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A bi-objective programming model for designing compact and balanced territories in commercial districting

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2011年第5期19卷 885-895页

作者： Angelica Salazar-Aguilar, M. Rios-Mercado, Roger Z. Luis Gonzalez-Velarde, Jose Univ Autonoma Nuevo Leon Grad Program Syst Engn San Nicolas Nuevo Leon Mexico Tecnol Monterrey Ctr Qual & Mfg Monterrey Nuevo Leon Mexico

In this paper, we address a territory design problem arising from a bottled beverage distribution company. We propose a bi-objective programming model where dispersion and balancing with respect to the number of customers are used as performance criteria. Constraints such as connectivity and balancing with respect to sales volume are considered in the model. Most of the work in territory design has been developed for single-objective models. To the best of our knowledge, this is the first multi-objective approach for this commercial territory design problem, and in particular, for territory design with connectivity constraints. We propose an improved epsilon-constraint method for generating the optimal Pareto front. Empirical evidence over a variety of instances shows that the improved method is well suited for finding optimal Pareto fronts with no more computational effort than the traditional method. Instances of up to 150 units and 6 territories are solved in relatively short amount of time. For this problem, the improved method finds practically the same fronts than those, found by the traditional epsilon-constraint method. In addition, we observe that when the firm reduces the tolerance in the imbalance of sales volume the efficient fronts change and when the number of territories increases, the balance with respect to the number of customers becomes harder to achieve. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Pareto front Improved epsilon-constraint method bi-objective programming Territory design

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A bi-objective programming model for loading finished-vehicles in containers with adjustable brackets

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MARITIME POLICY & MANAGEMENT 2024年第3期51卷 392-419页

作者： Tan, Zhijia Shao, Shuai Wang, Zhixin Wang, Hua Han, Linghui Dalian Maritime Univ Sch Maritime Econ & Management Dalian Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore

The containerized transportation mode has attracted more attention from many finished-vehicle (FV) logistics companies and ocean carriers which can provide door-to-door service and organize the multi-model transportation. This paper considers the FV loading problem for a carrier who uses containerized transportation mode. To increase the loading factor of containers and guarantee the safety of vehicles from damage, an adjustable bracket is introduced to place two-layer vehicles by tilting the upper pallet. A bi-objective programming problem is proposed in the sense of maximizing the safety margins of vehicles by setting the configuration of an adjustable bracket and the locations of the vehicles on the pallets. For any given combination of two vehicle types, we proved that the Pareto frontier of the bi-objective programming problem is a line and the Pareto optimal slope of the upper pallet can be determined by a maximization problem. With the Pareto frontier, the general FV loading problem with multiple vehicle types can be modeled as an integer programming problem and calculated by traditional programming algorithms. Areal loading task for the COSCO was adopted to depict our model and method.

关键词： Finished-vehicle loading containerized transportation adjustable brackets bi-objective programming

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An iterative method for solving a bi-objective constrained portfolio optimization problem

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2019年第2期72卷 479-498页

作者： Bezoui, Madani Moulai, Mustapha Bounceur, Ahcene Euler, Reinhardt Univ Mhamed Bougara Boumerdes Boumerdes 35000 Algeria USTHB LaROMad Fac Math BP 32 Algiers 16111 Algeria UBO UMR CNRS 6285 Lab STICC Brest France

In this work, we consider the problem of portfolio optimization under cardinality and quantity constraints. We use the standard model of mean-variance in its bi-objective form which is presented here as a bi-objective quadratic programming problem under cardinality and quantity constraints. This problem is NP-hard, which is why the majority of methods proposed in the literature use metaheuristics for its resolution. In this paper, we propose an iterative method for solving constrained portfolio optimization problems. Experiments are performed with major market indices, such as the Hang Seng, DAX, FTSE, S&P 100, Nikkei, S&P 500 and Nasdaq using real-world datasets involving up to 2196 assets. Comparisons with two exact methods and a metaheuristic are performed. These results show that the new method allows to find efficient portfolio fronts in reasonable time.

关键词： Cardinality and quantity constraints Cardinality portfolio selection bi-objective programming Mixed integer programming Steepest descent method Pascoletti-Serafini method

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Route and speed optimization for liner ships under emission control policies

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年 110卷 330-345页

作者： Zhen, Lu Hu, Zhuang Yan, Ran Dan Zhuge Wang, Shuaian Shanghai Univ Sch Management Shang Da Rd 99 Shanghai 200444 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Hong Kong Peoples R China

Pollutants such as nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matters (PM) generated by shipping industry are increasing in recent years. In order to control the ship emission pollution, the International Maritime Organization (IMO) has established the Emission Control Areas (ECAs). In the fierce competition of the shipping market, liner shipping companies are looking for strategies to maintain their core competencies under the emission control policy. To achieve this goal, this paper first proposes a bi-objective mixed integer linear programming model, aiming to optimize sailing routes and speeds within and outside the ECA while minimizing the total fuel cost and SO2 emissions. Then, a new algorithm is developed to solve the proposed model by combining the two-stage iterative algorithm and fuzzy logic method based on e-constraint. Finally, this paper compares and analyzes the navigation plan of a real sailing route considering and not considering the effects of ECA. Some experiments are conducted to analyze the effects of fuel cost, decision makers, and ECA boundaries on the total fuel cost and SO2 emissions. The results indicate that the proposed model and algorithm can contribute to save fuel cost and reduce SO2 emissions under the ECA policy and provide different Pareto optimal solutions. Thus, the effectiveness of the model and the efficiency of the algorithm are validated.

关键词： Emission Control Area (ECA) bi-objective programming Two-stage iterative algorithm Route optimization Sailing speed optimization

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Joint Ship Schedule Design and Sailing Speed Optimization for a Single Inland Shipping Service with Uncertain Dam Transit Time

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TRANSPORTATION SCIENCE 2018年第6期52卷 1570-1588页

作者： Tan, Zhijia Wang, Yadong Meng, Qiang Liu, Zhixue Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore Natl Univ Singapore Inst Operat Res & Analyt Singapore 119077 Singapore Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

Inland shipping has received extensive attention in practice and is a high energy-consuming service sensitive to the river streamflow speed and the uncertain lock traversing time of dams. In this study, we propose a joint ship schedule design and sailing speed optimization problem for a single inland shipping service by incorporating the effects of nonidentical streamflow speed and the uncertain dam transit time. A bi-objective programming model is developed for the proposed problem to simultaneously minimize the total bunker consumption and the ship round-trip time with service level constraints. The service level at each port call is captured by the punctual arrival probability of the ship not less than a predefined value. By introducing the term of buffer times, we analytically derive the Pareto optimal solutions of the ship sailing speeds and schedule. The theoretical method is extended to the case with an arbitrary number of dams and the uncertain port service and leg voyage time. We also examine the relationship between the Pareto optimal solution in this study and the solutions of the previous joint sailing speed and schedule optimization problems in the liner shipping service design. Numerical experiments based on the Yangtze River are conducted.

关键词： inland shipping ship schedule and speed optimization bi-objective programming uncertainty

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Collaborative multi-depot logistics network design with time window assignment

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 140卷 112910-112910页

作者： Wang, Yong Zhang, Shuanglu Guan, Xiangyang Peng, Shouguo Wang, Haizhong Liu, Yong Xu, Maozeng Chongqing Jiaotong Univ Sch Econ & Management Chongqing 400074 Peoples R China Univ Elect Sci & Technol Sch Management & Econ Chengdu 610054 Sichuan Peoples R China Univ Washington Dept Civil & Environm Engn Seattle WA 98195 USA Oregon State Univ Sch Civil & Construct Engn Corvallis OR 97330 USA

In logistics operation, delivery times are often uncertain for customers, and accommodating this uncertainty poses operation challenges as well as extra cost for logistics service providers. The delivery time uncertainty is particularly an issue if there are multiple service providers in a logistics network. To address this issue, we formulate and solve a collaborative multi-depot vehicle routing problem with time window assignment (CMDVRPTWA) to effectively reduce the impact of changing time windows on operating costs. This paper establishes a bi-objective programming model that optimize the total operating cost and the total number of delivery vehicles. A hybrid heuristic algorithm consisting of K-means clustering, Clarke-Wright (CW) saving algorithm and an Extended Non-dominated Sorting Genetic Algorithm-II (E-NSGA-II) is presented to efficiently solve CMDVRPTWA. The clustering and CW saving algorithm are employed to increase the likelihood of finding the optimal vehicle routes by identifying a feasible initial solution. The E-NSGA-II procedure combines partial-mapped crossover (PMC), relocation, 2-opt* exchange and swap mutation operations to find the optimal solution with pre-defined iteration and termination rules. Profit allocation schemes are then analyzed using the Game Quadratic programming (GQP) method, and the optimal sequences of joining coalitions are obtained based on the principle that coalition participants' benefits should be non-decreasing when a new participant joins the coalition. We conduct three empirical studies on a small-scale example, on several benchmark datasets and on a large-scale logistics network in Chongqing city, China. Further comparative analysis indicates that E-NSGA-II outperforms most other algorithms in solving CMDVRPTWA. This novel approach identifies profit allocation strategies that ensure the stability and reliability of the collaborative coalitions in the context of flexible customer service time windows, and can be util

关键词： Collaborative multi-depot vehicle routing problem Time window assignment bi-objective programming Hybrid heuristic algorithm Game quadratic programming

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EXACT METHOD BASED ON SOLUTION SPACE CUT FOR bi-objective SERU PRODUCTION

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2023年第12期19卷 8709-8730页

作者： Yu, Zixuan Sun, Wei Zhou, Jianwen Liu, Yang Yu, Jiao Li, Xiaolong Northeastern Univ Dept Intelligent Data & Syst Engn State Key Lab Synthet Automat Proc Ind Shenyang Liaoning Peoples R China Liaoning Univ Business Sch Shenyang Liaoning Peoples R China Beijing Inst Comp Technol & Applicat Beijing Peoples R China Ecol Environm Bur Chifeng City Chifeng Inner Mongolia Peoples R China

Seru Production is an innovative production mode frequently utilized in electronic assembly enterprises. Up to now, there is no exact algorithm for the bi-objective Seru Production. We develop an innovative exact method based on decomposition and cutting solution space to obtain the exact Pareto-optimal solutions for the first time. Seru Production is decomposed into two decision processes, i.e., seru formation and seru scheduling. We define dominated seru formations and cut 97% dominated seru formations based on the ideal point and Non-Dominated Incumbent Solutions (NDIS). Thus, the Pareto-optimal solutions is obtained by executing Non-dominant Sort (NS) for all the non-dominated seru formations. The epsilon-constraint method is applied in seru scheduling to obtain the Pareto-optimal schedules for a given seru formation. In addition, the current best algorithms for solving multi-objective Seru Production are used to ensure the initial NDIS high-quality. Finally, parallel programming is performed according to the characteristics of Seru Production. Test data verify that proposed exact method can find the Pareto-optimal solution set for all small-scale and most medium-scale instances of bi-objective Seru Production.

关键词： Seru Production bi-objective programming exact method so-lution space cut seru formation dominance intelligent algorithm parallel pro-gramming

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GRASP strategies for a bi-objective commercial territory design problem

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JOURNAL OF HEURISTICS 2013年第2期19卷 179-200页

作者： Angelica Salazar-Aguilar, M. Rios-Mercado, Roger Z. Luis Gonzalez-Velarde, Jose Univ Montreal HEC Montreal CIRRELT Montreal PQ H3T 2A7 Canada Univ Autonoma Nuevo Leon Grad Program Syst Engn San Nicolas De Los Garza 66450 NL Mexico Tecnol Monterrey Ctr Qual & Mfg Monterrey 64849 NL Mexico

A bi-objective commercial territory design problem motivated by a real-world application from the bottled beverage distribution industry is addressed. The problem considers territory compactness and balancing with respect to number of customers as optimization criteria. Previous work has focused on exact methods for small- to medium-scale instances. In this work, a GRASP framework is proposed for tackling considerably large instances. Within this framework two general schemes are developed. For each of these schemes two strategies are studied: (i) keeping connectivity as a hard constraint during construction and post-processing phases and, (ii) ignoring connectivity during the construction phase and adding this as another minimizing objective function during the post-processing phase. These strategies are empirically evaluated and compared to NSGA-II, one of the most successful evolutionary methods known in literature. Computational results show the superiority of the proposed strategies. In addition, one of the proposed GRASP strategies is successfully applied to a case study from industry.

关键词： Combinatorial optimization bi-objective programming Territory design GRASP

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bi-objective heuristics for multi-item freights distribution planning problem in crossdocking networks

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第9-12期58卷 1201-1216页

作者： Marjani, Mohammad Reza Husseini, Seyyed Mohammad Moattar Karimi, Behrooz Amirkabir Univ Technol Dept Ind Engn Tehran Iran

Crossdocking is a distribution strategy in which distribution centers operate as transfer points to harmonize the continuous physical flow through supply chain with the least storage. The coordination of this system in a network of crossdocks with multitype and time-restricted pickups and deliveries requires a complex planning. This paper addresses the distribution planning problem of crossdocking network, considering transshipment possibility among crossdocks and tardiness permission for some pickups. The problem is formulated as a bi-objective integer-programming model minimizing total transportation and holding costs and total tardiness. An innovative heuristic procedure to construct an effective initial solution and three improving algorithms based on appropriate meta-heuristics (variable neighborhood search, tabu search, and simulated annealing) is developed to solve the problem. Experimental results demonstrate the potency of proposed algorithms achieving remarkable results.

关键词： Distribution planning Crossdocking bi-objective programming Heuristic Meta-heuristics

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Improved tabu search algorithm for the open vehicle routing problem with soft time windows and satisfaction rate

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8725-S8733页

作者： Xia, Yangkun Fu, Zhuo Cent S Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

The open vehicle routing problem (OVRP) has a wide range of applications in the field of logistics distribution. A model of OVRP with soft time windows and satisfaction rate is analyzed here in order to reduce the logistics distribution cost. The minimization of the number of vehicles required is taken as the primary objective, while the minimization of the traveling distance cost and the deviation cost of the time windows are used as secondary objectives. A corresponding mathematical model with bi-objective programming is established for the problem. Based on the coding rules of natural numbers, several strategies such as the adaptive penalty mechanism, multi-neighborhood structure and re-initialization rule are embedded in the tabu search algorithm (TSA), resulting in an improved TSA (ITSA). Computational results are provided and these are compared with other methods in the literature, demonstrating the effectiveness of the ITSA.

关键词： Open vehicle routing problem (OVRP) Tabu search algorithm (TSA) bi-objective programming Adaptive penalty mechanism Logistics distribution Soft time windows Satisfaction rate

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