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bi-objective mixed integer linear programming for managing building clusters with a shared electrical energy storage

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COMPUTERS & OPERATIONS RESEARCH 2018年 96卷 172-186页

作者： Dai, Rui Charkhgard, Hadi Univ S Florida Dept Ind & Management Syst Engn Tampa FL 33620 USA

Emerging smart grid infrastructures are allowing buildings to connect to components in other buildings and utilize them in different ways. Clearly, these interconnected building clusters provide new opportunities for building operators to collaborate and help reduce their operational costs over a planning horizon. Nevertheless, since each building can be treated as an independent decision maker here, related fairness concerns have to be addressed in these collaborative environments. We address these issues on building clusters when a single electrical energy storage is shared between two buildings with deterministic demand. We introduce three energy storage sharing strategies, and develop a bi-objective mathematical formulation for each strategy. Several techniques such as piecewise McCormick relaxation are employed for linearizing non-linear terms in the proposed formulations. An extensive computational study demonstrates the efficacy of our proposed linearization techniques, and compares all three strategies in terms of fairness and freedom. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Smart buildings Shared energy storage Piecewise McCormick relaxation bi-objective mixed integer linear programming

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A NEW APPROACH TO SELECT THE BEST SUBSET OF PREDICTORS IN linear REGRESSION MODELLING: bi-objective mixed integer linear programming

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ANZIAM JOURNAL 2019年第1期61卷 64-75页

作者： Charkhgard, Hadi Eshragh, Ali Univ S Florida Dept Ind & Management Syst Engn Tampa FL 33620 USA Univ Newcastle Sch Math & Phys Sci Callaghan NSW 2308 Australia

We study the problem of choosing the best subset of p features in linear regression, given n observations. This problem naturally contains two objective functions including minimizing the amount of bias and minimizing the number of predictors. The existing approaches transform the problem into a single-objective optimization problem. We explain the main weaknesses of existing approaches and, to overcome their drawbacks, we propose a bi-objective mixed integer linear programming approach. A computational study shows the efficacy of the proposed approach.

关键词： linear regression best subset selection bi-objective mixed integer linear programming

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Energy-Conscious Common Operation Scheduling in an Identical Parallel Machine Environment

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INTERNATIONAL JOURNAL OF ENGINEERING 2024年第7期37卷 1443-1465页

作者： Ataei, H. Ahmadizar, F. Arkat, J. Univ Kurdistan Dept Ind Engn Sanandaj Iran

The relentless growth of global energy consumption poses a multitude of complex challenges, including the depletion of finite energy resources and the exacerbation of greenhouse gas emissions, which contribute to climate change. In the face of these pressing environmental concerns, the manufacturing sector, a significant energy consumer, is under immense pressure to adopt sustainable practices. The critical intersection of energy consumption management and production operation scheduling emerges as a pivotal domain for addressing these challenges. The scheduling of common operations, exemplified by the cutting stock problem in industries like furniture and apparel, represents a prevalent challenge in production environments. For the first time, this paper pioneers an investigation into an identical parallel machine scheduling problem, taking into account common operations to minimize total energy consumption and total completion time concurrently. For this purpose, two bi-objective mixed integer linear programming models are presented, and an augmented epsilon - constraint method is used to obtain the Pareto optimal front for small-scale instances. Considering the NP -hardness of this problem, a nondominated sorting genetic algorithm (NSGA-II) and a hybrid non -dominated sorting genetic algorithm with particle swarm optimization (HNSGAII-PSO) are developed to solve medium- and large-scale instances to achieve good approximate Pareto fronts. The performance of the proposed algorithms is assessed by conducting computational experiments on test problems. The results demonstrate that the proposed HNSGAII-PSO performs better than the suggested NSGA-II in solving the test problems.

关键词： bi-objective mixed integer linear programming Identical Parallel Machine Scheduling Common Operation Total Energy Consumption Total Completion Time

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Mathematical models for the bi-objective integrated delivery and installation routing problem 10

Mathematical models for the bi-objective integrated delivery...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Zhou, Shanshan Wang, Zheng Li, Yantong Dalian Maritime Univ Sch Maritime Econ & Management Dalian Peoples R China

ISBN: (纸本)9798350373981;9798350373974

This paper studies a new integrated delivery and installation routing problem arising from a home-furnishing delivery and installation service. A fleet of vehicles is arranged to provide both delivery and installation service for customers, and each vehicle is assigned to a team of technicians with multiple skills. Depending on worker capabilities, certain customers receive both delivery and installation service from a single vehicle, while others necessitate separate vehicles for each service. The company should make an optimal plan for this home-delivery and installation service, encompassing decisions such as order acceptance, assignment, and sequencing of delivery and installation tasks within each vehicle, along with worker allocation, balancing cost and service level. We provide two bi-objective mixed-integer linear programs that can be solved by off-the-shelf commercial solvers. The two objectives are maximizing the company's service profit and minimizing customer waiting time between delivery and installation services. We conduct numerical experiments on both models using randomly generated cases. Additionally, sensitivity analysis is performed to provide practical managerial insights.

关键词： furniture home-delivery and installation service integrated delivery and installation routing bi-objective mixed integer linear programming CPLEX

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bi-objective Optimization Model for Assembly Line Balancing with Worker Satisfaction 16

Bi-objective Optimization Model for Assembly Line Balancing ...

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16th International Conference on Service Systems and Service Management (ICSSSM)

作者： Liu, Ming Liu, Xin Liu, Zhongzheng Tongji Univ Sch Econ & Management Shanghai Peoples R China

ISBN: (纸本)9781728119410

This paper considers a joint problem of assigning workers and balancing assembly line, considering worker satisfaction. Two important managerial objectives are the minimisation of the cycle time and the maximisation of the worker satisfaction. With the perfection of labor protection law, production enterprises cannot only consider the production efficiency of the company in the process of organizing production, but also take into account the satisfaction of workers. Different workers usually have different preferences for different types of operations. Therefore, production enterprises need to consider different workers'satisfaction with different assembly operations when assigning assembly operations to workers. The main mission of this work is to improve the efficiency of assembly line balancing, that is, to reduce the cycle time, as well as increasing total worker satisfaction. A bi-objective mixed integer linear programming model is proposed. Then epsilon constraint algorithm is developed to verify the correctness and validity of the proposed model. Simultaneously, the framework of NSGA-II algorithm is designed to solve large scale problems in future work.

关键词： Assembly line worker assignment and balancing problem bi-objective mixed integer linear programming Worker satisfaction Epsilon-constraint method NSGA-II algorithm

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A robust optimization approach for solving problems in conservation planning

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ECOLOGICAL MODELLING 2018年 368卷 288-297页

作者： Haider, Zulqarnain Charkhgard, Hadi Kwon, Changhyun Univ S Florida Dept Ind & Management Syst Engn Tampa FL 33620 USA

In conservation planning, the data related to size, growth and diffusion of populations is sparse, hard to collect and unreliable at best. If and when the data is readily available, it is not of sufficient quantity to construct a probability distribution. In such a scenario, applying deterministic or stochastic approaches to the problems in conservation planning either ignores the uncertainty completely or assumes a distribution that does not accurately describe the nature of uncertainty. To overcome these drawbacks, we propose a robust optimization approach to problems in conservation planning that considers the uncertainty in data without making any assumption about its probability distribution. We explore two of the basic formulations in conservation planning related to reserve selection and invasive species control to show the value of the proposed robust optimization. Several novel techniques are developed to compare the results produced by the proposed robust optimization approach and the existing deterministic approach. For the case when the robust optimization approach fails to find a feasible solution, a novel bi-objective optimization technique is developed to handle infeasibility by modifying the level of uncertainty. Some numerical experiments are conducted to demonstrate the efficacy of our proposed approach in finding more applicable conservation planning strategies. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Conservation planning Robust optimization Invasion control Reserve selection bi-objective mixed integer linear programming

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