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The purpose of this paper is to present a new system frequency response model with participation of wind-hydro-thermal units to overcome frequency deviations.

The extracted minimum frequency equation is considered as a constraint in security-constrained unit commitment calculations. Because of high-order polynomials in the frequency transfer function and high degree of nonlinearity of minimum frequency constraint, Routh stability criterion method and piecewise linearization technique are used to reduce system order and linearize the system frequency response model, respectively.

The results of this paper indicate that by using this model, the hourly minimum frequency is improved and is kept within defined range.

This combined model can be used to evaluate the frequency of the power system following unexpected load increase or generation disturbances. It also can be used to investigate the system frequency performance and ensure power system security which are caused by peak load or loss of generation in presence of renewable energies.

This paper aims to address a distributed assembly permutation flow-shop scheduling problem (DAPFSP) considering budget constraints and factory eligibility. The first stage of the considered production system is composed of several non-identical factories with different technology levels and so the factories' performance is different in terms of processing time and cost. The second stage is an assembly stage wherein there are some parallel work stations to assemble the ready parts into the products. The objective function is to minimize the maximum completion time of products (makespan).

First, the problem is formulated as mixed-integer linear programing (MIP) model. In view of the nondeterministic polynomial (NP)-hard nature, three approximate algorithms are adopted based on variable neighborhood search (VNS) and the Johnsons' rule to solve the problem on the practical scales. The proposed algorithms are applied to solve some test instances in different sizes.

Comparison result to mathematical model validates the performance accuracy and efficiency of three proposed methods. In addition, the result demonstrated that the proposed two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm outperforms the other two proposed methods. Moreover, the proposed model highlighted the effects of budget constraints and factory eligibility on the makespan. Supplementary analysis was presented by adjusting different amounts of the budget for controlling the makespan and total expected costs. The proposed solution approach can provide proper alternatives for managers to make a trade-off in different various situations.

The problem of distributed assembly permutation flow-shop scheduling is traditionally studied considering identical factories. However, processing factories as an important element in the supply chain use different technology levels in the real world. The current paper is the first study that investigates that problem under non-identical factories condition. In addition, the impact of different technology levels is investigated in terms of operational costs, quality levels and processing times.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

The optimal payment in the whole operation and maintenance period of water environment treatment PPP projects has become the main approach to realize sustainable development of projects. This study is aimed at constructing an effective payment model for the whole life period of projects to achieve win-win among all stakeholders, so as to provide a theoretical reference and managerial implications for the public sector in the whole operation and maintenance period.

In the whole operation and maintenance period of water environment treatment PPP projects, this article investigates how the public sector optimizes the payment in the whole operation and maintenance period of projects. Firstly, the projects' whole operation and maintenance period is divided into several stages according to the performance appraisal period. And then, the multi-stage dynamic programming model is constructed to design the payment construct model for the public sector in each performance appraisal stage. The payment from the public sector is the decision variable, and the deduction from the private sector is a random variable.

The optimal payment model showed that the relatively less objective weight of public sector leaded to its relatively more total payment and vice versa. Therefore, the sustainable development of the projects can only be ensured when the objective weights both of them should be balanced. Additionally, the deduction from the performance appraisal of private sector plays an important role in the model construction. The larger deduction the private sector undertakes, the smaller profits private sector has. Since the deduction at each stage is a random variable, the deduction varies with the different probability distributions obeyed by the practical deduction in each stage.

The findings from this study have provided theoretical and application references, and some managerial implications are also given. First, the improvement of the pricing system of public sector should be accelerated. Second, the reasonable profit of the private sector must be guaranteed. While pursuing the maximization of social benefits, the public sector should make full use of the price sharing mechanism in the market and supervise the real income situation of the private sector. Third is increasing the public to participate in pricing. Additionally, it is a limitation that the deduction is assumed to conform to a uniform distribution in this study. Other probability distributions on deduction can be essentially further sought, so as to be more line with the actual situation of the projects.

The optimal payment in whole operation and maintenance period of the projects has become an important issue, which is a key to project success. This study constructs a multi-stage dynamic programming model to optimize payment in the whole period of projects. Additionally, this study adds its value through deeply developing the new theories of optimal payment to more suitable for the practical problems, so that to optimize the design of payment mechanism. Meanwhile, a valuable reference for public and private sectors is provided to ensure the sustainable development of the projects.

This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.

The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.

This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.

This study was steered to establish how buyer–supplier collaboration's commitment attributes serve as an antecedent for procurement performance in large manufacturing entities in Tanzania.

A parallel, concurrent, mixed method was used in the study. Quantitatively, 52 firms were surveyed from Temeke Municipality, Tanzania, using questionnaire that specified 1 procurement manager and 1 store manager from those firms, totaling a sample size of 104 respondents. Qualitatively, expressive opinions to supplement the numeric data were gathered from supply chain managers using the saturation principle. Explanatory design analyzed the existing cause–effect relationship, and the null hypotheses were tested using binary logistic regression at p values < 0.05 and ExpB > 1.

Fidelity and enthusiasm to suggest improvements to suppliers and the duration of the collaboration antecede the procurement performance of the manufacturing firms in Tanzania, while devotion to invest resources and initiatives on joint problem solving have no significant impact.

The causality between buyer–supplier collaboration and procurement performance has been revealed. Since there might be third party logistics in collaborations, future research should center on their moderating effect.

A framework has been developed for liberating procurement performance in the context of large manufacturing firms in Tanzania.

Based on Transaction Cost Economics and Resource Dependency Theories, the study revealed the root cause of procurement performance in the context of Tanzanian manufacturing firms, while also considering commitment to buyer–supplier collaboration as a prerequisit for the commendable target.

This study aims to analyze the effectiveness of the spin-off policy which includes clarity of objectives and criteria, implementation and monitoring and evaluation functions.

The method used is a qualitative method with a theme approach and the analytical hierarchy process (AHP). Data were obtained from the results of focus group discussions and AHP questionnaires with informants from Indonesian Sharia Insurance Association (AASI), the sharia life and general insurance industry, the Sharia Supervisory Board, the government and regulators.

The results of the research are the effectiveness of the clarity of goals and criteria has not been realized optimally, the effectiveness of increasing profitability has not been realized, and the effectiveness of the monitoring and evaluation functions by the government and regulators has been realized. The supporting factor that has the highest level of importance is the role of the government and regulator.

The limitation of this research is that it has not used a wider range of profitability test tools and projections. The theoretical implication of this research is as a reference for robust research in identifying spin-off success factors because this study uses a mixed method where qualitative methods are used in the study using data from theory and expert informants from three parties: regulatory parties, associations (AASI) and the insurance company (life insurance and general insurance). These results form the basis for compiling a questionnaire with a quantitative method so that the data is become relevant based on theory (design) and practical side.

Practical implication of the study is that the Islamic insurance industry has to prepare to achieve condition of Tabarru funds and the investment reaches 50% of the main insurance fund. AASI, as the sharia insurance industry organization, continues innovating the most suitable form of spin-off that can be achieved by the Sharia business unit and also continues to coordinate with regulators to discuss existing problems. The government and regulators also support the implementation of the spin-off by providing convenience in various aspects such as spin-off period relaxation and government incentive and relaxation to enhance sharia insurance industry.

The contribution of the results of this research for the government and regulatory agencies is as input in setting policies and regulations related to spin-offs, for the industry is expected to be more prepared in terms of resources, commitment and strategy.

This study aims to fill the gap in collaboration culture improvement by designing an information-sharing system as an enabler to support the forming dimensions of the collaboration process in previous studies. The authors propose the appropriate system to eliminate the collaboration culture gaps between the related functional units based on in-process interaction learning in a business process outsourcing company.

This study employed action research (AR) based on soft systems methodology (SSM) with a system thinking approach, in which the authors and process actors design the agreed information-sharing system by involving the process actors in identifying the initial problem situation as well as validating the conceptual model through discussions and designing the expected system.

This study discovers that SSM-based AR is a suited method for designing a system that supports the formation of collaboration culture among actors in the digital service creation process by learning user perceptions and expectations in order to obtain their commitment to empower the proposed system.

In addition to providing a system to minimize the lack of collaboration culture, this study contributes to the academic literature by offering a new way of planning and designing in a system development methodology using soft systems approaches to understand user perceptions, expressing user interaction in a conceptual model and validating it and defining agreed activities to obtain the best design according to user expectations.

This study demonstrates how becoming a high-reliability institution in health care is a priority, given the high-risk environment in which an error can result in harm. Literature conceptually supports the need for highly reliable health care facilities but does not show a comprehensive approach to operationalizing the concept into the daily workforce to support patients. The Veterans Health Administration closes the gap by documenting a case study that not only demonstrates specific actions and functions that create a high-reliability organization (HRO) for safety and improvement but also created a learning organization by spreading the knowledge to other facilities.

The authors instituted a methodology consisting of assessments, training and educational simulations to measure, establish and operationalize activities that identified and prevented harmful events. Visual communication boards were created to facilitate team huddles and discuss improvement ideas. Improvements were then measured and analyzed for purposeful outcomes and return on investment (ROI).

HRO can be operationalized successfully in health care systems. Measurable outcomes verified that psychological safety was achieved through the identification and participation of 3,184 process improvement projects over a five-year period, which yielded a US$2.8m ROI. Documented processes and activities were used for educational teachings, which were disseminated to other Veteran Affairs Medical Center’s through the Truman HRO Academy.

This case study is limited to one hospital in the Veterans Health Administration (VHA) network. As the VHA continues to deploy the methods outlined to other hospitals, the authors will perform incremental data collection and ongoing analysis for further validation of the HRO methods and operations. Hospitalists can adapt the methods in the case study for practical application in a health care setting outside of VHA. Although the model is rooted in health care, the methods may be adapted for use in other industries.

This case study overcomes the limitations within literature regarding operationalizing HRO by providing actual activities and demonstrations that can be implemented by other health care facilities.

This study aims to solve optimal pricing and power bidding strategy problem for integrated combined heat and power (CHP) system by using a modified heuristic optimization algorithm.

In electricity markets, generation companies compete according to their bidding parameters; therefore, optimal pricing and bidding strategy are solved. Recently, CHP units are significantly operated by generation companies to meet power and heat, simultaneously.

For validation, it is shown that profit is improved by 0.04%–48.02% for single and 0.02%–31.30% for double-sided auctions. As heat price curve is extracted, the simulation results show that when CHP system is integrated with other units results in profit increase and emission decrease by 3.04%–3.18% and 2.23%–4.13%, respectively. Also, CHP units significantly affect bidding parameters.

The novelties are pricing and bidding strategy of integrated CHP system is solved; local heat selling is considered in pricing and bidding strategy problem and heat price curve is extracted; the effects of CHP utilization on bidding parameters are investigated; a modified heuristic and deterministic optimization algorithm is presented.