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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Ren, X. Feng, S. Wu, X. Qi, J. Northwestern Polytech Univ Sch Management Xian Peoples R China Changan Univ Sch Construct Machinery Key Lab Rd Construct Technol & Equipment MOE Xian Peoples R China

ISBN: (纸本)9781665437714

Transportation activities have caused a large amount of energy consumption and environmental pollution. With increasing emphasis on environmental protection, transportation companies have to pay more attention to social benefits. At the same time, scorching competition has put forward a higher demand for customer satisfaction. This paper studies a pollution-routing problem (PRP) to minimize several pollutants emissions and find maximum improvements in delay time at customers based on soft time windows. The routing model focuses on a mixed-energy fleet, which introduces electric vehicles into a homogeneous combustion engine fleet. A Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to find the Pareto-frontier of the PRP model. We investigate the effect of the electric vehicle's mileage on two objectives by sensitivity analysis to understand the problem better.

关键词： Bi-objective NSGA-II mixed-energy fleet pollution-routing problem

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An evolutionary algorithm for the multi-objective pick-up and delivery pollution-routing problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2019年第1期26卷 302-317页

作者： Bravo, Mauricio Pradenas Rojas, Lorena Parada, Victor Univ Concepcion Ingn Ind Concepcion Chile Univ Santiago Chile Ingn Informat Santiago Chile

The design of sustainable logistics solutions poses new challenges for the study of vehicle-routing problems. The design of efficient systems for transporting products via a heterogeneous fleet of vehicles must consider the minimization of cost, emissions of greenhouse gases, and the ability to serve every customer within an available time slot. This phenomenon gives rise to a multi-objective problem that considers the emission of greenhouse gases, the total traveling time, and the number of customers served. The proposed model is approached with an epsilon-constraint technique that allows small instances to be solved and an evolutionary algorithm is proposed to deal with complex instances. Results for small instances show that all the points that approach the Pareto frontier found by the evolutionary algorithm are nondominated by any solution found by the multi-objective model. For complex instances, nondominated solutions that serve most of the requests are found with low computational requirements.

关键词： multi-objective pick-up and delivery problem pollution-routing problem green logistics routing carbon footprint

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Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms

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JOURNAL OF CLEANER PRODUCTION 2020年 276卷 122927-122927页

作者： Tirkolaee, Erfan Babaee Goli, Alireza Faridnia, Amin Soltani, Mehdi Weber, Gerhard-Wilhelm Mazandaran Univ Sci & Technol Dept Ind Engn Babol Iran Istinye Univ Dept Ind & Syst Engn Istanbul Turkey Yazd Univ Dept Ind Engn Yazd Iran Islamic Azad Univ Dept Civil Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Qazvin Branch Dept Ind & Mech Engn Qazvin Iran Poznan Univ Tech Fac Engn Management Poznan Poland Middle East Tech Univ Inst Appl Math Ankara Turkey

Cross-docking practice plays an important role in improving the efficiency of distribution networks, especially, for optimizing supply chain operations. Moreover, transportation route planning, controlling the Greenhouse Gas (GHG) emissions and customer satisfaction constitute the major parts of the supply chain that need to be taken into account integratedly within a common framework. For this purpose, this paper tries to introduce the reliable pollution-routing problem with Cross-dock Selection (PRP-CDS) where the products are processed and transported through at least one cross-dock. To formulate the problem, a Bi-Objective Mixed-Integer Linear Programming (BOMILP) model is developed, where the first objective is to minimize total cost including pollution and routing costs and the second is to maximize supply reliability. Accordingly, sustainable development of the supply chain is addressed. Due to the high complexity of the problem, two well-known meta-heuristic algorithms including Multi-Objective Simulated-annealing Algorithm (MOSA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are designed to provide efficient Pareto solutions. Furthermore, the e-constraint method is applied to the model to test its applicability in small-sized problems. The efficiency of the suggested solution techniques is evaluated using different measures and a statistical test. To validate the performance of the proposed methodology, a real case study problem is conducted using the sensitivity analysis of demand parameter. Based on the main findings of the study, it is concluded that the solution techniques can yield high-quality solutions and NSGA-II is considered as the most efficient solution tool, the optimal route planning of the case study problem in delivery and pick-up phases is attained using the best-found Pareto solution and the highest change in the objective function occurs for the total cost value by applying a 20% increase in the demand parameter. (C) 2020 Elsev

关键词： pollution-routing problem Cross-dock selection Reliability Sustainable development Taguchi design method NSGA-II

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Modified Brain Storm Optimization Algorithm in Objective Space for pollution-routing problem

Modified Brain Storm Optimization Algorithm in Objective Spa...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Rauniyar, Amit Nath, Rahul Muhuri, Pranab K. South Asian Univ Dept Comp Sci New Delhi 110021 India

ISBN: (纸本)9781728121536

Increase in transportation has led to alarming pollution globally causing an adverse effect on the environment. pollution-routing problem (PRP), a version of well-known Vehicle routing problem, has attracted researchers to develop efficient solutions to cut-off fuel consumption and minimize greenhouse gas emissions. This paper presents a novel effort with an objective pertaining to the minimization of fuel consumption (CO2 emissions). Thus, we have modified a recently developed Brain Storm Optimization in Objective Space (BSO-OS) algorithm to design a solution technique for PRP. Further, we have incorporated a recombination operator and a method to produce diverse solutions into the existing BSO-OS. The objective defined here is based on the load factor;thus, our modified BSO-OS concentrates towards deducting the load and minimize the fuel consumption efficiently. For comparison, we have also implemented a Genetic Algorithm (GA) for PRP. The comparative study of the experimental results demonstrates the efficacy of the algorithms for the addressed problem. BSO-OS outperforms GA in terms of running time and generates a varied range of solutions.

关键词： Brain Storm Optimization pollution-routing problem Evolutionary algorithms Genetic algorithm objective space

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Carbon pricing initiatives-based bi-level pollution routing problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第1期286卷 203-217页

作者： Qiu, Rui Xu, Jiuping Ke, Ruimin Zeng, Ziqiang Wang, Yinhai Sichuan Univ Business Sch Chengdu 610064 Peoples R China Univ Washington Dept Civil & Environm Engn Seattle WA 98195 USA

The pollution-routing problem aims to route a number of vehicles and determines their speeds on each route segment to minimize total cost, including fuel, emission and driver costs. Recently, carbon pricing initiatives have been widely implemented worldwide. With consideration of the interactions between carbon pricing initiatives and freight schedules, this paper presents a carbon pricing initiatives-based bi-level pollution routing problem involving an authority and a freight company. An interactive solution approach integrating a fuzzy logic controlled particle swarm optimization and a modified adaptive large neighborhood search heuristic is designed to search for solutions for the carbon pricing initiatives-based bi-level pollution routing problem. Computational experiments and analysis are then conducted to shed light on the influence of carbon pricing initiatives on carbon emissions and the total cost of freight companies. In this part, extended models for the carbon pricing initiatives-based bi-level pollution routing problem with a freight company delivering to multiple regions and with multiple freight companies are proposed and computed using the algorithms based on the interactive solution approach. The results indicate that the proposed method can promote freight company improvements in emission performance, and assist authorities in making decisions for road freight transport carbon emission reduction. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Transportation pollution-routing problem Carbon pricing initiatives Modified adaptive large neighborhood search heuristic Fuzzy logic controlled particle swarm optimization

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A two-phase Pareto local search heuristic for the bi-objective pollution-routing problem

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NETWORKS 2018年第3期72卷 311-336页

作者： Costa, Luciano Lust, Thibaut Kramer, Raphael Subramanian, Anand Ecole Polytech Montreal Dept Math & Genie Ind Montreal PQ H3C 3A7 Canada Gerad Montreal PQ H3C 3A7 Canada Univ Paris 06 UPMC Sorbonne Univ CNRSUMR 7606LIP6 F-75005 Paris France Univ Modena & Reggio Emilia Dipartimento Sci & Metodi Ingn I-42122 Reggio Emilia Italy Univ Fed Paraiba Dept Sistemas Comp Ctr Informat Rua Escoteiros BR-58058600 Joao Pessoa Paraiba Brazil

This article deals with the bi-objective pollution-routing problem (bPRP), a vehicle routing variant that arises in the context of green logistics. The two conflicting objectives considered are the minimization of the CO2 emissions and the costs related to driver's wages. A multi-objective approach based on the two-phase Pareto local search heuristic is employed to generate a good approximation of the Pareto front. During the first phase of the method, a first set of potentially efficient solutions is obtained by solving a series of weighted sum problems with an efficient heuristic originally developed to solve the single-objective PRP. A dichotomous scheme is used to generate the different weight sets in an automatic way. In the second phase, the set is improved with an efficient Pareto local search (PLS) procedure. The use of PLS allows to limit the number of computational demanding weighted sum problems solved in the first phase, while keeping high-quality results. Extensive computational experiments over existing benchmark instances show that the proposed approach leads to better results in less CPU time when compared to those obtained by state-of-the-art methods.

关键词： combinatorial optimization heuristics multi-objective optimization Pareto local search pollution-routing problem

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The capacitated pollution routing problem with pickup and delivery in the last mile

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ASIA PACIFIC JOURNAL OF MARKETING AND LOGISTICS 2019年第4期31卷 1193-1215页

作者： Tan, Yuyang Deng, Lei Li, Longxiao Yuan, Fang Chongqing Univ Coll Mech Engn Chongqing Peoples R China

Purpose With the increasing awareness of global warming and the important role of last mile distribution in logistics activities, the purpose of this paper is to build an environmental and effective last mile distribution model considering fuel consumption and greenhouse gas emission, vehicle capacity and two practical delivery service options: home delivery (HD) and pickup site service (PS). This paper calls the problem as the capacitated pollution-routing problem with pickup and delivery (CPRPPD). The goal is to find an optimal route to minimize operational and environmental costs, as well as a set of optimal speeds over each arc, while respecting capacity constraints of vehicles and pickup sites. Design/methodology/approach To solve this problem, this research proposes a two-phase heuristic algorithm by combining a hybrid ant colony optimization (HACO) in the first stage and a multiple population genetic algorithm in the second stage. First, the HACO is presented to find the minimal route solution and reduce distribution cost based on optimizing the speed over each arc. Findings To verify the proposed CPRPPD model and algorithm, a real-world instance is conducted. Comparing with the scenario including HD service only, the scenario including both HD and PS option is more economical, which indicates that the CPRPPD model is more efficient. Besides, the results of speed optimization are significantly better than before. Practical implications - The developed CPRPPD model not only minimizes delivery time and reduces the total emission cost, but also helps logistics enterprises to establish a more complete distribution system and increases customer satisfaction. The model and algorithm of this paper provide optimal support for the actual distribution activities of logistics enterprises in low-carbon environment, and also provide reference for the government to formulate energy-saving and emission reduction policies. Originality/value This paper provides a great space

关键词： Home delivery and pickup site services Hybrid two-phase algorithm pollution-routing problem

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A disjunctive convex programming approach to the pollution-routing problem

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2016年 94卷 61-79页

作者： Fukasawa, Ricardo He, Qie Song, Yongjia Univ Waterloo Fac Math Dept Combinator & Optimizat Waterloo ON N2L 3G1 Canada Univ Minnesota Dept Ind & Syst Engn Minneapolis MN 55455 USA Virginia Commonwealth Univ Dept Stat Sci & Operat Res Richmond VA 23284 USA

The pollution-routing problem (PRP) aims to determine a set of routes and speed over each leg of the routes simultaneously to minimize the total operational and environmental costs. A common approach to solve the PRP exactly is through speed discretization, i.e., assuming that speed over each arc is chosen from a prescribed set of values. In this paper, we keep speed as a continuous decision variable within an interval and propose new formulations for the PRP. In particular, we build two mixed-integer convex optimization models for the PRP, by employing tools from disjunctive convex programming. These are the first arc-based formulations for the PRP with continuous speed. We also derive several families of valid inequalities to further strengthen both models. We test the proposed formulations on benchmark instances. Some instances are solved to optimality for the first time. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： pollution-routing problem Speed optimization Green transportation Mixed-integer convex programming

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