Multiple-objective Model Research Articles

Reliability-based robust geotechnical design of braced excavations considering multiple failure modes

Reliability design of braced excavation is still a challenge for geotechnical community. Optimization design is a normal method to control the safety and cost of braced excavations. This study presents an advanced reliability-based robust geotechnical design method, which can consider multiple failures and uncertainty of statistical information. A universal design sample was conducted to verify the necessity of considering the uncertainty of statistical information. Ultimate limit state and serviceability limit state of braced excavations were defined, and point estimating method was used to evaluate the standard deviation of failure probabilities. Two-objective and three-objective optimization models were developed to illustrate the application of proposed methods in detail. In addition, the performance of optimization algorithms and further application of multiple-objective models were discussed. The results from this study indicate that the proposed method has a good performance in determining the optimal design with reasonable robustness and cost. New algorithms have higher efficiency in solving nonlinear and multiple-objective optimization problems than the 2nd Non-dominated sorting genetic algorithm. This study can guide the design of retaining systems of braced excavations in clay.

High-end equipment: An improved two-sided based S&M matching and a novel Pareto refining method considering consistency

In order to avoid resources waste and monopoly in high-end equipment manufacturing, the concept of a cloud platform was proposed, in which suppliers and manufacturers can match each other in two-sided. As a result of the increasing information complexity and the fuzziness of human cognition, matching evaluation values may contain some hesitancy and ambiguity. To simulate this situation, we consequently propose an improved two-sided-based S&M matching model and a novel Pareto refining method for a high-end equipment cloud manufacturing platform under a hesitant fuzzy environment. Firstly, the traditional maximum deviation method for calculating attribute weights is improved to adapt to multiple agents in S&M matching. Sequentially, the hesitant fuzzy set (HFS) is applied to describe the fuzziness of decision agents. Simultaneously, we innovatively introduce the score and deviation of HFS to comprehensive satisfaction degree calculating as objective functions. Subsequently, considering the matching consistency, we declare a novel Pareto refining method based on an interval-valued satisfaction matrix to deal with Pareto solutions in the two-sided matching multiple objective model. Finally, an illustrative case is employed to prove the practicability and usability of the two-sided-based S&M matching model in the high-end equipment cloud manufacturing platform. It reveals that this model can not only obtain multiple matching pairs with maximal satisfaction but also can select the most consistent S&M portfolio.

Slot Die 코팅 공정의 모델 예측 제어를 위한 다중목적 최적화 기법

In the field of chemical engineering, several studies proposed to find appropriate operating parameters of the Slot Die coating process, but there is still a problem with product quality because of limitation through trial and error by workers at the manufacturing site. Moreover, PID (Proportional-Integral-Derivative) controller and MPC (Model Predictive Control) are mostly used as process control techniques but they still have some limitations such as nonlinearity, different environmental condition, complexity of response, or multiple objectives of manufacturing processes. In this paper, we propose a novel multiple objective model predictive control technique called MOORA (Multiple Objective Optimization based Regression Analysis) in the Slot Die coating process with multiple-input and multiple-output. In this study, modeling with multiple regression analysis, we derive input values and output target values. Furthermore, in the multiple objective optimization problem that find the best solution, the proposed method has shown better execution time and performance than NSGA-Ⅱ(Non-dominated Sorting Genetic Algorithm).

Task scheduling on cloud computing based on sea lion optimization algorithm

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

Goals programming multiple linear regression model for optimal estimation of electrical engineering staff according to load Demand

The linear multiple regression model is one of the prediction models whose parametric estimations could be achieved in different methods. Ordinary Least Square error (OLS) is most popular in this field of application. Although it could accurately achieve the estimation task, it fails in processing the multiple objective models. From the other side of view, the load demand for electrical energy continuously rises around the world. The governments always tackle the increase in electrical load demanding by establishing more electrical power plants and more power distribution directories. Future prediction for several electrical engineers to manage and provide technical supports for these plants and directories becomes, nowadays, urgent. This paper addresses the estimating drawback of OLS by employing the Goals Programming (GP) in the field of parametric estimation. The validity of the proposed method was applied to estimating the required number of electrical engineers, in the next coming years, as the electrical load considerably increases. Thereby, the GP was used, in this work, to determine the best linear representation for a set of data. The obtained results proved that the (GP) method is more flexible and efficient in dealing with such subject area especially in the case of multiple objective models.

A Multi-objective model for a Chemical Industry considering economic risk and environment

The research proposes a multiple objective model considering economic risk and environment in the chemical industry. The economic order quantity (EOQ) model is used for the economic aspect which considers the transportation modes and all unit quantity discount. The objective of this research is to optimize economic, safety and green performance in terms of cost minimization, risk control, and carbon emission reduction. A solution approach to obtain the optimal solution is proposed. A numerical example is performed to demonstrate the applicability and efficacy of the procedures.

Investigation of simultaneous effect of demand response and load uncertainty on distribution feeder reconfiguration

Distribution feeder reconfiguration (DFR) and demand response (DR) are the common energy consumption management methods to enhance the operation quality of distribution networks (DNs). Moreover, DFR and DR may lead to system improvement through different aspects such as reliability, total cost, and power quality. Nevertheless, the high complexity of the new smart grids has caused a lot of uncertainty in the reconfiguration problem. Given that, the use of an adequate probabilistic framework is necessary to deal with such issues. Hence, the current study investigates the effect of DR on the DFR strategy in a stochastic environment as a single-period and multiple-objective model at peak load. A parametric probabilistic analysis is administrated based on unscented transform (UT) method to capture the load and generation uncertainties. High-precision results and low computational burden are the outstanding features of the UT method. Furthermore, an improved version of Cuckoo search Algorithm (CSA) as an optimizing tool is proposed. The effectiveness of the suggested method is tested on the modified IEEE 32- and 69-bus systems. Moreover, PDF and CDF functions of random output variables are plotted and compared to verify the proposed stochastic method. The results confirm the efficient operation of the proposed algorithms.

Cost Efficient Distributed Load Frequency Control in Power Systems

Abstract The introduction of new technologies and increased penetration of renewable resources is altering the power distribution landscape which now includes a larger number of micro-generators. The centralized strategies currently employed for performing frequency control in a cost efficient way need to be revisited and decentralized to conform with the increase of distributed generation in the grid. In this paper, the use of Multi-Agent and Multi-Objective Reinforcement Learning techniques to train models to perform cost efficient frequency control through decentralized decision making is proposed. More specifically, we cast the frequency control problem as a Markov Decision Process and propose the use of reward composition and action composition multi-objective techniques and compare the results between the two. Reward composition is achieved by increasing the dimensionality of the reward function, while action composition is achieved through linear combination of actions produced by multiple single objective models. The proposed framework is validated through comparing the observed dynamics with the acceptable limits enforced in the industry and the cost optimal setups.

Distributed Model Predictive Control of Steam/Water Loop in Large Scale Ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method.

A bi-level fuzzy random model for multi-mode resource-constrained project scheduling problem of photovoltaic power plant

As the largest energy consumption country, China now pays more and more attention to photovoltaic power generation because solar energy is the largest renewable and sustainable energy reserve in the world. In this case, the photovoltaic power generation plant project plays a crucial role in sustainable development, especially in the underdeveloped northwest region of China. This paper focuses on applying bi-level programing to the multimode resource-constrained project scheduling problem (MRCPSP) in photovoltaic power generation plant construction, which simultaneously considers the practical hierarchical organization structure and an uncertain decision-making environment. A bi-level fuzzy random multiple objective model is developed, wherein the government agency is in the upper decision level, while the contractor is in the lower decision level. Then, motivated by the particular mathematical nature of the proposed bi-level MRCPSP model, a hybrid intelligent algorithm is designed. Finally, a practical case from the Shenneng Futa Kashi-Tashi-Kuergan photovoltaic power generation plant project in a Chinese energy company is applied, and the results validate the practicability of the proposed model and solution algorithm for solving practical photovoltaic power plant project scheduling problems.

A Multiple Objective Model for Vehicle Routing Problem with Time Windows: A Case Study

Green transportation has emerged as an efficient way to promote a sustainable supply chain. Transportation activities have significant impact on the whole supply chain at any decision levels. At an operational level, vehicle routing decisions are one of the most critical determinants of the release of transportation emissions. Therefore, this study develops a mathematical model, with an aim to construct routes that simultaneously minimize total cost and total CO2emissions from the transportation activities of a case study located in Can Tho City, Vietnam. A multiple-objective model with the consideration of different vehicle loading capacity, and time windows is then solved using a Weighted Tchebycheff method. Results show that route selection has a positive contribution toward a better balance between economic and environmental objectives.

A multiple objective transportation problem approach to dynamic truck dispatching in surface mines

In surface mining operations, fleet management systems seek to make optimal decisions to handle material in two steps: path production optimization and real-time truck dispatching. This paper develops a multiple objective transportation model for real-time truck dispatching. The model addresses two major drawbacks of former models. The proposed model dispatches the trucks to destinations trying to simultaneously minimize shovel idle times, truck wait times, and deviations from the path production requirements established by the production optimization stage. To evaluate the performance of the proposed model, we developed a benchmark model based on the backbone of the most widely used fleet management system in the mining industry (Modular Mining DISPATCH). Afterward, we built a discrete event simulation model of the truck and shovel operation using an iron ore mine case study, implemented both of the dispatching models, and compared the results. The implementation of the models suggests that the multiple objective model developed in this paper is able to meet the production requirements of the operation using a fleet at 85% of the size of the deterministically calculated desired fleet. In addition, the model is able to meet the full capacity of the processing plants with a fleet of 30% less trucks than the desired fleet.

Emergency Rescue Vehicle Dispatch Planning Using a Hybrid Algorithm

This paper describes the emergency rescue vehicle transportation network within the entire rescue period, and imitates rescue vehicle to select rescue route and to allocate emergency resource. The presented emergency rescue vehicle dispatch model seeks to minimize rescue time as the first objective function, minimize delay cost as the second objective function and maximize lifesaving utility as the last objective function in disaster response operations. To solve the proposed multiple objective model, a hybrid algorithm named nondominated sorting genetic algorithm (NSGA-II) with ant colony algorithm and a NSGA-II with random crossover and mutation, which can find better initial solution, are presented. In order to further prove the validity of the model and algorithm, a more complicated case is cited. Computational results are reported to illustrate the performance of the proposed model and algorithm. Statistical analysis confirms that the proposed random crossover and mutation operator outperforms the original crossover and mutation operator. The sensitivity analysis proves which parameter is more important for objective function values.

Sustainability aspects in Inventory Routing Problem: A review of new trends in the literature

Abstract This paper presents a comprehensive content analysis-based literature review of studies on Inventory Routing Problem incorporating sustainability-related aspects. A total of 40 journal articles are selected from Web of Science and Google Scholar databases, and are reviewed by categorizing them into single-objective and multiple-objective models. The review suggests that studies with single-objective models covered areas including waste management (in reverse logistics models), returnable transport item management (in closed-loop supply chain models), waste prevention and reduction (in models with perishable products), and emission reduction (in models with emission caps and carbon price). Studies on reverse logistics are limited to collecting and storing waste at a collection site, while including waste processing and disposal operations could make them more applicable. Studies on closed-loop supply chains are few with a focus on managing returnable transportation items. Future studies could consider remanufacturing, recycling and disposal of products for efficient waste management. Most of the papers in logistics of perishable products considered waste prevention rather than waste reduction. Previous studies related to emission reduction using carbon policies mainly focused on emissions from transportation. Including emissions from other logistics operations could be useful from a practical perspective. Relatively few studies developed multi-objective models. Environmental objectives in multi-objective models focused on minimizing total emissions. Few studies considered social objectives related to minimizing inventory of hazardous material and infectious medical waste, and improving customer satisfaction. Other social aspects such as those related to traffic congestion and job creation could be considered in future studies. Furthermore, application of efficient models and solution approaches to real case studies could be valuable.

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Abstract Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.

Modelling undesirable outputs in multiple objective data envelopment analysis

Recent empirical and conceptual work in data envelopment analysis (DEA) have emphasised its potential importance in highlighting the environmental performance of economic entities. Initial work in this emerging research area has focused on the separation of output factors into desirable and undesirable ones. In this paper, we describe recent developments in the modelling undesirable outputs. In particular, the modelling of undesirable outputs in the range adjusted measure (RAM) is investigated. We discuss some of the difficulties of RAM in assessing the environmental efficiency of decision-making units (DMUs) and develop a multiple objective DEA model to overcome these difficulties. The proposed multiple objective model is solved as a linear programming and its applicability as a mechanism for assessing environmental efficiency is demonstrated by evaluating the technical, ecological and process environmental quality efficiency scores of China’s provinces.

Relation Between Imprecise DESA and MOLP Methods

It is generally accepted that Data Envelopment Analysis (DEA) is a method for indicating efficiency. The DEA method has many applications in the field of calculating the relative efficiency of Decision Making Units (DMU) in explicit input-output environments. Regarding imprecise data, several definitions of efficiency can be found. The aim of our work is showing an equivalence relation between one of the models of DEA with imprecise data and Multiple Objective Linear Programming (MOLP). The relation between DEA and MOLP was studied to use interactive multiple objective models for solving the DEA problem in exact situation and find the most preferred solution. The aim of this study is to analyze an equivalent relation between imprecise DEA (IDEA) and MOLP models. In this context, we tried to solve IDEA models with interactive project procedure. The Project method is the responsible method, because it can estimate any efficient solution, and it indicates Most Preferred Solution (MPS). In addition, we will use the Data Envelopment Scenario Analysis (DESA) model. The main characteristic of DESA model is to decrease all inputs and increase all outputs and estimate one problem instead of n problems.

Novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: Case study in beverage industry

A decision model is proposed to aggregate production planning under uncertainty.The proposed multiple objective model is based on mixed chase and level strategy.The model is validated using real world data from beverage manufacturing industry.The wait and see approach is applied to solve the constructed decision model.Sensitivity analysis and several forms of trade-off analysis are also conducted. The present study proposes a novel decision model to aggregate production planning (APP) decision making problem based on mixed chase and level strategy under uncertainty where the market demand acts as the main source of uncertainty. By taking into account the novel features, the constructed model turns out to be stochastic, nonlinear, multi-stage and multi-objective. APP in practice entails multiple-objectivity. Therefore, the model involves multiple objectives such as total revenue, total production costs, total labour productivity costs, optimum utilisation of production resources and capacity and customer satisfaction, and is validated on the basis of real world data from beverage manufacturing industry. Applying the recourse approach in stochastic programming leads to empty feasible space, and therefore the wait and see approach is used instead. After solving the model using the real-world industrial data, sensitivity analysis and several forms of trade-off analysis are conducted by changing different parameters/coefficients of the constructed model, and by analysing the compromise between objectives respectively. Finally, possible future research directions, with regard to the limitations of current study, are discussed.

Improving the Resilience of Energy Flow Exchanges in Eco-Industrial Parks: Optimization Under Uncertainty

Eco-Industrial parks (EIPs) and industrial symbioses (IS) provide cost-effective and environmental friendly solutions for industries. They bring benefits from industrial plants to industrial parks and neighborhood areas. The exchange of materials, water, and energy is the goal of IS to reduce wastes, by-products, and energy consumption among a cluster of industries. However, although the IS design looks for the best set of flow exchanges among industries at a network level, the lack of access to accurate data challenges the optimal design of a new EIP. IS solutions face uncertainties. Considering the huge cost and long establishment time of IS, the existing studies cannot provide a robust model to investigate effects of uncertainty on the optimal symbioses design. This paper introduces a framework to investigate uncertainties in the EIP design. A multi-objective model is proposed to decide the optimal network of symbiotic exchanges among firms. The model minimizes the costs of multiple product exchanges and environmental impacts of flow exchanges. Moreover, this paper integrates the analysis of uncertainties effects on synergies into the modeling process. The presented models are depicted through optimizing energy synergies of an industrial zone in France. The efficiency of single and multiple objective models is analyzed for the effects of the identified uncertainties. In addition, the presented deterministic and robust models are compared to investigate how the uncertainties affect the performance and configuration of an optimal network. It is believed that the models could improve an EIP's resilience under uncertainties.

A multi-objective spatial queuing model for the location problem in natural disaster response

Emergency operations are one of the most important components of the emergency response that are required in the short time after the occurrence of disaster. For this reason specified sites for the establishment of the emergency medical centres are needed. In this study, we define a new multi-objective location problem for emergency therapeutic centres that include minimising the average response time, minimising the cost of the emergency medical centres and maximising surface coverage demands, given the size of the budget that is acceptable to guarantee a minimum level of coverage. From the point of view of computational complexity, it is classified as an NP-hard problem, since it will require algorithms with the ability to solve problems in non-deterministic polynomial (NP) time, but the solution can be verified in polynomial time. Moreover, the term hard means that it is at least as hard as the hardest problems in NP. Hence, we used PASA, NSGAII, and NRGA algorithms to solve this multiple objective model. After comparing the solutions, NSGAII had a better performance.