMUs) through the cross-efficiency evaluation method. Ideal and Anti-Ideal models are generated to form a comprehensive method based on the cross-efficiency evaluation method. The two models are formulated and combined to the Data Envelopment Analysis using the CRITIC method. In a comparative analysis based on three numerical examples, the proposed approach can lead to achieving a more reliable result than one based on an individual method.

The primary objective of this study is associated with the management of educational system in schools using Three Law Auguste Comte at Pesantren Tebuireng Jombang, East Java, Indonesia. Since June 2006 the Tebuireng Islamic Boarding School has been providing the Islamic education. However, in these schools, the students coming from other schools do not have Islamic education and face difficulty while taking courses. Additionally, the students of Tebuireng Islamic Boarding Schools are not familiar with traditional education, such as science-based subjects; mathematics, chemistry, physics, biology and management. Therefore, man-agement of educational system is required in Tebuireng Islamic Boarding Schools. For this purpose, data were collected from the teachers of these schools by using a questionnaire survey. Partial Least Square (PLS)-Structural Equation Modeling (SEM) was used to analyze the data. Findings of the study revealed that all levels of education in Islamic schools (Pesantren) should provide science-based subjects rational, among others: mathematics, chemistry, physics, biology, management, etc. Moreover, there should be a balance between Islamic education and science-based education.

This paper has been retracted.

Precast concrete (PC) can potentially improve construction industry projects when used as a primary process in the precast concrete supply chain (PCSC). Successful inventory reduction and on-time delivery are essential in the PCSC because of critical scheduling and cost factors. To solve these challenges, this study applied Just-In-Time (JIT) strategies in the PCSC using the Discrete-Event Simulation (DES) model to help manufacturers identify the optimal decision. The factors considered were offsite inventory levels (based on days buffered) and batch delivery decisions that reduced supply penalties and improved construction performance. The model was validated based on a precast project experiment in Vietnam; the model provided a saving of 52% in manufacturer penalties and reduced construction idle work by 77%. An optimal combined decision was provided for the manufacturer, such as a 1day-4batch decision with maximum benefit and a 2day-2batch decision for safety using an uncertainty risk consideration in the PC wall panel study case. This research contributes a tool to help manufacturers in decision making and promotes the use of JIT applications/strategies in the PCSC process.