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Although green logistics has become a new focus of cooperation between government and enterprises under environmental constraints, how local governments formulate subsidy policies to effectively guide the green transformation of regional logistics and how to facilitate the reasonable cost-sharing are rather critical. This paper will deeply explore the dynamic process of the tripartite participation (government, platform, and logistics enterprises) in the selection of regional green logistics strategy, and reveal the evolutionary game relationship of the three parties.

To explore the dynamics involving the government, platform and logistics enterprises for the green logistic transformation, and reveal the evolutionary gaming among the three parties, based on the bounded rationality premise, this study constructs the tripartite asymmetric evolutionary game models, uses the stability theorem of differential equation to explore the evolution and stability strategy of the system in different cases and explicates the paths of influence on the tripartite behaviors via simulations.

Results of this study indicate that there exist stable equilibrium strategies among the three parties regarding the regional green logistics, and they are affected by different factors. The government's subsidy, subsidy intensity and the platform's cost-sharing proportion can generate positive effects, but the latter two can also impact negatively beyond the effective ranges. The findings provide a theoretical basis for local governments, platforms and logistics enterprises to formulate justifiable subsidy intensity and determine reasonable sharing proportion.

Firstly, considering the significant relevance of local government, it is included in the evolution model, and the tripartite game (among government, platform and enterprises) is explored; Secondly, by comparing the equilibrium results under different game conditions, this paper analyzes the evolution of each party's game strategy to achieve the optimal return under bounded rationality and the important factors determining the strategic selection; Finally, the key factor of platform cost sharing is involved, and to what extent the change of platform cost sharing ratio will influence the systematic stability is explored.

The aims of this paper is to investigate simulation‐based optimisation and stochastic dominance testing while employing kanban‐like production control strategies (PCS) operating dedicated and, where applicable, shared kanban card allocation policies in a multi‐product system with negligible set‐up times and with consideration for robustness to uncertainty.

Discrete event simulation and a genetic algorithm were utilised to optimise the control parameters for dedicated kanban control strategy (KCS), CONWIP and base stock control strategy (BSCS), extended kanban control strategy (EKCS) and generalised kanban control strategy (GKCS) as well as the shared versions of EKCS and GKCS. All‐pairwise comparisons and a ranking and selection technique were employed to compare the performances of the strategies and select the best strategy without consideration of robustness to uncertainty. A latin hypercube sampling experimental design and stochastic dominance testing were utilised to determine the preferred strategy when robustness to uncertainty is considered.

The findings of this work show that shared GKCS outperforms other strategies when robustness is not considered. However, when robustness of the strategies to uncertainty in the production environment is considered, the results of our research show that the dedicated EKCS is preferred. The effect of system bottleneck location on the inventory accumulation behaviour of different strategies is reported and this was also observed to have a relationship to the nature of a PCS's kanban information transmission.

The findings of this study are directly relevant to industry where increasing market pressures for product diversity require operating multi‐product production lines with negligible set‐up times. The optimization and robustness test approaches employed in this work can be extended to the analysis of more complicated system configurations and higher number of product types.

This work involves further investigation into the performance of multi‐product kanban‐like PCS by examining their robustness to common sources of uncertainties after they have been initially optimized for base scenarios. The results of the robustness tests also provide new insights into how dedicated kanban card allocation policies might offer higher flexibility and robustness over shared policies under conditions of uncertainty.

Although there are many models of decision making, there are few attempts to match the appropriate decision strategy with different situations. After reviewing and comparing six contemporary decision‐making models, a framework and propositions are developed that match strategies with circumstances. The article concludes with a discussion of theoretical and practical application of the proposed contingency model decision making.

This work aims to present the design of a new continuous tool-path strategy for open-source low-cost fused deposition modeling (FDM) machines, such as Fab@Home or RepRap; and the development of an innovative integrated tool to design and fabricate customized tracheal stents with any FDM machine and six patient parameters. Both contributions were validated and implemented by obtaining a customized medical-grade silicone tracheal stent.

For the design of the new deposition strategy, a Python programming language was used. The new tool-path strategy was proposed as a continuous path to avoid drops and gaps and to improve the accuracy of the final model. Meanwhile, patient parameters were obtained by medical doctors and introduced into the innovative integrated system. On the one hand, one mold generated automatically, and viewed with Matlab® software, was fabricated with a Fab@Home machine, optimized with the continuous tool-path strategy. On the other hand, the same generated mold was viewed in SolidWorks/Excel software and was fabricated using a commercial FDM machine. Finally, the mold was filled with medical grade silicone, and a silicone tracheal stent was obtained.

Path planning for extrusion technologies is still a major concern, especially for open-source FDM machines. The results obtained in this work show the benefits of applying the newly developed continuous tool-path strategy to optimize the performance and efficiency of these machines. In addition, the proposed innovative integrated system allows the fabrication of customized tracheal stents rapidly and affordably.

The possibility of obtaining customized tracheal stents is a worthy challenge. Medical doctors could play a more active role and interact during the design process, helping to obtain more suitable stents. The method proposed herein would provide the opportunity to obtain real values from the trachea of a patient in the operating room and quickly fabricate a customized stent that would fit the patient's trachea perfectly.

The results obtained in this work are relevant and have future applications in both the medical and the additive manufacturing fields. The optimized tool-path strategy can help to improve and enhance the use of low-cost FDM machines. Moreover, the innovative automatic design approach to fabricate tracheal stents may open new market opportunities in the medical device field.

Optimization of energy planning for growth and sustainable development has become very important in the context of climate change mitigation imperatives in developing countries. Existing models do not capture developing country realities adequately. The purpose of this paper is to conceptualizes a framework for energy strategy optimization of the Indian energy sector, which can be applied in all emerging economies.

Hierarchical multi‐objective policy optimization methodology adopts a policy‐centric approach and groups the energy strategies into multi‐level portfolios based on convergence of objectives appropriate to each level. This arrangement facilitates application of the optimality principle of dynamic programming. Synchronised optimization of strategies with respect to the common objectives at each level results in optimal policy portfolios.

The reductionist policy‐centric approach to complex energy economy modelling, facilitated by the dynamic programming methodology, is most suitable for policy optimization in the context of a developing country. Barriers to project implementation and cost risks are critical features of developing countries which are captured in the framework in the form of a comprehensive risk barrier index. Genetic algorithms are suitable for optimization of the first level objectives, while the efficiency approach, using restricted weight stochastic data envelopment analysis, is appropriate for higher levels of the objective hierarchy.

The methodology has been designed for application to the energy sector planning for India's 12th Five Year Plan for which the objectives of faster growth, better inclusion, energy security and sustainability have been identified. The conceptual framework combines, within the policy domain, the bottom‐up and top‐down processes to form a hybrid modelling approach yielding optimal outcomes, transparent and convincing to the policy makers. The research findings have substantial implications for transition management to a sustainable energy framework.

The methodology is general in nature and can be employed in all sectors of the economy. It is especially suited to policy design in developing countries with the ground realities factored into the model as project barriers. It offers modularity and flexibility in implementation and can accommodate all the key strategies from diverse sectors along with multiple objectives in the policy optimization process. It enables adoption of an evidence‐based and transparent approach to policy making. The research findings have substantial value for transition management to a sustainable energy framework in developing countries.

This study aims to avail architects with the potentials of knowledge management (KM) principles towards an optimal and effective procedural mechanism for the choice of building materials during design and construction processes.

In all, 202 questionnaire forms were distributed in a survey. They were administered to practicing architects and Architectural firms in Nigeria. Thereafter, ANOVA, regression analysis and exploratory factor analysis with reliability and Cronbach’s alpha coefficient of 0.861 identified KM principles in specification writing for architects.

These findings show that the building material selection procedure can be optimized with efficient and conscious consideration of KM principles imbibed by architects in tune with global trends. The study serves as a guide to architects and other stakeholders on the effect of KM principles in deepening reflectiveness of the surpassing role of effective KM in specification writing in the construction industry.

This is perhaps the first empirical research that sought to understudy knowledge sharing strategies in architectural firms within the context of the study location Nigeria. The value of the research lies in optimization of architects’ building materials’ specification strategy through KM principles.

This research aims to develop a decision support framework to determine the optimal strategies for mitigating supply chain sustainability (SCS) barriers.

To operationalize the research objectives, both qualitative and quantitative methods were adopted. The qualitative phase comprised a field study, while a quality function deployment approach and optimisation technique were used in the quantitative phase.

This study finds that a lack of support from top management and cost and utility supply problems are the primary barriers to SCS. This study also finds that incentives for suppliers to implement sustainability practices, awareness building among supply chain members and supplier development are the main strategies to mitigate the barriers.

The findings of this study will assist the supply chain managers in prioritizing sustainability barriers and implementing the optimal strategies to mitigate the barriers.

Founded on the stakeholder theory and dynamic capability view, this study developed a unique decision support framework to identify appropriate strategies for mitigating SCS barriers while optimizing the social, environmental and economic objectives of the supply chain.

The research aims to investigate consumer decision‐making strategies using quantitative and qualitative methods. Two decision theories are contrasted: neoclassical theory proposes compensatory and optimising strategies with complete information, whilst bounded rationality theory suggests simplified and non‐compensatory strategies. The research assesses whether these theories will explain consumers' decision‐making strategies when completing a survey, and the extent to which qualitative and quantitative methods provide convergent validity of the explanations.

A computer‐based choice survey was administered to university students and staff. The choice task was to select a preferred potato from sets of potatoes with different attributes. Information for each attribute was initially hidden by a “card”; respondents had to reveal the information by clicking on the card with the computer mouse. The survey recorded the order of mouse clicks, providing quantitative data on decision strategies. Some respondents were also interviewed about their decision processes, which provided qualitative data on strategies.

Respondents left over one‐fifth of the cards unopened. Interview findings confirmed that respondents generally did not obtain all available information and used simplified strategies. The qualitative data were generally validated by the quantitative data and provided improved descriptions of decision strategies.

Some qualitative data can not be verified with quantitative data, and the convenience sample of university students and staff may limit the generalisability of the findings.

The paper suggests that consumers may not use all available information when making choices. The neoclassical assumption of full information may therefore not hold. It also indicates that probing decision strategies with qualitative questioning provides accurate data and better details than could be obtained quantitatively.

The purpose of this paper is to investigate the research approach to optimize shape accuracy, dimensional accuracy and density of customized orthodontic production fabricated by selective laser melting (SLM).

A series of process experiments were applied to fabricating customized brackets directly by SLM, using 316L stainless steel. Shape accuracy can be optimized through the study on fabricating characteristics of non‐support overhanging structure. A scanning strategy combining contour scanning with orthogonal scanning, which differ in scanning speed and spot compensations, was proposed to improve dimensional accuracy. Scanning laser surface re‐melting was added to enhance the SLM density.

Optimized SLM parameters lead to high shape precision of customized brackets, and the average size error of bracket slot is less than 10 μm. The customized brackets density is more than 99 per cent, and the surface quality and mechanical properties meet the requirements.

The paper presents the state of the art in SLM of customized production (especially medical appliances) by optimizing part properties. It is the first time that SLM is employed in the manufacturing of customized orthodontic products. It shows the original research on overhanging structure and compound scanning strategy, approach to optimize SLM part accuracy. An improved laser surface re‐melting is employed in the density optimization.

The main objective of safety instrumented systems (SISs) is to maintain a safe condition of a facility if hazardous events occur. However, in some cases, SIS's can be activated prematurely, these activations are characterized in terms of frequency by a Spurious Trip Rate (STR) and their occurrence leads to significant technical, economic and even environmental losses. This work aims to propose an approach to optimize the performances of the SIS by a multi-objective genetic algorithm. The optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).

The optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).

A case study concerning a safety instrumented system implemented in the RGTE facility has shown the great applicability of the proposed approach and the results are encouraging. The results show that the selection of a good maintenance strategy allows a very significant minimization of the PFDavg, the frequency of spurious trips and Life Cycle Costs of SIS.

The maintenance strategy defined by the system designer can be modified and improved during the operational phase, in particular safety systems. It constitutes one of the least expensive investment strategies for improving SIS performances. It has allowed a considerable minimization of the SIS life cycle costs; PFDavg and the frequency of spurious trips.