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A bi-objective analysis of the r-all-neighbor p-center problem

COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期72卷 114-128页

作者： Medal, Hugh R. Rainwater, Chase E. Pohl, Edward A. Rossetti, Manuel D. Mississippi State Univ Dept Ind & Syst Engn Starkville MS 39762 USA Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA

In this paper we consider a generalization of the p-center problem called the r-all-neighbor p-center problem (RANPCP). The objective of the RANPCP is to minimize the maximum distance from a demand point to its rth-closest located facility. The RANPCP is applicable to facility location with disruptions because it considers the maximum transportation distance after (r - 1) facilities are disrupted. While this problem has been studied from a single-objective perspective, this paper studies two bi-objective versions. The main contributions of this paper are (1) algorithms for computing the Pareto-efficient sets for two pairs of objectives (closest distance vs rth-closest distance and cost vs. rth-closest distance) and (2) an empirical analysis that gives several useful insights into the RANPCP. Based on the empirical results, the RANPCP produces solutions that not only minimize vulnerability but also perform reasonably well when disruptions do not occur. In contrast, if disruptions are not considered when locating facilities, the consequence due to facility disruptions is much higher, on average, than if disruptions had been considered. Thus, our results show the importance of optimizing for vulnerability. Therefore, we recommend a bi-objective analysis. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Facility location Interdiction Mixed-integer programming bi-objective programming Disruptions Risk assessment

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bi-objective optimization of a grid-connected decentralized energy system

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2018年第2期42卷 447-465页

作者： Altintas, Onur Okten, Busra Karsu, Ozlem Kocaman, Ayse Selin Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkey

Motivated by the increasing transition from fossil fuel-based centralized systems to renewable energy-based decentralized systems, we consider a bi-objective investment planning problem of a grid-connected decentralized hybrid renewable energy system. In this system, solar and wind are the main electricity generation resources. A national grid is assumed to be a carbon-intense alternative to the renewables and is used as a backup source to ensure reliability. We consider both total cost and carbon emissions caused by electricity purchased from the grid. We first discuss a novel simulation-optimization algorithm and then adapt multi-objective metaheuristic algorithms. We integrate a simulation module to these algorithms to handle the stochastic nature of this bi-objective problem. We perform extensive comparative analysis for the solution approaches and report their performances in terms of solution time and quality based on well-known measures from the literature.

关键词： bi-objective programming CO emission grid-connected decentralized systems metaheuristic algorithms renewable energy simulation-optimization 2-stage stochastic mixed-integer programming

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A bi-objective humanitarian logistics model considering equity in the affected zones: application to a recent earthquake in Mexico

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RAIRO-OPERATIONS RESEARCH 2022年第3期56卷 1737-1762页

作者： Hernandez-Leandro, Norberto A. Ibarra-Rojas, Omar Camacho-Vallejo, Jose-Fernando Ctr Invest Matemat Unidad Monterrey AC Av Alianza Ctr 502 Apodaca 66628 Nuevo Leon Mexico Univ Autonoma Nuevo Leon Fac Ciencias Fis Matemat Monterrey Mexico

In this paper, we propose a bi-objective program to model a post-disaster strategical decision problem. We consider the situation after a catastrophic disaster occurred, in which temporary distribution centers must be located. The distribution centers consolidate aid to be delivered to affected people. We assume that affected people go to collect needed aid from temporary located distribution centers. Hence, a predefined mobility radius is considered, that indicates the distance that people are willing to travel to receive aid. Additionally, needed aid required by affected individuals is consolidated in an affected demand zone and equity constraints are included to balance the aid given to those affected zones. One objective of the proposed model is to minimize the time employed by demand zones to collect aid. In humanitarian logistics it is common that the decision maker is associated with either government or non-profit organizations that are in charge of relief. Usually, there is a limited budget to conduct the operations. Hence, the decision maker also aims to minimize the cost of locating temporary distribution centers. Both objectives are simultaneously considered. Hence, to obtain efficient solutions of this bi-objective problem, an exact AUGMECON method is proposed, which is an improved version of the classic epsilon-constraint method for multi-objective optimization. To overcome with the computational limitations shown by the exact method, a genetic algorithm is also designed and used to approximate the Pareto front. To conduct the computational experience, a case study and additional random instances are considered. The case study is based on an earthquake that recently occurred in Mexico. The results obtained by both implemented methods are compared by using different well-known metrics, such as, the number of solutions, the k-distance, the size of the space covered, and a coverage measure. It is shown that, on average, the proposed genetic algorithm outp

关键词： Humanitarian logistics bi-objective programming AUGMECON method genetic algorithm earthquake in Mexico

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On single-source capacitated facility location with cost and fairness objectives

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期289卷 959-974页

作者： Filippi, C. Guastaroba, G. Speranza, M. G. Univ Brescia Dept Econ & Management C da S Chiara 50 Brescia Italy

We consider a location problem where a planner has to decide where to open facilities which have to be reached by customers at their own cost. The planner has two objectives: cost minimization and customer satisfaction. We argue that performance and fairness, as structural components of customer satisfaction, are both captured by an equity measure called conditional beta-mean. We thus formulate the Fair Single-Source Capacitated Facility Location (F-SSCFL) problem, where the cost minimization objective is paired with the conditional beta-mean minimization objective. The resulting formulation is a bi-objective mixed-integer linear program. A weighted sum method is developed to generate a small representative set of efficient solutions to the F-SSCFL problem, and a Benders decomposition approach is implemented to handle large-scale instances. On small/medium-scale instances, we analyse the trade-off between cost and conditional beta-mean, showing that the model may be used as a managerial tool to balance direct costs and customer service. We also compare the quality of the solutions obtained with those obtained using alternative equity measures. On large-scale instances, we test Benders decomposition, showing that it is able to find good quality solutions in a relatively short computing time. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Facility location Fairness Equity bi-objective programming Benders decomposition

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A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2018年 196卷 269-283页

作者： Feng, Yanling Li, Guo Sethi, Suresh P. Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China Beijing Inst Technol Ctr Energy & Environm Policy Res Beijing 100081 Peoples R China Sustainable Dev Res Inst Econ & Soc Beijing Beijing 100081 Peoples R China Univ Texas Dallas Naveen Jindal Sch Management Dallas TX 75080 USA

Alternative machines assignment, machine sharing, and inter-cell movements are very common yet difficult to be solved integratedly in modern dynamic Cellular Manufacturing Systems (CMS). In this paper, we incorporate these issues and consider a dynamic cellular scheduling problem with flexible routes and machine sharing. We employ a mixed integer programming scheduling model to minimize both the makespan and the total workload. To solve this new model, we propose a three-layer chromosome genetic algorithm (TCGA). We first compare the performances of the proposed TCGA with the optimal solution obtained by CPLEX. Computational results show that the TCGA performs well within a reasonable amount of time. We further compare our proposed TCGA with the classic genetic algorithm (GA) and the shortest processing time (SPT) rule through numerical experiments. The results reveal that the TCGA significantly improves the performance and effectively balances the workload of machines.

关键词： Dynamic cellular manufacturing Inter-cell movement bi-objective programming Three-layer chromosome genetic algorithm Machine sharing

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Capital-constrained bi-objective newsvendor model with risk-averse preference and bankruptcy threshold

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2020年第2期120卷 406-424页

作者： Yang, Chen Wu, Desheng Fang, Weiguo Beihang Univ Sch Econ & Management Beijing Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Beihang Univ Minist Educ Key Lab Complex Syst Anal Management & Decis Beijing Peoples R China

Purpose The purpose of this paper is to investigate the major factors influencing retailer's optimal ordering strategy in a supply chain consisting of one supplier and one retailer, where the retailer is newsvendor-like and capital-constrained, and further explore the issue of supply chain coordination. Design/methodology/approach Based on bi-objective programming which is modeled under the mean-variance framework, the retailer's optimal ordering strategy is derived. Furthermore, through comparative analysis between decentralized system and centralized system along with a numerical simulation, this study examines the theoretical conclusions about supply chain coordination. Findings This study shows that a poor retailer with a high Expected Terminal Wealth Target Threshold (ETWTT) would ignore bankruptcy risk and order more, whereas a rich retailer is relatively conservative. It also reveals that in some cases, the optimal order quantity and performance of decentralized system could be both improved. However, the centralized system can always get more profit than the decentralized one. Originality/value This study uses a bankruptcy threshold to describe retailer's bankruptcy risk, and considers retailer's wealth status to formulate the model as an innovative bi-objective programming. The type of retailer as rich or poor in terms of his wealth status and asset structure is distinguished. Moreover, the impacts of retailer's type and ETWTT on ordering strategy are examined.

关键词： Risk aversion Supply chain coordination Bankruptcy threshold bi-objective programming Capital constraint

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bi-objective Vehicle Routing for Muck Transportation in Urban Road Networks

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IEEE ACCESS 2020年 8卷 114219-114227页

作者： Guo, Qinge Wang, Nengmin Su, bing Zhang, Meng Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Xian Technol Univ Sch Econ & Management Xian 710021 Peoples R China Kunming Univ Sci & Technol Fac Management & Econ Kunming 650500 Yunnan Peoples R China

As a consequence of expedited urban construction, large quantities of wastes are shipped every day. Because of the excessive pursuit of economic benefits by muck truck drivers and unreasonable transportation route choices, the accident rates of these drivers continue to increase. It is very important to optimize the safe transportation routing of muck trucks. In this paper, a safe transportation routing model was established to minimizes path safety risks and single driving time as the bi-objective, and two different situations for the optimal path of the model were analyzed. For the situation in which there are several short paths in the network, one of which is the path with the least safety risks, an exact algorithm, A*, was designed, and time complexity was analyzed. For the situation in which there is a single path or several shortest paths in the network but the path with the minimal path safety risks is not included in the set of shortest paths, an approximation algorithm, GA*, was designed, and time complexity and the approximation ratio were analyzed. Our experimental results demonstrated the validity of our developed model and approaches. Moreover, a case study is carried out on a real-case application related to the network in the Xi'an International Port District.

关键词： Accidents Safety Roads Economics Vehicles Vehicle routing Muck trucks safety transportation vehicle routing bi-objective programming approximation algorithm

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Approximating the Pareto front of a bi-objective problem in telecommunication networks using a co-evolutionary algorithm

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WIRELESS NETWORKS 2020年第7期26卷 4881-4893页

作者： Camacho-Vallejo, Jose-Fernando Garcia-Reyes, Cristobal Univ Autonoma Nuevo Leon Fac Ciencias Fis Matemat San Nicolas Mexico Ctr Inves Matemat AC Guanajuato Mexico

This paper studies a telecommunication network design problem. In this network, users must be connected to capacitated hubs. Then, hubs that concentrate users must be connected to each other and possibly to other hubs with no users. The connections in the network must lead to a tree topology. Hence, connection between hubs can be considered as looking for forming a Steiner tree. This problem is modeled as a bi-objective mathematical programming problem. One objective function minimizes user's latency with respect to the information packages flowing through the capacitated hubs, and the other objective function aims the minimization of the total network's connection cost. To approximate the Pareto front of this bi-objective problem, a co-evolutionary algorithm is developed. In the proposed algorithm, two populations are considered. Each population is associated with one objective function. The co-evolutionary operator consists of an information exchange between both populations that occurs after the genetic operators have been applied. As a result of this co-evolutionary operator, the non-dominated solutions are identified. Computational experimentation shows that the approximated Pareto fronts are representative despite their non-convexity, and they contain a sufficient number of non-dominated solutions over the tested instances. Also, thekth distance among non-dominated solutions is relatively small, which indicates that the approximated Pareto fronts are dense. Furthermore, the required computational time is very small for a problem with the characteristics herein considered.

关键词： bi-objective programming Telecommunication networks Steiner problems Pareto front Co-evolutionary algorithms

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bi-objective design-for-control of water distribution networks with global bounds

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OPTIMIZATION AND ENGINEERING 2022年第1期23卷 527-577页

作者： Ulusoy, Aly-Joy Pecci, Filippo Stoianov, Ivan Imperial Coll London Dept Civil & Environm Engn InfraSenseLabs London England

This manuscript investigates the design-for-control (DfC) problem of minimizing pressure induced leakage and maximizing resilience in existing water distribution networks. The problem consists in simultaneously selecting locations for the installation of new valves and/or pipes, and optimizing valve control settings. This results in a challenging optimization problem belonging to the class of non-convex bi-objective mixed-integer non-linear programs (BOMINLP). In this manuscript, we propose and investigate a method to approximate the non-dominated set of the DfC problem with guarantees of global non-dominance. The BOMINLP is first scalarized using the method of epsilon-constraints. Feasible solutions with global optimality bounds are then computed for the resulting sequence of single-objective mixed-integer non-linear programs, using a tailored spatial branch-and-bound (sBB) method. In particular, we propose an equivalent reformulation of the non-linear resilience objective function to enable the computation of global optimality bounds. We show that our approach returns a set of potentially non-dominated solutions along with guarantees of their non-dominance in the form of a superset of the true non-dominated set of the BOMINLP. Finally, we evaluate the method on two case study networks and show that the tailored sBB method outperforms state-of-the-art global optimization solvers.

关键词： bi-objective programming Global optimization Integer programming Branch and bound Water distribution network

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A robust counterpart approach to the bi-objective emergency medical service design problem

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APPLIED MATHEMATICAL MODELLING 2014年第3期38卷 1033-1040页

作者： Zhang, Zhi-Hai Jiang, Hai Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

The paper presents a bi-objective robust program to design a cost-responsiveness efficient emergency medical services (EMS) system under uncertainty. The proposed model simultaneously determines the location of EMS stations, the assignment of demand areas to EMS stations, and the number of EMS vehicles at each station to balance cost and responsiveness. We develop a robust counterpart approach to cope with the uncertain parameters in the EMS system. Extensive numerical studies are performed to demonstrate the benefits of our robust optimization approach. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Emergency medical services Robust optimization bi-objective programming Conic quadratic mixed-integer programming Weighting method

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