This paper focuses on solving a problem of green location-routing with cold chain logistics (GLRPCCL). Considering the sustainable effects of the economy, environment, society, and cargos, we try to establish a multi-objective model to minimize the total cost, the full set of greenhouse gas (GHG) emissions, the average waiting time, and the total quality degradation. Several practical demands were considered: heterogeneous fleet (HF), time windows (TW), simultaneous pickup and delivery (SPD), and a feature of mixed transportation. To search the optimal Pareto front of such a nondeterministic polynomial hard problem, we proposed an optimization framework that combines three multi-objective evolutionary algorithms (MOEAs) and also developed two search mechanisms for a large composite neighborhood described by 16 operators. Extensive analysis was conducted to empirically assess the impacts of several problem parameters (i.e., distribution strategy, fleet composition, and depots’ time windows and costs) on Pareto solutions in terms of the performance indicators. Based on the experimental results, this provides several managerial insights for the sustainale logistics companies.

Purpose The construction industry is characterized by a long construction period, high cost and many uncontrollable factors. The owners and contractors are increasingly focusing on the efficiency of their construction and costs in pursuit of greater economic benefits. However, current methods used in the construction period and cost optimization analysis with multiple constraints the have their own limitations. Therefore, this study aims to propose a combination of genetic algorithm (GA) and building information modeling (BIM) to construct a five-dimensional construction duration-cost optimization model with the advantages of optimization and simulation for optimization. Design/methodology/approach This design first analyzed the characteristics of changing construction period and cost and then improved the genetic mechanism and the data processing method in the GA according to the aforementioned characteristics. Then, BIM technology was combined with GA to testify the feasibility of the model in the practical engineering project. Findings The result proved that this new method was reasonable and effective in dealing with the complicated problem of period and cost. GA accelerated the optimization process and yielded a reliable Pareto solution. BIM technology simulated the construction process before construction to increase the feasibility of the construction scheme. Originality/value This method not only can rapidly provide the best construction period/cost decision to the architect according to the previous working period/cost or contract data that can meet the demands of the architect but also visualize the construction and give a dynamic schedule of the project.

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