Hierarchical Decision-making Approach Research Articles

Hierarchical decision-making approach for the task planning of the BWBUG cluster with three-dimensional time-varying ocean currents

This study concerns the task planning problem for a Blended-Wing-Body Underwater Glider (BWBUG) cluster with three-dimensional, time-varying ocean currents. The objective is to achieve task allocation and three-dimensional glide path planning for the BWBUG cluster in a manner that ensures safety and cost-effectiveness. First, the task planning problem for the BWBUG cluster is analyzed, detailing the task requirements and operational space. Next, a hierarchical decision-making approach is proposed, which comprises an upstream planner and a downstream planner. The upstream planner aims to determine a feasible task space for the BWBUG cluster while satisfying task constraints. The downstream planner fully considers the impact of ocean currents on BWBUG movement, as well as the safety and kinematic constraints of BWBUGs. It optimizes the three-dimensional movement trajectories of each BWBUG with the objective of minimizing energy consumption. Inspired by the nest-seeking behavior of bees, a novel heuristic algorithm called the Bee Nesting Optimization Algorithm (BNOA) is proposed to solve both upstream and downstream planning problems. The results of the simulations demonstrate the efficacy of the hierarchical decision-making approach developed in this study. Additionally, the proposed BNOA is highly competitive, offering reasonable task allocations and optimal three-dimensional glide paths for the BWBUG cluster.

Hierarchical Model for Optimizing Natural Fiber Selection Process for Eco-design of Buildings

ABSTRACT The composites based on fiber are commonly used in the high performance building materials. The composites mostly use petro-chemically derived fibers like polyester and e-glass due to their beneficial material properties like high stiffness and strength. All the same, these fibers also have important shortcomings related to energy consumption, recyclability, initial processing expense, producing health hazards, and sustainability. Rising environmental awareness along sustainable building technologies is driving the research, development and utilization of “green” building materials, especially the development of bio-based materials depend on renewable, natural materials. In this paper, the natural fiber evaluation approach is applied, which covers a lot of criteria. Comparative assessment of diverse natural fiber types is conducted by using a hierarchical decision-making approach. Multi-criteria decision-making methodologies are appropriate for making decision and assessing for the most proper options in order to select the excellent criteria. This includes a group of investigation methods, which contemplates problems in decision-making. The best performing fiber choice is made by comparatively evaluating the materials related to green building. The proposed fiber can be used and applied by green building materials manufacturing companies in various countries or locations as a reference when selecting the fiber with best performance.

Coordinated Charging Control of Electric Vehicles While Improving Power Quality in Power Grids Using a Hierarchical Decision-Making Approach

As electric vehicles (EVs) are connected to the distribution power system, EV owners desire to charge their EVs economically, reliably, confidentially and without requiring major modification to the charging systems in their EVs. At the same time, the increasing EV connections to the distribution power system can increase the electric load demand and decrease the power quality (PQ) of the power system due to the charging current of the EV chargers. It is, therefore, necessary to devise novel strategies for the coordination of the connection of EVs at the distribution level. Although different control methods have been proposed previously, most of these solutions require costly advanced infrastructures and computational resources. In this paper, a charging control system (CCS) using a hierarchical decision-making (HDM) approach is devised that can be easily applied for the economical and reliable charging of commercial EVs with minor modifications while improving the PQ in the power grids. The HDM approach includes a primitive energy management strategy and an adaptive droop control for the real-time operation. A thorough investigation of the effectiveness of the proposed CSS to meet the major needs of EV owners while improving the PQ of the distribution network has been carried out. The results from the case studies show that the CCS can satisfy the preferences of the EV owners, while improving the PQ of distribution grids. Specifically, the CCS can meet the economical and reliable EV charging for the EV owners, and at the same time maintain the voltage deviation within the acceptable PQ limits.

A multi-stage multi-criteria hierarchical decision-making approach for sustainable supplier selection

Sustainable supplier selection is known as a crucial objective in supply chains due to its impact on profitability, adorability, flexibility, and agility of the system. This study proposes a new multi-stage hierarchical fuzzy index-based approach with which decision-makers are empowered to select the most sustainable supplier based on sustainability triple bottom line criteria. Besides, a new fuzzy extension for the best-worst method is proposed considering trapezoidal fuzzy membership functions that can cover uncertainty under imprecise environments. This study makes a contribution to the literature of sustainable supply chains in the sense that it facilitates the computational complexity of previous decision-making approaches by collecting the most relevant criteria and a straightforward fuzzy process. Besides, the graded mean integration representation method has been adopted for prioritizing the supplier based on their performance of sustainable development, which enhances the accuracy of selection compared with previous ranking methods. The proposed study can be utilized as a benchmark for sustainability evaluations between suppliers. A real-world case study is resolved to illustrate the superiority and broad application of the proposed model.

Bi-level genetic algorithms for a two-stage assembly flow-shop scheduling problem with batch delivery system

Abstract In this paper, an integrated production and distribution scheduling on a two-stage assembly flow-shop setting with a batch delivery system is addressed. The objective is to schedule the jobs in the two-stage assembly flow-shop and groups the completed products into a suitable number of batches for delivery with the minimum number of weighted tardy jobs and sum of delivery costs. After extending a mixed-integer linear programming (MILP) model, we proposed a bi-level genetic algorithm to solve the addressed problem that the first level of its chromosome is supposed to determine the sequence of the processing jobs, and the second level of it is responsible for allocating jobs to batches independently. To offer the more efficient algorithm, we change the structure of the proposed GA. Therefore, by applying the hierarchical decision-making approach, we present a bi-level improved genetic algorithm (IGA) in which according to the determined sequence in the first level, batches are determined. The proposed algorithms are evaluated based on computational experiments. The experiments reveal that IGA, which has the decreased decision-making space for the second level, outperforms GA. Moreover, to validate the proposed model and the efficiency of the proposed algorithms, a real-life example is presented and solved.

Computing sustainable manufacturing index with fuzzy analytic hierarchy process

The growing industrial interest in adopting sustainability programmes has ushered in studies regarding sustainability indicators which have continually flourished in current literature. However, limited attention is given to the development of priority ranking, which is an important input for any adopting firm. This paper presents a hybrid multi-criteria approach in determining priority areas in sustainable manufacturing (SM). Using fuzzy analytic hierarchy process to address uncertainty in hierarchical decision-making, this paper determines SM priority strategies and eventually identifies even lower level strategies. The computed sustainable manufacturing index is presented at both the organizational and operational levels for a real case study of an industrial plastic manufacturing firm. This work provides a detailed and transparent hierarchical decision-making approach based on SM framework, the use of which could be valuable to practicing managers across industries in their pursuit of greater sustainability.

Production and maintenance planning for a failure-prone deteriorating manufacturing system: a hierarchical control approach

The work presented in this paper examines the joint analysis of the optimal production and maintenance planning policies for a manufacturing system subject to random failures and repairs. When a machine fails, an imperfect corrective maintenance is undertaken. The objective of this study is to minimize a discounted overall cost consisting of preventive and corrective maintenance costs, inventory holding cost, and backlog cost. A two-level hierarchical decision-making approach is proposed, based on the determination of the mean time to failure (first level) and the statement of a joint optimization of production, preventive, and corrective maintenance policies (second level). Hence, the production, preventive, and corrective maintenance rates are determined in the second level, given the failure rates obtained from the first level. In the proposed model, the machine’s failure rate depends on the number of imperfect repairs, and as a result, the control policies of the considered planning problems therefore depend on the number of failures. The structure of the optimal control policies and the usefulness of the proposed approach are illustrated through a numerical example and a sensitivity analysis.

Decision-making approach for the distribution centre location problem in a supply chain network using the fuzzy-based hierarchical concept

In the era of global business and competitiveness, supply chain management has emerged as the critical factor for the success of companies. The past few decades have witnessed the importance of the linking channels among suppliers, manufacturers, and customers. The distribution centre (DC) location problem is a very fundamental and basic decision-making problem in terms of an efficient and effective supply chain. The decision concerning DC location, bearing in mind the current and future business perspective, is a cumbersome task and involves a large amount of investment. The selection of a site is a multicriterion decision-making problem and requires a thorough analysis of both qualitative and quantitative factors. The comparative study of different potential sites on a common set of criteria can help to handle the problem systematically and effectively. In general, most of the decision-making approaches such as the analytical hierarchy process (AHP) seem inefficient in handling the imprecise and vague linguistic comparisons of the different criteria. In this paper, a fuzzy integrated hierarchical decision-making approach is developed to solve the DC location selection problem. A case study adopted from previous literature is also discussed in this paper to show the effectiveness and robustness of the proposed methodology over the existing conventional hierarchical approaches.