In sustainable project management, time and cost are two critical factors affecting the success of a project. Time/cost trade-offs in projects accelerate the execution of some activities by increasing the amount of non-renewable resources committed to them and therefore shorten the project duration. The discrete time/cost trade-off problem (DTCTP) has been extensively studied during the past 20 years. However, due to its complexity, the DTCTP—especially the DTCTP curve problem (DTCTP-C)—has only been solved for relatively small instances. To the best of our knowledge, there is no computational performance analysis for solving the DTCTP-C on large project instances with up to 500 activities. This paper aims to fill this gap. We present two bi-objective heuristic algorithms for the DTCTP-C where both project duration and cost are minimized. The objective is to obtain a good appropriate efficient set for the large-scale instances. The first algorithm is based on the non-dominated sorting genetic algorithm II (NSGA-II) and uses a specially designed critical path-based crossover operator. The second algorithm is a steepest descent heuristic which generates efficient solutions by iteratively solving the DTCTP with different deadlines. Computational experiments are conducted to validate the proposed algorithms on a large set of randomly generated problem instances.

The Free-Floating Bike-Sharing system (FFBSS) connects users to public transit networks and is an important component of the “last-mile” transport network. However, the rapid development of the FFBSS in China has significantly increased the local municipal workload and deteriorated the public transport. To mitigate these negative impacts, the Chinese government has launched a pilot project of electronic fence spots, by selecting several bike-sharing parking spots from the existing ones to set up the virtual stations. Compared to the traditional public shared bicycles with fixed stations, the flexibility of choosing parking spots could dynamically cater the fast-changing traffic environment and facilitate the renting and returning for users and, therefore, render a more sparse and complicate parking spots network. In this paper, we study the location selection of bike-sharing parking points as a multidimensional problem, which considers not only the interests of users and stakeholders but also the environment and safety issues. We propose a multicriteria decision-making (MCDM) model including the analytic hierarchy process (AHP) and the weight-restricted data envelopment analysis (DEA) method to evaluate and determine the optimal bike-sharing parking points. Since the additional weight restrictions in the DEA method might lead to infeasible solutions, we introduce the weight restrictions feasibility theorem to avoid such infeasibility in the proposed weight-restricted DEA model, which has not been thoroughly studied in the literature. Specifically, a hyperplane adjusting model is developed to adjust the infeasible results. In the computational study, we evaluate 36 current parking spots in three regions in Beijing, China, to verify the rationality of the combined approach and put forward some managerial suggestions for this pilot project in Beijing, China.

Time restriction and logistics sprawl (e.g., relocating logistics facilities), as two popular urban policies, usually affect the urban freight and environmental burden, but their combination might lead to unexpected results. This paper analyzes the impact of time restriction and logistics sprawl on urban freight and local environments based on a Beijing agricultural freight case through traffic simulation. The data is derived through a freight demand forecasting method. Based on the data, this paper constructs four groups of scenarios to represent different policies (or combined policies) and then conducts macro-simulation to obtain the economic and environmental indicators. Results show that (1) time restriction can increase the freight costs and slightly decrease local emissions, while logistics sprawl can increase both costs and emissions; (2) the joint implementation of the two policies are proved to be positive in economic and environmental aspects because it helps freight carriers adopt a new strategy to improve delivery efficiency; (3) urban freight policies are closely related to the freight carriers because different responses from carriers can lead to different policy effects.

High-story residential structures and off-site prefabrication have been dominant choices in the construction industry. There is a substantial quantity of greenhouse gas (GHG) emissions produced by the construction industry. In fact, the construction industry is responsible for 30 percent of all GHG emissions. In this study, we analyse the differences between the conventional technique of building and the off-site prefabricating construction method. First, we evaluate the emissions emitted from key processes during the off-site prefabricating construction. In addition, we analyse the qualitative and quantitative differences between two prefabrication structural systems, namely concrete and steel, which are the two most common structural systems utilised in residential construction projects in China. We examine and analyse four different case studies in order to exemplify the proposed methodology and offer managerial insights.

The classical multi-level lot-sizing and scheduling problem formulations for process industries rarely address perishability issues, such as limited shelf lives of intermediate products. In some industries, ignoring this specificity may result in severe losses. In this paper, we start by extending a classical multi-level lot-sizing and scheduling problem formulation (MLGLSP) to incorporate perishability issues. We further demonstrate that with the objective of minimising the total costs (purchasing, inventory and setup), the production plans generated by classical models are often infeasible under a setting with perishable products. The model distinguishes different perishability characteristics of raw materials, intermediates and end products according to various industries. Finally, we provide quantitative insights on the importance of considering perishability for different production settings when solving integrated production planning and scheduling problems.