Mathematical simulation analysis of optimal detection of shot-putters’ best path

In order to consider many uncertain factors in the process of shot-put, a fuzzy optimisation model of shot-put is proposed. With the help of fuzzy anthropometric data and strength data, the model calculates the fuzzy solution set of the athlete's best throwing mode and throwing distance with a known probability distribution, which reflects the actual process of shot throwing better than the non-fuzzy optimisation model. Then, using MATLAB6 software, the program design of the model solving and the user interface of optimisation software are developed, which realises fast calculation and good user interaction function. Finally, the actual measurement data of university shot-putters are used to verify the feasibility and effectiveness of the fuzzy optimisation model.

Optimising truck arrival management and number of service gates at container terminals

PurposeTruck appointment systems have been applied in critical container ports in the United States due to their potential to improve handling operations. This paper aims to develop a truck appointment system to optimise the total cost experiencing at the entrance of container terminals by managing truck arrivals and the number of service gates satisfying a given level of service.Design/methodology/approachThe approximation of Mt/G/nt queuing model is applied and integrated into a cost optimisation model to identify (1) the number of arrival trucks allowed at each time slot and (2) the number of service gates operating at each time slot that ensure the average waiting time is less than a designated time threshold. The optimisation model is solved by the Genetic Algorithm and tested with a case study. Its effectiveness is identified by comparing the model's outcomes with observed data and other recent studies.FindingsThe results indicate that the developed truck appointment system can provide more than threefold and twofold reductions of the total cost experiencing at the terminal entrance compared to the actual data and results from previous research, respectively.Originality/valueThe proposed approach provides applicably coordinated truck plans and operating service gates efficiently to decrease congestion, emission and expenses.

GNOME: A Dynamic Dispatch and Investment Optimisation Model of the European Natural Gas Network and Its Suppliers

As indigenous production declines, the European gas market is becoming increasingly dependent on imports. This poses energy security questions for a number of countries, particularly in the north-east of Europe. A suite of mathematical models of the European natural gas network has been borne from these concerns and has traditionally been used to assess supply disruption scenarios. The literature reveals that most existing European gas network models are insufficiently specified to analyse changes in supply and demand dynamics, appraise proposed infrastructure investments, and assess the impacts of supply disruption scenarios over a range of time horizons. Furthermore, those that are suited to these applications are typically proprietary and therefore publicly unavailable. This offers an opportunity to present a new model. The Gas Network Optimisation Model for Europe (GNOME) is a dynamic, highly granular mixed-integer linear optimisation model of the European natural gas network and its exogenous suppliers. GNOME represents demand and supply for all EU-27 Member States except Cyprus, Luxembourg, and Malta. The UK, Norway, Switzerland, Belarus, Ukraine, and Turkey are also included. Russia, the Southern Corridor suppliers, Qatar, North Africa, Nigeria, and the Americas are modelled as supply-only regions. GNOME satisfies gas demand in each country by generating a cost-minimal mix of indigenous gas production, pipeline flows, LNG imports, and storage use. If demand cannot be met using existing infrastructure, GNOME will generate a cost-optimal investment strategy of pipeline, LNG regasification, and gas storage capacity additions. The model solves on a monthly basis, from 2025 to 2040, in 5-year steps. The capabilities of GNOME are demonstrated by tasking it to analyse the impacts of a failure to complete the upcoming Nord Stream 2 pipeline between Russia and Germany. The complete formulation of GNOME including input files, equations, and source code is provided.

ALIS: A novel metric in lineage-independent evaluation of scholars

Evaluative bibliometrics often attempts to explore various methods to measure individual scholarly influence. Scholarly independence (SI) is a unique indicator that can be used to understand and assess the research performances of individual scholars. The SI is a rare quality that most funding agencies and universities seek during funding decisions or hiring processes. We propose author lineage independent score (ALIS), a unique model to measure SI of a scholar by using his or her academic genealogy tree as the underlying graph structure. The analysis is performed on real data of 100 authors, collected from the Web of Science (WoS) and the Mathematics Genealogy Project. The analysis is further validated on a larger scale, on a simulated sample of 10,000 authors. The simulation exercise is the proof-of-concept for scalability of the metric and the proposed optimisation model. ALIS exploits genealogical relationships between scholars and their mentors and collaborating communities and constructs an influence scoring model based on the Genealogy tree structure of the respective scholars. The implications from the theoretical model are found to be profound in tracing known and recursive citation patterns among peers. The genealogy tree is used to investigate the advisor–advisee relationship and lays the foundation for defining metrics used to calculate the various indicators such as non-genealogy citations (NGCs), non-community citations (NCCs) and other citation quotient (OCQ). As these indicators/parameters are novel and thus not readily accessible, algorithms are written to compute these indicator values for the scholars under study.

Optimising a Euclidean Colour Space Transform for Colour Order and Perceptual Uniformity

In this paper, we attempt to optimise a colour space transform for colour order and perceptual uniformity to verify if a trade-off could be achieved between the two. The IPT colour space is used as basis for the optimisation. An optimisation model consisting of a modified XYZ-to-LMS matrix, a nonlinearity factor, and two geometric transformation matrices is proposed. Two objective functions are constructed based on the optimisation model, where one would improve perceptual uniformity primarily and the other would improve colour order instead. Finally, the two objective functions are combined, in an attempt to optimise both simultaneously and see if a trade-off between the seemingly incompatible features can be achieved. The performance of the optimised IPT transform is then compared to the original IPT transform, in terms of relative improvements in perceptual uniformity and colour order. Finally, the results show that there is indeed an inverse relationship between the two objectives. However, by adjusting the bias of the optimisation, a balance could be achieved between the two, where both colour order and perceptual uniformity was improved with respect to the original IPT transform.

Modelling resilient fashion retail supply chain strategies to mitigate the COVID-19 impact

PurposeThis paper aims to develop supply chain strategies for the fashion retail supply chain (FRSC), likely to be disrupted by the current pandemic (COVID-19) under physical and online retail stores. The resilient retail supply chain design is proposed under budget allocation and merchandise capacity constraints.Design/methodology/approachThis paper utilises the theory of constraint (ToC) and goal programming (GP) to address the COVID-19 impact on FRSC. The budgetary and capacity constraints are formulated with a constraint optimisation model and tested with six different priorities to deal with the physical and online stores. Next, all priorities are developed under different FRSC business scenarios. The ToC-GP-based optimisation model is validated with one of the Indian fashion retail supply chains.FindingsThe proposed optimisation model presents the optimal retailing strategies for selling fashion goods over physical and online platforms. The multiple scenarios are presented for developing trade-offs among different strategies to maximise the retailer's merchandise performance. This paper also highlighted the strategic movement from high merchandise density stores to low merchandise density stores. This implies a reduction of sales targets and aspiration levels of both online and physical fashion stores.Research limitations/implicationsThe proposed model is validated with one of the fashion retailers in India. Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.Practical implicationsThis research helps fashion retail supply chain managers deal with consumer demand uncertainty over physical and online stores in pandemic times. Limitation: Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.Originality/valueThis is the first study that considered the impact of COVID-19 on the retail fashion supply chain. The effect of physical and online platforms is mainly discussed from consumer marketing perspectives, but an inventory and resilience perspective is missing in earlier studies. The role of merchandise planning is highlighted in this study.

Optimising of vacuum distillation units using surrogate models

Crude oil blending is an important step for the operation of crude distillation systems in the refinery to improve the yield and profitability of the products. The product’s yield and quality are strongly dependent on the properties of the crude oil. However, the products of crude distillation units, especially the vacuum distillation unit (VDU) need to satisfy the yield and quality requirements of the downstream process units in the refinery. Otherwise, the performance of downstream processes will be affected, and loss of profitability in the refinery. Hence, it is important to optimise the performance of the VDU to ensure the optimum operation of VDU. This work covers the process simulation of VDU, surrogate modelling and mathematical optimisation model. The objective of the developed optimisation model is to determine an optimal for maximum high vacuum gas oil (HVGO) yield and minimum total annualised cost (TAC) respectively. To do this, crude oil blending ratio, column temperature, column pressure, stripping steam flowrate, pump-around flowrate in the VDU are optimised. Based on the optimised result, the heavy-light crude blend achieves higher HVGO yield and lower TAC as compared to the heavy-medium crude blend and heavy-medium-light crude blend. The optimised results can provide insight into the optimal process conditions of VDU for the refiners. With this insight, effective operating strategies can be developed to overcome the limitations present in real VDU operations.