Hierarchical Decomposition Approach Research Articles

Model and heuristics for the multi-manned assembly line worker integration and balancing problem

This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products.

Bayesian Strategy Networks Based Soft Actor-Critic Learning

A strategy refers to the rules that the agent chooses the available actions to achieve goals. Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system’s utility, decrease the overall cost, and increase mission success probability. This article proposes a novel hierarchical strategy decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method—soft actor-critic (SAC), and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. Our method achieves the state-of-the-art performance on the standard continuous control benchmarks in the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. Furthermore, we extend the topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions.

Accurate finite element modeling of multilayered flexible piezoelectric energy harvesting devices with strong coupling of structure, piezoelectricity, and circuit

Multilayered flexible piezoelectric energy harvesting devices (FPEDs) are a new future harvesting technology which are highly flexible and lightweight than familiar cantilever piezoelectric energy harvesters. This mechanical vibration driven FPED is a strongly coupled multiphysics phenomena that involve complex natural three-way interaction among the composite piezoelectric structure, the electric charge accumulated in the piezoelectric material, and a controlling electrical circuit attached to it. Efficient and accurate computational solution approaches are essential for analyzing these mechanical vibration-driven FPEDs to capture the main physical aspect of the coupled phenomena and to accurately predict the output voltage. While there are some numerical models for simple familiar cantilever type piezoelectric energy harvester reported in the literature, a fully three-dimensional strongly coupled model for complex material distributed, complex geometry, and multilayered FPED involving strong coupling of structure, piezoelectricity, and circuit phenomena has not yet been developed. A partitioned iterative algorithm is developed using a hierarchical decomposition approach wherein the coupled three fields are solved separately and coupled through loop union integration techniques that provide an efficient and accurate simulation of FPEDs. The simulation results matched the experiment results very well. This study provides a basis for the natural extension of partitioned iterative finite element coupled algorithm to simulate the future piezoelectric energy harvesting technologies involving a strong coupling of structure, piezoelectricity, and circuit phenomenon.

A Strategy-Oriented Bayesian Soft Actor-Critic Model

Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks – Hopper-v2, Walker2d-v2, and Humanoid-v2 – in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency.

A modelling framework for efficient reduced order simulations of parametrised lithium-ion battery cells

AbstractIn this contribution, we present a modelling and simulation framework for parametrised lithium-ion battery cells. We first derive a continuum model for a rather general intercalation battery cell on the basis of non-equilibrium thermodynamics. In order to efficiently evaluate the resulting parameterised non-linear system of partial differential equations, the reduced basis method is employed. The reduced basis method is a model order reduction technique on the basis of an incremental hierarchical approximate proper orthogonal decomposition approach and empirical operator interpolation. The modelling framework is particularly well suited to investigate and quantify degradation effects of battery cells. Several numerical experiments are given to demonstrate the scope and efficiency of the modelling framework.

Holistic multi-criteria performance measurement system for the maintenance function

Due to market globalization and technological development, companies are focusing more than ever on the performance of their maintenance function, which can only be achieved through a good performance measurement system. Although numerous works have been realized on this issue, there is still a lack of a flexible system that considers the particularities of each company and deals with all the performance measurement issues. For this reason, this paper introduces a maintenance performance measurement system, which allows the identification of all the relevant performance indicators using a hierarchical decomposition approach. Subsequently, the elementary performance indicators will be quantified using a measurement approach based on the fuzzy set theory, which deals with the unavailability and unreliability of data as well as time and resources limitation. Finally, an aggregation method based on the MACBETH method combined with the 2-additive Choquet integral was used in order to measure the global maintenance performance while considering the mutual interactions between the different criteria. Finally, to illustrate all these steps, the proposed system has been applied in a large Moroccan company operating in the chemical transformation sector.

The Single-Line Design Problem for Demand-Adaptive Transit Systems: A Modeling Framework and Decomposition Approach for the Stationary-Demand Case

When demand for transportation is low or sparse, traditional transit cannot provide efficient and good-quality service, because of its fixed structure. For this reason, mass transit is evolving toward some degree of flexibility. Although the extension of Dial-a-Ride systems to general public meets such need of adaptability, it presents several drawbacks mostly related to the their extreme flexibility. Consequently, new transportation alternatives, such as demand-adaptive systems (DASs), combining characteristics from both the traditional transit and Dial-a-Ride, have been introduced. For their twofold nature, DASs require careful planning. We focus on tactical aspects of the planning process by formalizing the single-line DAS design problem with stationary demand and proposing two alternative hierarchical decomposition approaches for its solution. The main motivation behind this work is to provide a general methodology suitable to be used as a tool to build the tactical DAS plan in real-life conditions. We provide an experimental study where the two proposed decomposition methods are compared and the general behavior of the systems is analyzed when altering some design parameters. Furthermore, we test the versatility of our methods on a variety of situation that may be encountered in real-life conditions.

A hierarchical decomposition approach for multi-level building design optimization

With the advancement in and integration of building energy simulation and optimization tools, building design optimization considering different design objectives and involving multiple disciplines has become technically feasible. However, this multifaceted approach unavoidably increases the dimension of the problem and comes with a prohibitively high computational cost. Hierarchical decomposition divides an optimization problem into several interconnected subproblems, and each subproblem has its objectives and constraints. Therefore, a complex problem is represented by a series of manageable subproblems. However, solving subproblems may lead to the local optimal solutions of the original problem. This research proposes a hierarchical decomposition approach that can achieve global optimal solutions. The proposed approach is applied to a multi-objective optimization problem to minimize the carbon emission and operating cost of building. This problem is decomposed into two subproblems to be solved sequentially. The first subproblem is to minimize the energy consumptions of the building. The second subproblem has the same objectives as the original one, but the search space is the result of the first subproblem. The benefit of decomposing this problem is that only the second problem needs to be solved again when additional energy price and carbon emission scenarios to be considered. A mid-rise residential building is selected as a case study to demonstrate the approach. The results show that the decomposition approach can reduce the number of simulations while keeping the full set of solutions of the original problem. The application of the proposed approach is based on a fixed utility rate, and only two objectives are considered for each level of optimization. These limitations could be overcome by adjusting the way to apply this approach.

Extended hierarchical decomposition approach for the synthesis of biorefinery processes

Synthesis of biorefinery processes have been of great interest in the research community and industrial practitioners for the past two decades. However, there is a lack of systematic insight-based approach for the synthesis of biorefinery processes, especially at the conceptual design level. This paper presents an extension on the systematic hierarchical decomposition approach (commonly known as the “Douglas’s hierarchical approach”) for the synthesis of biorefinery processes. Note that the Douglas’s hierarchical approach was originally developed for the synthesis of conventional chemical processes, and hence has various limitations when it is applied directly for the synthesis of biorefinery processes. These include unknown reaction stoichiometry, wide varieties of biomass feedstock, alternative conversion platforms, uncertain characteristic of biomass feedstock, etc. These challenges are now being addressed in the extensions proposed in this work for biorefinery process synthesis. Besides, additional tasks are included to assist reaction pathways screening at the early stage of design, while solid recovery system and material integration are being considered within the extended approach to maximise resource conservation and to minimise waste generation. The extended Douglas’s approach is demonstrated with a case study on the synthesis of palm-based biorefinery processes.

Two‐stage restoration strategies for power systems considering coordinated dispatch between plug‐in electric vehicles and wind power units

With increased penetration of wind power units and plug-in electric vehicles (PEVs), their control flexibility and quick response potentially provide an alternative way to fulfil the need for rapid restoration. A two-stage restoration strategy optimisation approach is presented with the coordination of PEVs and wind power units considered. The optimisation model is to maximise the restored generation capability and minimise the fluctuation of cranking power. In the first stage, the aim is to provide reliable cranking power remotely for black start generators through coordinated dispatch of PEVs and wind power units and quadratic programming (QP) models for dispatching electric vehicle aggregators (EVAs) subject to wind power fluctuations, and for dispatching numerous PEVs within each EVA are developed. To ensure close coordination between these two dispatching procedures, bi-level programming-based hierarchical decomposition approach is used to solve the QP models in an iterative way. In the second stage, an integer linear programming model is proposed to optimise the restoration schedules through an effective transformation of the original non-linear formulation, so as to reduce the computing time and effort significantly. Finally, a case study is presented to demonstrate the effectiveness and essential features of the developed models and methods.

Multi-stage hierarchical decomposition approach for stowage planning problem in inland container liner shipping

The relatively limited capacities of inland container liner shipping mean that, unlike in maritime container shipping, capacity utilisation is more important than scheduling. Capacity utilisation and stability must be considered in the stowage planning problem in inland container liner shipping. We adopt a multi-stage hierarchical decomposition approach to decompose the problem into multiple stages because a ship needs to visit multiple ports during its voyage. At each stage, the stowage planning problem of the current port is decomposed hierarchically into two sub-problems: the master bay planning problem (MBPP) and slot planning problem (SPP). The multi-port MBPP is first optimised to simultaneously generate the master bay plans for multiple ports over the full route. This approach incorporates two heuristic algorithms, a greedy randomised adaptive search procedure for the multi-port MBPP, and a heuristic evolutionary strategy algorithm for the SPP. Computational results for randomly generated data corresponding to real-size scenarios of inland container ships are presented validating the proposed algorithms.

Data-driven assignment of delivery patterns with handling effort considerations in retail

We consider a supply chain with one warehouse and multiple stores. At the warehouse, the orders for the stores are picked and in the store, shelves are stacked from the backroom. We include handling costs at the warehouse and stores as these are main drivers for logistics costs. We find delivery patterns and order-up-to levels, both of which shall remain fixed for a certain time. As especially in retail stochastic non-stationary demand structures are prevalent, we extend the classic joint replenishment problem under dynamic demand by a stochastic yet distribution-free optimization approach based on historical data samples. We formulate a mixed integer linear program using the plant-location formulation and develop several hierarchical decomposition approaches and a genetic algorithm. We consider a cyclic approach for orders, which allows an order at the end of the time horizon to fulfill the demand at the beginning of the time horizon. Using this approach, there is no need for initial inventories to be set as an input; they are are optimized within the model. Furthermore, a metacalibration approach is introduced, which allows an automated setting of input parameters for the genetic algorithm. To derive insights into the performance of the models, random instances are solved and then the most promising models are used for a case study with a European retailer. The results for the controlled test instances are analyzed by a meta-modeling approach that provides insights into performance drivers for the investigated model variants. The average logistics cost savings of our model over a deterministic approach with safety stocks amount to 3.02 % for the controlled test instances. In a similar comparison for the case study, average results over different parameter combinations show a 20.60 % logistics costs saving potential.

Hierarchical Decomposition for Betweenness Centrality Measure of Complex Networks

Betweenness centrality is an indicator of a node’s centrality in a network. It is equal to the number of shortest paths from all vertices to all others that pass through that node. Most of real-world large networks display a hierarchical community structure, and their betweenness computation possesses rather high complexity. Here we propose a new hierarchical decomposition approach to speed up the betweenness computation of complex networks. The advantage of this new method is its effective utilization of the local structural information from the hierarchical community. The presented method can significantly speed up the betweenness calculation. This improvement is much more evident in those networks with numerous homogeneous communities. Furthermore, the proposed method features a parallel structure, which is very suitable for parallel computation. Moreover, only a small amount of additional computation is required by our method, when small changes in the network structure are restricted to some local communities. The effectiveness of the proposed method is validated via the examples of two real-world power grids and one artificial network, which demonstrates that the performance of the proposed method is superior to that of the traditional method.

Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS

In this study, a new interval type-2 fuzzy multiple-attribute decision making model is developed by integrating Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Decision Making Trial and Evaluation Laboratory (DEMATEL). The proposed model utilizes hierarchical decomposition approach for reducing inherent complexity of the decision making problems. Additionally, interdependencies among problem attributes are taken into consideration by using interval type-2 fuzzy DEMATEL method. Finally, ranking orders of the alternatives are obtained by hierarchical interval type-2 fuzzy TOPSIS method. As there are several forms of interactions among criteria in real life settings, decision makers should be provided with the expert and intelligent systems which can overcome the preferential independence assumption. The proposed method is able to model causal dependencies by using interval type-2 trapezoidal fuzzy sets. The proposed method is implemented in a Strengths, Weaknesses, Opportunities, and Threats (SWOT)-based strategy selection problem.

Hierarchical Decomposition Approach for Process Synthesis of Integrated Biorefinery

Process synthesis is one of the most important activities in chemical process design. In the past decades, development in process synthesis has been dominantly targeted for petrochemical or petroleum-based industries. However, in recent years, the industries have been searching for alternative sustainable resources to replace fossil fuels. Biomass has been identified as one of the most promising alternatives. Biomass can be used to produce fuel, energy and chemicals in an integrated biorefinery. In order to synthesise and design an integrated biorefinery, many researchers have extended and modified current available process synthesis approaches, in which they have been mainly developed based on petrochemical industries. Although these reported approaches are useful in designing the integrated biorefinery; however, they are mainly mathematical optimisation approaches which require either high computational efforts or intensive modelling of the system. Based on the literature, there are limited works on developing hierarchical approach that can provide guidance for designer to synthesise an integrated biorefinery systemically. Due to the diversification in compositions and characteristics of biomass feedstock, as well as limited physical and chemical properties of biomass, additional considerations are required to modify the current available hierarchical approaches for integrated biorefinery synthesis. In this work, a well-established hierarchical decomposition approach is extended for synthesis of integrated biorefinery. The revised hierarchical approach now includes a new level, which is known as “characterisation and standardisation of biomass”. Besides, appropriate modifications are also made on the other levels (i.e. level 2- 6) to accommodate the requirements of synthesis of integrated biorefinery.

Reduction of greenhouse gas emissions from steam power plants through optimal integration with algae and cogeneration systems

This paper presents an optimization approach for mitigating CO2 emissions in the electric power generation through integrated algae and cogeneration systems. A framework is proposed for the integration of biofixation of CO2 through the cultivation of microalgae, conversion of microalgae to biodiesel, and a steam power plant with cogeneration that is thermally coupled with an industrial facility. A systematic multi-objective optimization approach is developed to integrate the considered units while simultaneously addressing technical, economic, and environmental objectives. The solution of the optimization problem is carried out via a hierarchical decomposition approach, a genetic algorithm, and the e-constraint method for solving the multi-objective optimization problem. A case study is considered to integrate an existing thermoelectric power station in Mexico with an algae-and-cogeneration system. The results show that important environmental, economic, and energy benefits can be achieved as a result of the proposed integration approach.

Novel MILP Decomposition Approach for Scheduling Product Distribution through a Pipeline Network

This work presents an approach for scheduling of operational activities in a large real-world pipeline network, where oil derivatives and ethanol are transported and distributed among refineries, terminals, depots, and final clients. The hierarchical decomposition approaches to solve the pipeline-scheduling problem presented by Boschetto et al. [Ind. Eng. Chem. Res. 2010, 49, 5661] and Magatao et al. [Ind. Eng. Chem. Res. 2012, 51, 4591], which are based on the integration of mixed integer linear programming (MILP) models and a set of heuristic modules, are merged and compounding blocks are also improved. Thus, a novel decomposition approach for scheduling product distribution through a pipeline network is proposed. In addition, this work presents a new MILP approach for the last hierarchical level: the timing block (timing model). This paper expands and improves the former MILP model, which was the timing block core. A series of operational constraints were considered within a continuous time representat...

Software Reliability Estimate with Duplicated Components Based on Connection Structure

Abstract Reliability testing of complex software at the system level is impossible due to the environmental constraint or the time limitation, so its reliability estimate is often obtained based on the reliability of subsystems or components. The connection structure was defined and the component-based software reliability was estimated based on it. For the present popular software with duplicated components, an approach to variance estimation of software reliability for complex structure systems was proposed, which has improved the hierarchical decomposition approach of variance estimation just for series-parallel systems. Experimental results indicated that the approach to variance estimation for reliability of software with duplicated components has advantages, such as the simple calculation process, small error result, and suitability for complex structure systems. Finally, the sensitivity analysis, used to identify critical components for resource allocation, could better improve the software reliability

Decomposition and extraction: a new framework for visual classification.

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

Hierarchical Decomposition Approach for Crude Oil Scheduling: A SINOPEC Case

This work addresses the large-scale crude oil scheduling problem of China’s SINOPEC Maoming refinery that imports various types of crude oil from two terminals via bidirectional long-distance pipelines. We use a hierarchical decomposition approach to construct a two-stage model of the refinery operations. In the upper-level model, storage and charging tanks are aggregated to determine the inflows and outflows between the two terminals and the refinery plant. The lower-level submodels solve the detailed loading and unloading operations at storage and charging tanks inside the terminals and the refinery plant. To further improve the computational efficiency, we develop a rule-based tank-selection strategy to obtain a feasible schedule. Although state-of-the-art commercial solvers cannot obtain feasible solutions of the relaxed monolithic mixed-integer linear programming model within a reasonable time, our decomposition heuristic can generate schedules that are more flexible than manually generated schedules. It also provides the refinery with annual estimated cost savings of $30 million.