Solution Framework Research Articles

Integrated optimization of timetable, bus formation, and vehicle scheduling in autonomous modular public transport systems

This paper presents a joint optimization of the timetable, bus formation, and vehicle scheduling in a flexible public transport (PT) system that utilizes autonomous modular vehicles (AMVs). In this system, AMVs have the capability to detach or join with each other at intermediate stops along the route to dynamically adjust the bus formation (capacity). To increase vehicle utilization, a flexible scheduling strategy is proposed that allows AMVs to detach from one modular bus and join another modular bus in either direction of a bidirectional line. In particular, the penalty cost for each detachment or joining operation, as well as the limited number of available AMVs is explicitly considered. We formulate a unified model for the integrated optimization of the modular bus service (timetable and bus formation) and vehicle scheduling by introducing two types of decision variables. The objective is to minimize overall system costs, including passenger waiting time cost, operational costs, and detachment/joining penalty costs. The two types of decision variables are coupled by a vehicle resource consistency constraint, ensuring the conformity of the modular bus service and vehicle scheduling decisions. To tackle the complexity of our model, the Alternating Direction Method of Multipliers (ADMM) is employed to decompose it into two subproblems, which can be efficiently solved using a customized forward dynamic programming algorithm and a commercial solver. The model is validated using illustrative examples and a real-world instance from the Beijing Public Transport system, and it is compared with two benchmark models. Our results demonstrate the efficiency of the ADMM-based solution framework for solving the integrated optimization model. Furthermore, our findings indicate that the use of AMVs in PT systems can lead to reduced overall system costs and increased vehicle utilization.

On Using Metaheuristics for the Allocation of Electric Vehicles to Charging Stations

In this article, we present a comprehensive study regarding the problem of allocating a fleet of electric vehicles to charging stations according to charging time and battery constraints. Each charging station’s capacity as well as the necessary charging time is known in advance while each vehicle’s arrival time is provided by a GPS device. We provide an integer programming model solved with an exact method to effectively handle this combinatorial problem along with a set of metaheuristic algorithms. To evaluate the performance of this solution framework, computational experiments are conducted on large-scale randomly generated instances simulating a real-world scenario.

Dynamic community partitioning for e-commerce last mile delivery with time window constraints

Community logistics (CL) is a recently proposed delivery strategy designed to deal with e-commerce last-mile delivery scheduling by dynamically assigning vehicles to designated delivery regions partitioned into “communities”. Since optimizing vehicle routes is not mandatory in the CL spectrum, the delivery solution format and optimization process can be greatly simplified. Nevertheless, abandoning vehicle routes means vehicle arrival time at each customer specified delivery destination is unknown, resulting in the inability of handling time window constraints of e-commerce orders. To expand the application scope of CL, this study introduces community time window, an aggregation of identical or adjacent order time windows. Once the community time window for a delivery community is satisfied, all orders in this community can be received within designated time windows without determining vehicle routes. With this new concept, the application range of the CL is extended to e-commerce last mile delivery contexts where order time window constraints are considered. A dynamic community partitioning problem with the time window is presented based on the Markov decision process (MDP). An efficient heuristic solution framework based on policy function approximation is proposed to solve the MDP model. Numerical results show that the CL is very effective in dealing with the time window constraints of e-commerce orders.

Train schedule optimization for commuter-metro networks

The interconnection and synchronization among different transport modes have been more and more attractive as the modern transportation system is moving towards Mobility-as-a-Service. In this study, we address the train scheduling problem for “commuter rail-metro” systems, where the trains from commuter rail lines can go directly into metro systems to provide seamless services for passengers. To optimize the schedule of trains for both commuter rail lines and metro lines, we propose a job shop scheduling model where precedence constraints from commuter-metro networks are taken into account and develop a mixed-integer programming (MIP) model with quadratic constraints. Our model considers the orders of different types of trains and the safety constraints, due to different types of signalling equipment in commuter and metro systems. Since these constraints involve a set of IF-THEN rules, we prove that these constraints can be equivalently reformulated as linear inequalities, without adding new variables. To solve the proposed model efficiently, we design an iterative solution framework, which generates a feasible solution using dynamic programming, next solves a MIP model, then calculates the train speed profiles, and if train speed profiles violate the safety constraints, re-optimizes the MIP model with modified alternative constraints. To verify the effectiveness of the proposed approaches, numerical experiments are performed on small and real-world instances based on the Beijing metro Line 1 and the Batong Line operational data.

Minimising conflicts among run‐time non‐functional requirements within DevOps

AbstractSignificant contributions in the existing literature highlight the potential of softgoal interdependency graphs towards analyzing conflicting non‐functional requirements (NFRs). However, such analysis is often at a very abstract level and does not quite consider the run‐time performance statistics of NFR operationalizations. On the contrary, some initial empirical evaluations demonstrate the importance of the run‐time statistics. In this paper, a framework is proposed that uses these statistics and combines the same with NFR priorities for computing the impact of NFR conflicts. The proposed framework is capable of identifying the best possible set of NFR operationalizations that minimizes the impact of conflicting NFRs. A detailed space analysis of the solution framework helps proving the efficiency of the proposed pruning mechanism in terms of better space management. Furthermore, a Dynamic Bayesian Network (DBN) ‐ based system behavioral model that works on top of the proposed framework, is defined and analyzed. An appropriate tool prototype for the framework is implemented as part of this research.

A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques

In supply chain management, selection of suitable suppliers and allocating corresponding orders are two essential strategic decisions. Making these decisions is a complex process due to some uncertain parameters, such as future demand. This study proposes a three-stage solution framework to solve problems with supplier selection & order allocation planning. In Stage 1, a new modified relational deep learning forecasting technique is developed to forecast the demands of products. In this part, the efficiency of this modified technique is compared with two well-known forecasting techniques, namely Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Light-Gradient Boosted Machine (LGBM). In Stage 2, a new hybrid principal component analysis method is used to generate suppliers’ weights. The results from Stages 1 and 2 are used in the multiple objectives optimization model which is developed in Stage 3. The hybrid method is used to derive a set of efficient solutions. The developed framework is discussed using a real dataset from the Canadian meat industry. The results of forecasting models show that the developed deep learning network can reduce the forecasting error by 55.42% when compared to the SARIMA method, and 13.1% when compared to the LGBM method. It is also observed that the consideration of inter-product correlation functions can change the selected suppliers and the corresponding orders.

Finite‐time stabilization of Markovian switched stochastic high‐order nonlinear systems with inverse dynamics

AbstractIn this paper, we investigate the finite‐time stabilization of a class of nonlocal Lipschitzian stochastic nonlinear systems with Markovian switching and stochastic inverse dynamics. We first present the finite‐time stability theory for stochastic nonlinear systems with Markovian switching under the framework of weak solutions. Then, a finite‐time controller with state feedback is constructed for a class of stochastic high‐order nonlinear systems with Markovian switching and stochastic inverse dynamics. The trivial weak solution of the closed‐loop system is proved to be globally finite‐time stable in probability. Finally, the effectiveness of the proposed design method is verified by a simulation example.

A numerical study of the nanofluid mixtures inside a Buoyancy-driven cavity in the presence of a variable magnetic field

While magnetic flow applications have become a topic of great interest for decades, the coupling between fluid flow and electromagnetic forces is not straightforward. Therefore, the necessity of a robust guideline to simulate these types of problems has become apparent. Moreover, the heat transfer capabilities and the interaction between electromagnetic forces such as Lorentz and Kelvin require further investigation.In this regard, two objectives are pursued to address the noted issues. First, a robust and flexible solution framework using User-Defined-Functions (UDF)s is presented so that magnetic flow applications can be investigated without software limitations such as discretization scheme, solution setup, and simultaneous use of other advanced modules. Next, the impact of electromagnetic forces on streamlines and isotherms has been studied, and the forces’ area of influence is carefully investigated.Based on the results, the introduced framework has successfully predicted consistent results for three previous studies. Next, by considering a wide range of Rayleigh (Ra), Hartmann (Ha), and Magnetic (Mn) numbers, valuable revelations regarding the force interactions, thermofluidic properties, and force area of influence were revealed. Finally, the situations in which the Kelvin and Lorentz forces are influential are identified based on dimensional analysis.

Omega Ideals in Omega Rings and Systems of Linear Equations over Omega Fields

Omega rings (Ω-rings) (and other related structures) are lattice-valued structures (with Ω being the codomain lattice) defined on crisp algebras of the same type, with lattice-valued equality replacing the classical one. In this paper, Ω-ideals are introduced, and natural connections with Ω-congruences and homomorphisms are established. As an application, a framework of approximate solutions of systems of linear equations over Ω-fields is developed.

Coordinated charging station search in stochastic environments: A multiagent approach

Range and charge anxiety remain essential barriers to a faster electric vehicle (EV) market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an EV uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real‐time availability data but struggle with data inaccuracy, for example, due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize a driver's detour until reaching an available charging station. So far, both practical and theoretical approaches ignore driver coordination enabled by charging requests centralization or sharing of data, for example, sharing observations of charging stations' availability or visit intentions between drivers. Against this background, we study coordinated stochastic search algorithms, which help to reduce station visit conflicts and improve the drivers' charging experience. We model a multiagent stochastic charging station search problem as a finite‐horizon Markov decision process and introduce an online solution framework applicable to static and dynamic policies. In contrast to static policies, dynamic policies account for information updates during policy planning and execution. We present a hierarchical implementation of a single‐agent heuristic for decentralized decision making and a rollout algorithm for centralized decision making. Extensive numerical studies show that compared to an uncoordinated setting, a decentralized setting with visit intentions sharing decreases the system cost by 26%, which is nearly as good as the 28% cost decrease achieved in a centralized setting. Even in long planning horizons, our algorithm reduces the system cost by 25% while increasing each driver's search reliability.

Insight into the construction of multi-cavity micro-mesoporous SSZ-13: Dissolution, Al backfilling and framework reconstruction

The alkali leaching post-treatment method is a simple, economical and general route for introducing mesopores into SSZ-13, while the existing studies have rarely addressed the Al behavior between solution and zeolites framework during the alkali treatment. In this paper, a series of multi-cavity micro-mesoporous SSZ-13 were successfully prepared by using the alkali leaching post-treatment method at various treatment time. Extensive characterization illustrated that Al backfilling and zeolite framework reconstruction also existed during the NaOH treatment, besides the destruction of the zeolite framework due to the dissolution of Si and Al at the beginning of the reaction, and Al was preferentially backfilled into eight-membered ring (8 MR) to repair the zeolite framework and then filled into six-membered ring (6 MR) to reconstruct the zeolite framework. Concurrently, the amount of Brønsted acid (B acid) showed a decreasing-increasing-stabilizing trend during the progress. The catalytic activity and hydrothermal stability tests confirmed that multi-cavity micro-mesoporous SSZ-13 showed excellent catalytic activity for NH3 selective catalytic reduction (NH3-SCR) and good hydrothermal stability. This study provided basic guidance for the optimal design of the post-treatment alkali leaching route, which was highly significant for the development of hierarchically porous zeolites.

A multi-objective solution framework for the assembly inventory routing problem considering supply risk and carbon offset policies

This study aims to provide novel insights into the multi-objective optimisation of an inbound assembly inventory routing problem (IRP) by considering customer demand uncertainty, stochastic supply failure risks, and a carbon offset policy. An inbound IRP network generally comprises several geographically dispersed suppliers, vehicle depots, and assembly plants. In this study, a new mixed-integer nonlinear optimisation model is formulated considering two objective functions: (i) minimising the total supply network cost and (ii) maximising a low-risk supplier priority function with practical constraints. A hybrid non-dominated sorting genetic algorithm-II was developed to obtain near-optimal Pareto solutions for various small, medium, and large IRP instances. The Pareto solutions were compared with the efficient solution obtained using the ε-constraint method. Finally, the best Pareto solution was obtained using the multi-criteria decision-making technique. The results indicate that implementing supply risk-mitigation strategies leads to higher overall supply network costs and carbon emissions. However, a sensitivity analysis based on factorial simulation experiments indicates that a supply risk-mitigation strategy is advantageous if component prices are low and supply capacity, fuel price, and demand variations are high. In addition, the lower the demand variation, the lower the expected overall network cost and carbon emissions.

COVID-19 and global supply chain risks mitigation: systematic review using a scientometric technique

PurposeThis paper aims to investigate how manufacturing firms behave to mitigate business risk during and post-COVID-19 coronavirus disease (COVID-19) on the global supply chain.Design/methodology/approachA systematic literature review for data mining was used to address the research objective. Multiple scientometric techniques (e.g. bibliometric, machine learning and social network analysis) were used to analyse the Lens.org, Web of Science and Scopus databases’ global supply chain risk mitigation data.FindingsThe findings show that the firms’ manufacturing supply chains used digitalisation technologies such as Blockchain, artificial intelligence (AI), 3D printing and machine learning to mitigate COVID-19. On the other hand, food security, government incentives and policies, health-care systems, energy and the circular economy require more research in the global supply chain.Practical implicationsGlobal supply chain managers were advised to use digitalisation technology to mitigate current and upcoming disruptions. The manufacturing supply chain has high uncertainty and unpredictable global pandemics. Manufacturing firms should consider adopting Blockchain technology, AI and machine learning to mitigate the epidemic risk and disruption.Originality/valueThis study found the publication trend of how manufacturing firms behave to mitigate the global supply chain disruptions during the global pandemic and business uncertainty. The findings have contributed to the supply chain risk mitigation literature and the solution framework.

Evaluation of Open-Source Tools for Differential Privacy

Differential privacy (DP) defines privacy protection by promising quantified indistinguishability between individuals who consent to share their privacy-sensitive information and those who do not. DP aims to deliver this promise by including well-crafted elements of random noise in the published data, and thus there is an inherent tradeoff between the degree of privacy protection and the ability to utilize the protected data. Currently, several open-source tools have been proposed for DP provision. To the best of our knowledge, there is no comprehensive study for comparing these open-source tools with respect to their ability to balance DP’s inherent tradeoff as well as the use of system resources. This work proposes an open-source evaluation framework for privacy protection solutions and offers evaluation for OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib. In addition to studying their ability to balance the above tradeoff, we consider discrete and continuous attributes by quantifying their performance under different data sizes. Our results reveal several patterns that developers should have in mind when selecting tools under different application needs and criteria. This evaluation survey can be the basis for an improved selection of open-source DP tools and quicker adaptation of DP.

VAST: a practical validation framework for e-assessment solutions

The influx of technology in education has made it increasingly difficult to assess the validity of educational assessments. The field of information systems often ignores the social dimension during validation, whereas educational research neglects the technical dimensions of designed instruments. The inseparability of social and technical elements forms the bedrock of socio-technical systems. Therefore, the current lack of validation approaches that address both dimensions is a significant gap. We address this gap by introducing VAST: a validation framework for e-assessment solutions. Examples of such solutions are technology-enhanced learning systems and e-health applications. Using multi-grounded action research as our methodology, we investigate how we can synthesise existing knowledge from information systems and educational measurement to construct our validation framework. We develop an extensive user guideline complementing our framework and find through expert interviews that VAST facilitates a comprehensive, practical approach to validating e-assessment solutions.

NiCu bimetallic metal–organic framework to improve the desalination performance of capacitive deionization

Due to its energy-saving and environment-friendly process without secondary pollutant, capacitive deionization (CDI), which separates the charged ion species from solution by applying external electric energy, provides an alternative desalination technology. Even though carbon-based electrodes have mostly been used so far, they suffer from limited salt removal performance because of insufficient ion uptake capacity and stringent regeneration conditions, which hinder the system from obtaining higher performance and scale-up. The development of a transition metal-based electrode has attracted much attention for emerging electrochemical applications, such as capacitive deionization, due to its unique electrochemical properties. However, synthesizing metal–organic frameworks (MOFs) in aqueous solution with high crystalline structure has been a challenging task. In this study, we used a simple hydrothermal technique to create NiCu–FA in aqueous solution to synthesize crystalline MOF structure. The newly developed NiCu–FA demonstrated high salt adsorption capacity, high salt adsorption rate, and outstanding CDI cycle stability, emphasizing the importance of high surface area with salt-activated transition metal. The high adsorption capacity of 10.55 mg/g could be achieved, which is over 76 % increase, compared to that of the pristine AC electrode 5.98 mg/g. Such enhanced desalination performance results from the synergistic contribution of electric double layer (EDL) capacitance from AC and pseudocapacitive behavior from NiCu–FA, which was confirmed by electrochemical measurements. We believe that our approach can offer a promising candidate for practical CDI application with high desalination performance and high salinity feed source.

An Improved Finite-Time and Fixed-Time Stable Synchronization of Coupled Discontinuous Neural Networks.

This article focuses on the finite-time and fixed-time synchronization of a class of coupled discontinuous neural networks, which can be viewed as a combination of the Hindmarsh-Rose model and the Kuramoto model. To this end, under the framework of Filippov solution, a new finite-time and fixed-time stable theorem is established for nonlinear systems whose right-hand sides may be discontinuous. Moreover, the high-precise settling time is given. Furthermore, by designing a discontinuous control law and using the theory of differential inclusions, some new sufficient conditions are derived to guarantee the synchronization of the addressed coupled networks achieved within a finite-time or fixed-time. These interesting results can be seemed as the supplement and expansion of the previous references. Finally, the derived theoretical results are supported by examples with numerical simulations.

عصر حاضر میں بڑھتی ہوئی غربت، اس کے اسباب اور اس کا حل اسلامی تعلیمات کی روشنی میں

This article discusses poverty and its solution in Islamic teachings. Today, poverty is not only a problem to any one region or country, but it is an important and fundamental problem of the whole world. According to the report of the journal Global Issues, about more than three billion people lives on less than two and a half US dollars per day. The combined GDP of forty-one indebted countries with 560 million people in the world is less than the combined wealth of the seven richest people in the world. Out of two billion children in the world, one billion children are living in poverty. Children have no place to live. 27 crores people do not have access to medical facilities. Currently, there is enough food to meet the needs of nearly seven billion people on the planet, yet one in seven people go to bed hungry at night. Poverty is an undesirable thing in Sharia. This paper attempts to study the framework of Islamic solution to this fundamental problem.

Accelerated nonlinear domain decomposition solver for multi-phase flow and transport in porous media

Development of robust and efficient nonlinear solution strategies for multi-phase flow and transport within natural porous media is challenging. Fully Implicit Method (FIM) is widely used in reservoir simulation for solving nonlinear algebraic systems. Previous studies showed that nonlinearities of flow and transport problems could exhibit large degrees of locality across timesteps and nonlinear iterations. Nonlinear domain decomposition (NDD) is an attractive class of methods for achieving localization and resolving spatially unbalanced nonlinearities. However, NDD is not robust as iterative solvers, because the outer iterations are essentially fixed-point type.We design a solution framework to accelerate the outer-loop convergence of NDD. We first apply a nonlinear acceleration (NA) technique to the fixed-point iterations, for correcting the boundary conditions of subdomains. Because flow problems are parabolic and exhibit global behaviors, long-range information is essential to capture main features of the flow field. We incorporate a preconditioning step based on CPR-AMG before each timestep, to provide better initial solutions. Additionally, smoother iterations can be taken to improve the boundary conditions during the NDD process.We evaluate the NDD methods using heterogeneous multi-phase models. The nonlinearities associated with the flow, transport and the coupling are locally resolved through the NDD iterations. Results demonstrate that the basic NDD solver is afflicted with severe nonlinear difficulties. By comparison, the accelerated NDD solver greatly improves the outer convergence performance. Moreover, the new solver can effectively exploit locality on the Newton level, for both the flow and transport problems.

Assessment of Vietnam Tourism Recovery Strategies after COVID-19 Using Multi-Criteria Decision-Making Approach

Tourism is the economic sector most heavily influenced by COVID-19, and it has suffered unprecedented losses. The competitiveness and resilience of the tourism industry have recently become a topic of great concern for global stakeholders. A series of ambitious recovery strategies have been announced by countries to rebuild the tourism industry, that aim to make “smokeless industry” more resilient and sustainable. The objective of this study is to evaluate and rank the effectiveness of nine recovery strategies in the post-COVID-19 period for Vietnam’s tourism industry. A combined model of the Best–Worst Method (BWM) and the Group Best Worst Method (GBWM), an efficient tool using the multi-criteria decision-making (MCDM) approach, is used to rank the tourism solutions. The assessment process is carried out by six stakeholder groups considered decision makers, including tourism operators, enterprises, scholars, employees, residents, and tourists. In the context of Vietnam, the most influential tourism recovery strategy is using innovative tourism business models (ST2), which is a solid step forward in utilizing potential resources, meeting current tourism needs, and adapting to natural changes. The model results reflect that the tourism model’s restructuring is necessary to provide new types of experiences and entertainment suitable for the new tourism context. The findings illustrate that the priority of strategies depends on the perception of decision-makers, levels of involvement in the tourism industry, and local conditions. The study has contributed a theoretical framework for tourism recovery solutions and decision support in the post-pandemic stage. The model can be applied to other countries worldwide in improving tourism performance or assisting in decision-making for similar issues.