This study considers a supply chain master planning problem in an uncertain environment where operating costs, customer demand, production capacity, manufacturer’s acceptable defective rate, and manufacturer’s acceptable service level are uncertain. Our supply chain consists of one manufacturer, multiple suppliers, and multiple distribution centers. While one objective is to minimize the total costs of logistics that consists of purchasing cost, production cost, and distribution cost, the other objective is to maximize total value of purchasing. These objectives are in conflict with each other. In this paper, the fuzzy multi-objective linear model is applied with α-Cut analysis to achieve the optimal supply chain master planning in an uncertain environment by balancing these two conflicting objectives. The α-Cut analysis is introduced to ensure decision-makers that the outcome satisfies their preferences based on a specified minimum allowed satisfaction value (α).

In the present competitive world, facility location is an important aspect of the supply chain (sc) optimization. It involves selecting specific locations for facility construction and allocation of the distribution channel among different SC levels. In fact, it is a strategic issue which directly affects many operational/tactical decisions. Besides the accessibility, which results in customer satisfaction, the present paper optimizes the establishment costs of a number of distribution channels by considering their proximity to the stock market of the goods they distribute, and proposes mathematical models for two objective functions using the set covering problem. Then, two objective functions are proposed into one through the ε-constraint method and solved by the metaheuristic Genetic Algorithm (GA). To test the resulted model, a smaller scale problem is solved. Results from running the algorithm with different ε-values show that, on average, a 10% increase in ε, which increases the value of the second objective function - distance covered by customers will cause a 2% decrease in the value of the first objective function including the costs of establishing distribution centers). The repeatability and solution convergence of the two-objective model presented by the GA are other results obtained in this study.

In this paper, a novel multi-objective robust possibilistic programming model is proposed, which simultaneously considers maximizing the distributive justice in relief distribution, minimizing the risk of relief distribution, and minimizing the total logistics costs. To effectively cope with the uncertainties of the after-disaster environment, the uncertain parameters of the proposed model are considered in the form of fuzzy trapezoidal numbers. The proposed model not only considers relief commodities priority and demand points priority in relief distribution, but also considers the difference between the pre-disaster and post-disaster supply abilities of the suppliers. In order to solve the proposed model, the LP-metric and the improved augmented ε-constraint methods are used. Second, a set of test problems are designed to evaluate the effectiveness of the proposed robust model against its equivalent deterministic form, which reveales the capabilities of the robust model. Finally, to illustrate the performance of the proposed robust model, a seismic region of northwestern Iran (East Azerbaijan) is selected as a case study to model its relief logistics in the face of future earthquakes. This investigation indicates the usefulness of the proposed model in the field of crisis.

This study investigates the relationships between individual characteristics (including outcome expectation; perceived relevance of training; trainee ability) and transfer of technical training (TTT) in Vietnamese enterprises. Quantitative method was employed to examine how individual characteristics influence TTT. A self-completion questionnaire was administered to 185 employees in seven Vietnamese enterprises. All these employees received training from their enterprise. The results show that three predicted individual factors significantly influenced on TTT. Based on the findings, this study suggest that in order to motivate trainee’s efforts to transfer, organisational changes should be conducted to offer bonus as well as perceived relevance of training succeeding training activities.

No-wait flow shop scheduling refers to continuous flow of jobs through different machines. The job once started should have the continuous processing through the machines without wait. This situation occurs when there is a lack of an intermediate storage between the processing of jobs on two consecutive machines. The problem of no-wait with the objective of minimizing makespan in flow shop scheduling is NP-hard; therefore the heuristic algorithms are the key to solve the problem with optimal solution or to approach nearer to optimal solution in simple manner. The paper describes two heuristics, one constructive and an improvement heuristic algorithm obtained by modifying the constructive one for sequencing n-jobs through m-machines in a flow shop under no-wait constraint with the objective of minimizing makespan. The efficiency of the proposed heuristic algorithms is tested on 120 Taillard’s benchmark problems found in the literature against the NEH under no-wait and the MNEH heuristic for no-wait flow shop problem. The improvement heuristic outperforms all heuristics on the Taillard’s instances by improving the results of NEH by 27.85%, MNEH by 22.56% and that of the proposed constructive heuristic algorithm by 24.68%. To explain the computational process of the proposed algorithm, numerical illustrations are also given in the paper. Statistical tests of significance are done in order to draw the conclusions.

This study is conducted to investigate the impact of corporate social responsibility (CSR) on supply chain management (SCM) and financial performance, and the effect of SCM on financial performance through the mediate role of corporate reputation and customer loyalty. In addition, the study tests the moderate role of firm size and the ratio of export products in the relationship between SCM and financial performance. The research sample is 389 Vietnamese garment and textile firms by collecting through survey questionnaire. The results show that corporate social responsibility has a positive impact on SCM and financial performance and SCM has a positive impact on financial performance. However, firm reputation and customer loyalty do not have any mediate role in the relationship between SCM and financial performance. Firm size and export product ratio play a moderate role with statistical significance in the relationship between SCM and financial performance.

In this paper, a new hybrid algorithm based on multi-objective genetic algorithm (MOGA) using simulated annealing (SA) is proposed for scheduling unrelated parallel machines with sequence-dependent setup times, varying due dates, ready times and precedence relations among jobs. Our objective is to minimize makespan (Maximum completion time of all machines), number of tardy jobs, total tardiness and total earliness at the same time which can be more advantageous in real environment than considering each of objectives separately. For obtaining an optimal solution, hybrid algorithm based on MOGA and SA has been proposed in order to gain both good global and local search abilities. Simulation results and four well-known multi-objective performance metrics, indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA) and SA in terms of each objective and significantly in minimizing the total cost of the weighted function.

Digitalization plays a crucial role on current competitive market and it is associated with the utilization of digital technologies to change business model and to deliver new value-producing opportunities to the current business environment in an aim to move towards a digital business. Therefore, to address new business model, the purpose of this study is to explore the effect of digital customer experience and electronic word of mouth on sustainable performance of supply chain through brand image and supply chain image. In this study, data is collected from employees of Indonesian ICT companies. Clusters were formulated, and respondents were selected randomly. Three hundred and fifteen (315) responses were analysed with the help of structural equation modelling. The results indicate that digital customer experience and electronic word of mouth played positive role on promoting brand image. Brand image had positive role to promote supply chain image which influences positively on sustainable supply chain performance. Therefore, this study is important for Information Communication and Technology (ICT) industry to implement various e-commerce practices to promote digital customer experience and electronic word of mouth.

Digitalization is impacting almost every aspect of people’s life and is greatly affecting supply chain processes. At the same time, supply chains are becoming progressively more aware of their impacts on people, the planet and profits (triple bottom line perspective) and the concept of sustainable supply chain has received increasing attention among scholars and practitioners. In this context, this article aims to analyze scientific research on supply chain management in the field of sustainability and digitalization using a bibliometric approach. To this end, this article analyses 484 papers from Scopus database according to the following parameters: chronology, discipline, source, center, country and citations. The dataset consisted of articles from scientific journals published over the period 2002–2018. In addition, VOSviewer software is used to map the bibliographic material using the co-occurrence of keywords and most influential journals. The analysis reveals that: sustainability and digitalization are important emerging fields within supply chains that have increased significantly during the last ten years; the USA is the most influential country; and the topic is structured around 2 clusters (1) Supply chain management, concepts and practices and (2) energy related technologies. The results may facilitate the planning, designing, running and publishing of future research on this topic.

This study aims to investigate the multi-item inventory model in a production/rework system with multiple production setups. Rework can be depicted as the transformation of production rejects, failed, or non-conforming items into re-usable products of the same or lower quality during or after inspection. Rework is very valuable and profitable, especially if materials are limited in availability and also pricey. Moreover, rework can be a good contribution to a ‘green image environment’. In this paper, we establish a multi-item inventory model to determine the optimal inventory replenishment policy for the economic production quantity (EPQ) model for imperfect, deteriorating items with multiple productions and rework under inflation and learning environment. In inventory modelling, Inflation plays a very important role. In one cycle, production system produces items in n production setups and one rework setup, i.e. system follows (n, 1) policy. To reduce the deterioration of products preservation technology investment is also considered in this model. Holding cost is taken as time dependent. We develop expressions for the average profit per time unit, including procurement of input materials, costs for production, rework, deterioration cost and storage of serviceable and reworkable lots. Using those expressions, the proposed model is demonstrated numerically and the sensitivity analysis is also performed to study the behaviour of the model.