Criteria selection and multi-objective optimization of aircraft landing problem

Inspired by the similarities of the aircraft landing problem (ALP) and the single machine scheduling problem, we propose a criteria selection method that has been used successfully in the single machine scheduling problem to determine appropriate objective functions of ALP. First, for four different types of criteria—min-max, min-sum, completion time related, and due-dates related criteria—their corresponding physical meanings in ALP are elaborated. Then, a criteria selection method is proposed to determine several appropriate criteria, which are taken as the multi-objective while modeling ALP. Different solution algorithms, including Imperialist Competitive Algorithm (ICA), are adopted to solve the multi-objective ALP. Finally, the performance of the proposed model and method are evaluated using a set of benchmark instances. The computational results demonstrate the efficiency of our approach for solving ALP, which can simultaneously improve punctual performance, enhance runway utilization, reduce air traffic controller workload, and maintain equity among airlines.     

Berth scheduling by customer service differentiation: A multi-objective approach

In this paper the discrete and dynamic berth allocation problem is formulated as a multi-objective combinatorial optimization problem where vessel service is differentiated upon based on priority agreements. A genetic algorithms based heuristic is developed to solve the resulting problem. A number of numerical experiments showed that the heuristic performed well in solving large, real life instances. The heuristic provided a complete set of solutions that enable terminal operators to evaluate various berth scheduling policies and select the schedule that improves operations and customer satisfaction. The proposed algorithm outperformed a state of the art metaheuristic and provided improved results when compared to the weighted approach.     

A consolidated model of trip distribution

This work analyzes and compares various trip distribution models with spatial aggregation within a common theoretical framework for formulating and solving multi-objective optimization problems. A new model is designed that incorporates the main characteristics of existing ones. These models are then calibrated with a single database at different spatial aggregation levels using maximum likelihood. The results show that with aggregated data the various models differ little, but with disaggregated data the differences are considerable. It is also demonstrated that changing the level of data aggregation can significantly alter the models’ parameter values.     

Green supply chain management,reverse logistics and nuclear power generation

Built on the concepts of green supply chain management (G-SCM), this paper presents a multi-objective optimization programming approach to address the issue of nuclear power generation. In this study, a linear multi-objective optimization model is formulated to optimize the operations of both the nuclear power generation and the corresponding induced-waste reverse logistics. Factors such as the operational risks induced in both the power generation and reverse logistics processes are considered in the model formulation. Numerical results indicate that using the proposed approach, the induced environmental impact including the corresponding costs and risks can be improved up to 37.8%.     

Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions

This paper aims to examine the potential of multi-objective optimization (MOO) as a decision support to improving sustainability in maritime shipping. We focus on environmental sustainability and the trade-offs involved with economic and operational objectives. Through a systematic approach, we review the literature on environmental sustainability, decision support and multi-objective optimization in maritime shipping. We identify the gaps and directions for future research. It is expected that the next generation of decision support systems for maritime transport will exploit the theoretical development in MOO to facilitate informed decision making in maritime supply chains considering environmental sustainability and the competing objectives.     

基于满意度分析的高速铁路列车运行调整优化研究

为了研究高速铁路列车在恶劣天气影响下导致区段临时限速情景下的列车运行调整问题,文中选择总晚点时间和晚点列数2个指标,分别转化为列车延误满意度和正点率满意度,建立线性满意度、梯形满意度和传统多目标线性加权模型,通过改变列车到发时刻、运行径路、次序等实现高效、准点的列车运行调整。最后设计一个限速场景,运用软件求解,通过改变总晚点时间和晚点列数的权重,得到模型的帕累托解,结果表明满意度模型的帕累托解优于线性加权模型。此外,算例验证了梯形满意度线性加权模型从计算时间上优于传统多目标线性加权模型,提出的满意优化模型可以辅助调度员在发布限速命令后尽快获得满意的调度方案。     

Multi-objective optimization for planning liner shipping service with uncertain port times

This paper considers a joint tactical planning problem for the number of ships, the planned maximum sailing speed, and the liner service schedule in order to simultaneously optimize the expected cost, the service reliability and the shipping emission in the presence of port time uncertainty. The problem is formulated into a stochastic multi-objective optimization problem at the operational level. The relationships between the objectives and the decision variables are established. A simulation-based non-dominated sorting genetic algorithm is then presented to solve this problem. A case study is provided to illustrate the results and the application of the model.     

A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling

We propose an efficient evolutionary multi-objective optimization approach to the capacitated facility location–allocation problem (CFLP) for solving large instances that considers flexibility at the allocation level, where financial costs and CO₂ emissions are considered simultaneously. Our approach utilizes suitably adapted Lagrangian Relaxation models for dealing with costs and CO₂ emissions at the allocation level, within a multi-objective evolutionary framework at the location level. Thus our method assesses the robustness of each location solution with respect to our two objectives for customer allocation. We extend our exploration of selected solutions by considering a range of trade-offs for customer allocation.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space-time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.     

A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application

Growing importance of intermodal transportation necessitates modeling and solving load planning problems by taking into account various complex decisions simultaneously like transportation mode/service type selection, load allocation, and outsourcing. This paper presents a mixed-integer mathematical programming model for a multi-objective, multi-mode and multi-period sustainable load planning problem by considering import/export load flows to satisfy transport demands of customers and many other related issues. Several multiple objective optimization procedures are utilized in order to handle conflicting objectives simultaneously under crisp and fuzzy decision making environments. A real-life case study is also performed to present application and usefulness of the proposed model.     

Industry choice for an airport economic zone by multi-objective optimization

Airport Economic Zone (AEZ) is a suburban area whose infrastructure, land-use, and economy are centered on an airport. More than 100 AEZs are under planning and development in China. Industry choice is the critical issue for all the AEZs. To ensure that all the industries located in AEZs are closely related to airports and air transport, first, we create a pool of optional industries for AEZs based on the industrial input-output model. Second, we construct a multi-objective combinatorial optimization model subject to the constraints of land area of an AEZ, targeting the maximum number of employees, the largest GDP, the highest degree of aviation correlation, the fullest utilization of basic resources, and the greatest policy support. Third, to verify the feasibility of this model in practice, we choose the Zhengzhou Airport Economic Comprehensive Experimental Zone for case study. We divide these five objectives into two categories: economic objectives and non-economic objectives, and put different weights on them to describe different political preferences, and then discuss the calculation results in three scenarios. Finally, we come to the conclusion that comprehensive consideration of every characteristic of each industry can reasonably determine the scale of industrial land use and increase industrial economic benefit in AEZs. Our research provides a new quantitative and reliable method for industry choice and land use planning of an AEZ.     

Multiobjective optimization of asphalt pavement design and maintenance decisions based on sustainability principles and mechanistic-empirical pavement analysis

Highway pavement as an important component of transport infrastructure has significant impacts on economy, society, and environment. The management of highway pavement has been traditionally focused on economy. In this study, the impacts of management decisions are examined in three dimensions, including life-cycle cost (LCC), energy consumption, and greenhouse gas (GHG) emissions. Quantitative models to predict the three dimensions are developed from mechanistic-empirical pavement analysis results. Two decision variables, pavement thickness and threshold roughness level for pavement resurfacing, are found to be significant in affecting the three dimensions. These two variables are subsequently used as decision variables in multiobjective optimization. The ranges of decisions that result in minimum LCC, energy consumption, and GHG emissions are identified through multiobjective optimization. Although the analysis is illustrated in the context of pavement design and management in Hong Kong, the analysis techniques and procedures can be easily applied in other regions.     

Selecting sustainable electric bus powertrains using multipreference evolutionary algorithms

Constant improvement of vehicle technologies towards more efficient powertrains and reduced pollutant emissions, frequently leads to the increase of the vehicle or fuel costs, compromising its viability. Multi-objective optimization methods are commonly used to solve such problems, finding optimal trade-off solutions relatively conflicting objectives. Nevertheless, vehicle driving performance, is often disregarded from the optimization process or considered only as a fixed constraint. This may raise some issues, which are discussed in this paper: (a) vehicle dynamics are not improved, (b) trade-off optimal solutions are not distinguishable, (c) interesting solutions near constraints limits won´t be considered if constraints are not marginally relaxed.

This paper proposes a method to optimize three electric-drive vehicle options for an urban bus, a battery electric (BEV), a fuel cell hybrid (FC-HEV) and a plug-in hybrid (FC-PHEV), aiming minimum carbon footprint, maximum financial indicator and simultaneously improved driving performance (speed, acceleration, and electric range). The carbon footprint is assessed by a life cycle (LC) approach, considering the impact of the fuel production and use, and vehicle embodied materials; while the financial assessment considers the vehicle and fuel costs. The spherical pruning multi-objective differential evolution algorithm (spMODE-II) is used in the optimization, considering different preference regions within the problem constraints and objectives. The vehicle solutions optimality and suitability are compared with other multi-objective algorithm, NSGA-II.

The FC-HEV achieved the lowest LC emissions (547 g/km), and the FC-PHEV the maximum financial gain (0.19 $/km), while the BEV achieved the best trade-off of solutions.     

Affective-symbolic and instrumental-independence psychological motives mediating effects of socio-demographic variables on daily car use

An empirical study investigates the extent to which affective-symbolic and instrumental-independence psychological motives mediate effects of socio-demographic variables on daily car use in Sweden. Questionnaire data from a mail survey to 1134 car users collected in 2007 were used to assess the relationships daily car use as driver or passenger have to sex, household type (single or cohabiting with or without children), and residential area (urban, semi-rural or rural). Reliable measures of affective-symbolic and instrumental-independence motives were constructed. The results show that households with children use the car more than households with no children, that men make more car trips as drivers than women who use the car as passenger more than men, and that households living in rural areas use the car more than households living in semi-rural areas who use the car more than households living in urban areas. An affective-symbolic motive partially mediates the relationship between the number of weekly car trips and sex, the instrumental-independence motive partially mediates the relationships between weekly car use and percent car use as driver and several of the socio-demographic variables (living in urban vs. rural residential area for both measures; sex and living in urban vs. semi-rural residential area for percent car use as driver). Of several other socio-demographic variables (age, employment, and income) affecting car use, only the relationship of the number of cars to percent car use as driver was (partially) mediated by the instrumental-independence motive.     

Marrying supply chain sustainability and resilience: A match made in heaven

Sustainable supply chain management has become an integral part of corporate strategy for virtually every industry. However, little is understood about the broader impacts of sustainability practices on the capacity of the supply chain to tolerate disruptions. This article aims to explore the sustainability–resilience relationship at the supply chain design level. A multi-objective optimization model featuring a sustainability performance scoring method and a stochastic fuzzy goal programming approach is developed that can be used to perform a dynamic sustainability tradeoff analysis and design a “resiliently sustainable” supply chain. Important managerial and practical insights are obtained from an empirical case study.     

Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model

This paper presents a novel model for designing a reliable network of facilities in closed-loop supply chain under uncertainty. For this purpose, a bi-objective mathematical programming formulation is developed which minimizes the total costs and the expected transportation costs after failures of facilities of a logistics network. To solve the model, a new hybrid solution methodology is introduced by combining robust optimization approach, queuing theory and fuzzy multi-objective programming. Computational experiments are provided for a number of test problems using a realistic network instance.     

A multi-objective approach to scheduling joint participation with variable space and time preferences and opportunities

Activity scheduling supports activity-based analysis in travel demand management and promotes a potentially popular traveler assistance service. A multi-objective approach is proposed to schedule joint participation of multiple individuals, in which the candidate space-time opportunities for joint participation are identified by a concept of time-varying network-based prisms, and optimal opportunities for joint participation are determined by the non-dominated sorting genetic algorithm-II (NSGA-II) with four objectives (i) minimizing cost for congestion charges, (ii) maximizing participation desirability of time-of-day, (iii) minimizing total travel distance and (iv) time in the trips of multiple individuals. A scenario of joint participation among four people is designed and implemented to demonstrate the feasibility of this approach. The results suggest that this approach has the ability to schedule activities within real situations.     

Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model

This paper presents a novel model for designing a reliable network of facilities in closed-loop supply chain under uncertainty. For this purpose, a bi-objective mathematical programming formulation is developed which minimizes the total costs and the expected transportation costs after failures of facilities of a logistics network. To solve the model, a new hybrid solution methodology is introduced by combining robust optimization approach, queuing theory and fuzzy multi-objective programming. Computational experiments are provided for a number of test problems using a realistic network instance.     

Route optimization for energy efficient airport shuttle operations – A case study from Dallas Fort worth International Airport

The objective of this research is to reduce energy consumption from intra airport shuttle operations by optimizing routes and schedules, without compromising on passenger travel experience. To achieve this objective, we propose an optimization model that generates optimal airport shuttle routes for a given set of constraints and a discrete-event simulator that evaluates the optimal shuttle routes in a stochastic environment to understand the tradeoffs between the amount of time passengers wait for shuttles, and shuttle energy consumption. The proposed optimization model and stochastic simulation are tested using shuttle route data provided by the Dallas Fort Worth International Airport. Results indicate that optimized routes can lead to a 20% energy reduction in shuttle operations with a modest 2-min increase in average shuttle wait times. The optimization model and simulator presented here are designed to be generalizable and can be adapted to optimize shuttle operations at any major airport.