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More and more work zone projects come with the needs of new construction and regular maintenance-related investments in transportation. Work zone projects can have many significant impacts socially, economically and environmentally. Minimizing the total impacts of work zone projects by optimizing relevant schedules is extremely important. This study aims to analyze the impacts of scheduling long-term work zone activities.

Optimal scheduling of the starting dates of each work zone project is determined by developing and solving using a bi-level genetic algorithm (GA)–based optimization model. The upper level sub-model is to minimize the total travel delay caused by work zone projects over the entire planning horizon, whereas the lower level sub-model is a traffic assignment problem under user equilibrium condition with elastic demand.

Sioux Falls network is used to develop and test the proposed GA-based model. The average and minimum total travel delays (TTDs) over generations of the proposed GA algorithm decrease very rapidly during the first 20 generations of the GA algorithm; after the 20th generations, the solutions gradually level off with a certain level of variations in the average TTD, showing the capability of the proposed method of solving the multiple work zone starting date optimization problem.

The proposed model can effectively identify the near-optimal solution to the long-term work zone scheduling problem with elastic demand. Sensitivity analysis of the impact of the elastic demand parameter is also conducted to show the importance of considering the impact of elastic demand parameter.

This paper aims to analyze and provide insight on the algorithms for the optimization of construction site layout planning (CSLP). It resolves problems, such as the selection of suitable algorithms, considering the optimality, optimization objectives and representation of layout solutions. The approaches for the better utilization of optimization algorithms are also presented.

To achieve the above, existing records (results = 200) were selected from three databases: Web of Science, ScienceDirect and Scopus. By implementing a systematic protocol, the articles related to the optimization algorithms for the CLSP (results = 75) were identified. Moreover, various related themes were collated and analyzed according to a coding structure.

The results indicate the consistent and increasing interest on the optimization algorithms for the CLSP, revealing that the trend in shifting to smart approaches in the construction industry is significant. Moreover, the interest in metaheuristic algorithms is dominant because 65.3% of the selected articles focus on these algorithms. The optimality, optimization objectives and solution representations are also important in algorithm selection. With the employment of other algorithms, self-developed applications and commercial software, optimization algorithms can be better utilized for solving CSLP problems. The findings also identify the gaps and directions for future research.

The selection of articles in this review does not consider the industrial perspective and practical applications of commercial software. Further comparative analyses of major algorithms are necessary because this review only focuses on algorithm types.

This paper presents a comprehensive systematic review of articles published in the recent decade. It significantly contributes to the demonstration of the status and selection of CLSP algorithms and the benefit of using these algorithms. It also identifies the research gaps in knowledge and reveals potential improvements for future research.

This paper deals with two problems in transport network planning and control: trip matrix estimation and traffic signal optimisation. These two problems have both been formulated as bi-level programming problems with the User Equilibrium assignment as the second-level programming problem. One currently used method for solving the two problems consists of alternate optimisation of the two sub-problems until mutually consistent solutions are found. However, this alternate procedure does not converge to the solution of the bi-level programming problem. In this paper, a new algorithm will be developed and will be applied to two road networks.

The purpose of this paper is to study a transportation service procurement bid construction problem from a less than a full truckload perspective. It seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which could improve the likelihood that carrier can earn its maximum utility.

The authors proposes a bi-level programming that integrates the bid selection and winner determination and a discrete particle swarm optimization (PSO) solution algorithm is then developed, and a numerical simulation is used to make model and algorithm analysis.

The algorithm comparison shows that although GA could find a little more Pareto solutions than PSO, it takes a longer time and the quality of these solutions is not dominant. The model analysis shows that compared with traditional approach, our model could promote the likelihood of winning bids and the decision effectiveness of the whole system because it considers the reaction of the shipper.

The highlights of this paper are considering the likelihood of winning the business and describing the conflicting and cooperative relationship between the carrier and the shipper by using a stochastic mixed integer programming, which has been rarely examined in previous research.

The purpose of this paper is to propose a bi‐level programming optimization model to reduce traffic congestion of transportation network while evacuating people to safe shelters during disasters or special events.

The previous optimization model for contra flow configuration only considered the character of the manager. However, the traffic condition is not only controlled by managers, but also depended on the root choice of travelers. A bi‐level programming optimization model, which considered managers and evacuees' character, is proposed to optimize the contra flow of transportation network in evacuation during special events. The upper level model aims to minimize the total evacuation time, while the lower level based on user equilibrium assignment. A solution method based on discrete particle swarm optimization and Frank‐Wolfe algorithm is employed to solve the bi‐level programming problem.

It is found that the bi‐level programming based contra flow optimization model can improve evacuation efficiency and decrease evacuation time 30 per cent or more. With the increase of traffic demand, the evacuation time will decrease significantly by contra flow configuration.

In the optimization model, the background traffic is ignored for simplification and the contra flow is configured absolutely as 0 or 1, which ensures vehicles do not go back into the evacuation area.

An efficient optimization model for traffic managers to reduce congestion and evacuation time of evacuation network.

The new bi‐level programming model not only considers managers' character, but also considers evacuees' reaction. The paper is aimed to optimize contra flow for transportation network.

The facility in an emergency system could be immobilized because of the huge destructive power of an irregular emergency and the uncertainty of the time, place and scale of occurrence. So facility failure scenarios must be considered at the time of location. The purpose of this paper is to establish a location model based on the worst facility failure, the objective of which is to minimize the cost and cover the demand maximally. It is demonstrated that location choice, considering facility failure, has significant meaning when considering economic benefit and covering the demand.

A bi‐level programming model which studies the facility location is established by using the methods of scenario analysis and robust optimization. It is compared with a classic location model, without considering facility failure, from the points of view of economic benefit and maximal covering demand.

Compared to the classic location model, without considering facility failure, it is demonstrated that the location model which considers facility failure can save more costs from the economic benefit point of view and, from the maximal covering of the demand point of view, has a higher covering ratio. So facility failure scenario should be considered in the location of an emergency facility.

The paper studies facility location based on the worst scenario, from the two aspects of economic benefits and maximal covering demand.

This paper aims to develop a grey decentralized bi-level multi-objective programming (MOP) model. A solution approach is also proposed for the given model. A production and transportation plan for a closed loop supply chain network under an uncertain environment and different scenarios is also developed.

In this paper, we combined grey linear programming (GLP) and fuzzy set theory to present a solution approach for the problem. The proposed model first solves the given problem using GLP. Membership functions for the decision variables under the control of the leader and for the goals are created. These membership functions are then used to generate the final solutions.

This paper provides insight for fomenting the decision-making process while providing a more flexible approach in uncertain logistics problems. The deviations of the final solution from the individual best solutions of the two levels are very little. These deviations can further be reduced by adjusting the tolerances associated with the decision variables under the control of the leader.

The proposed approach uses the concept of membership functions of linear form, and thus, requires less computational efforts while providing effective results. Most of the organizations exhibit decentralized decision-making under the presence of uncertainties. Therefore, the present study is helpful in dealing with such scenarios.

This is the first time, formulation of a decentralized bi-level multi-objective model under a grey environment is carried out as per the best knowledge of the authors. A solution approach is developed for bi-level MOP under grey uncertainty.

The purpose of this paper is to report the design of a lightweight tree-shaped support structure for fused deposition modeling (FDM) three-dimensional (3D) printed models when the printing path is considered as a constraint.

A hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) is proposed to address the topology optimization of the tree-shaped support structures, where GA optimizes the topologies of the trees and PSO optimizes the geometry of a fixed tree-topology. Creatively, this study transforms each tree into an approximate binary tree such that GA can be applied to evolve its topology efficiently. Unlike FEM-based methods, the growth of tree branches is based on a large set of FDM 3D printing experiments.

The hybrid of GA and PSO is effective in reducing the volume of the tree supports. It is shown that the results of the proposed method lead to up to 46.71% material savings in comparison with the state-of-the-art approaches.

The proposed approach requires a large number of printing experiments to determine the function of the yield length of a branch in terms of a set of critical parameters. For brevity, one can print a small set of tree branches (e.g. 30) on a single platform and evaluate the function, which can be used all the time after that. The steps of GA for topology optimization and those of PSO for geometry optimization are presented in detail.

The proposed approach is useful for the designers and manufacturers to save materials and printing time in fabricating complex models using the FDM technique. It can be adapted to the design of support structures for other additive manufacturing techniques such as Stereolithography and selective laser melting.