Supply chain network design problem is increasingly showing its importance, especially the perishable supply chain. This research develops a multi-objective mathematical model to design four-echelon intermodal multi-product perishable supply chain configuration in order to ensure a balance of the three pillars of sustainable development: economy, environment, and society. The optimization objective functions of the model are, respectively, minimizing costs, delivery time, emissions, and the supply-demand mismatch in time. The model addresses particular problems in the supply chain of fresh fruits, which is more challenging compared to other types of perishable products due to its seasonal characteristics. The study proposes a new approach that combines and standardizes the above objective functions into a single weighted objective function. The solution from the model supports the decision-making process at both strategic and tactical levels. Strategically, the model supports decisions about the location, size of facilities, product flows, and workforce level. Tactically, the decision variables provide information on harvest time, delivery time, the delivery route, and mode of transport. To demonstrate its practical applicability, the model is applied to Mekong Delta region, Vietnam, where a variety of fruit types, large yields, and high distribution demand in this region make designing a shared supply chain desirable for its overall economic, environmental, and social concerns. Moreover, sensitivity analysis regarding weights of different objectives is performed to assess possible changes in supply chain configurations. Application of this model to other perishable products, the addition of modes of transport, social policy, and uncertainty parameters may be suggested for future research.

Despite the high capital requirements, offshore wind and wave energy integrated stations (WWS) are an emerging and potential solution to optimize efficiency in renewable energy development. Decisions about installation location significantly influence their efficiency. This study examines and determines highly efficient and sustainable locations based on quantitative indicators and qualitative criteria. For this purpose, a novel dual-side behavioral spherical fuzzy multi-criteria decision-making (MCDM) approach was developed and applied for the case study of Vietnam. In the first stage, the behavioral Data Envelopment Analysis (B-DEA) model, constructed based on prospect theory, is applied to analyze locations according to quantitative indicators under decision makers’ psychological behavior consideration. In the second stage, a spherical fuzzy extension of the integration composed of the DEMATEL (decision-making trial and evaluation laboratory) and the EDAS (evaluation based on distance from average solution) methods helped to evaluate the locations. Based on the convergence in qualitative and quantitative analysis results, efficiency–sustainability positioning maps are established. The research provides recommendations for appropriate WWS locations from that visualization. The research compared findings with current development projects, plans, and policies in Vietnam for validation.

This study aims to develop a novel and robust simulation-based integration framework to identify optimal locations for wave energy projects in Australia. The process of multi-criteria evaluating and selecting locations based on feasible conditions, published numerical data, and linguistic judgments of experts. With this aim in mind, this paper proposes the first combination of Data Envelopment Analysis (DEA) approach, Fuzzy Best-Worst Method (Fuzzy BWM), and Simulation-based Fuzzy Multi-Criteria Interactive Decision-Making method (Fuzzy TODIM). Firstly, the DEA model helps to filter out potentially and highly effective locations from feasible locations. Afterward, Fuzzy BWM is a worthy and effective alternative to Analytic Hierarchy Process (AHP) in weighting criteria. Finally, the simulation-based Fuzzy TODIM method evaluates potential locations, taking into account both the consideration of the criteria interaction and the decision maker’s aversion to loss. Based on the simulation results, the study finds the thresholds of the loss attenuation coefficient that change the ranking of potential locations. Last but not least, to validate the ranking result, the comparison shows that the proposed method’s results have a high degree of similarity with actual wave energy projects in Australia. Accordingly, governments, investors, and institutions can sustainably optimize the efficiency of wave energy projects by applying the proposed model. Moreover, the integrated framework can provide robust solutions to other multiple criteria decision-making problems.

The global manufacturing supply chain has been disrupted by the negative impacts of the pandemic. In Southeast Asia, Vietnam’s manufacturing industry is one of the most strongly integrated with global and regional supply chains. The production strategies in the “new normal” are the key solution to the survival and sustainable development of manufacturers. This study aims to develop a two-stage framework to investigate the impacts of COVID-19 and the post-COVID-19 production strategies for Vietnam’s manufacturing industry. As a theoretical contribution, this study proposes a novel and robust integration approach, which combines the Ordinal Priority Approach (OPA) and Fuzzy Evaluation Based on Distance from Average Solution (Fuzzy EDAS), for the first time. The negative impacts of the pandemic were identified and weighted by the OPA method. Then, production strategies were comprehensively evaluated using the Fuzzy EDAS method. Findings indicate that digitization and on-site renewable energy are the most essential recovery strategies for manufacturing in Vietnam. These findings are validated by comparisons with the results of recent multiple criteria decision-making (MCDM) methods. Furthermore, weight sensitivity analysis reveals different suitability of strategies for short-term and long-term negative impacts. As a managerial implication, the multi-scenario ranking results help managers to make resource-allocation decisions for the implementation of post-COVID-19 production strategies.

Purpose: Business Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence. Design/methodology/approach: The BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness. Findings: The findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities. Research limitations/implications: The single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework. Originality/value: The experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

Production planning is a necessary process that directly affects the efficiency of production systems in most industries. The complexity of the current production planning problem depends on increased options in production, uncertainties in demand and production resources. In this study, a stochastic multi-objective mixed-integer optimization model is developed to ensure production efficiency in uncertainty conditions and satisfy the requirements of sustainable development. The efficiency of the production system is ensured through objective functions that optimize backorder quantity, machine uptime and customer satisfaction. The other three objective functions of the proposed model are related to optimization of profits, emissions, and employment changing. The objective functions respectively represent the three elements of sustainable development: economy, environment, and sociality. The proposed model also assures the production manager’s discretion over whether or not to adopt production options such as backorder, overtime, and employment of temporary workers. At the same time, the resource limits of the above options can also be adjusted according to the situation of each production facility via the model’s parameters. The solutions that compromise the above objective functions are determined with the Chebyshev goal programming approach together with the weights of the goals. The model is applied to the multinational production system of a Southeast Asian supplier in the textile industry. The goal programming solution of the model shows an improvement in many aspects compared to this supplier’s manufacturing practices under the same production conditions. Last but not least, the study develops different scenarios based on different random distributions of uncertainty demand and different weights between the objective functions. The analysis and evaluation of these scenarios provide a reference basis for managers to adjust the production system in different situations. Analysis of uncertain demand with more complex random distributions as well as making predictions about the effectiveness of scenarios through the advantages of machine learning can be considered in future studies.