A bi-level multi-objective location-routing model for municipal waste management with obnoxious effects.

Municipal waste management is a complex problem. This paper develops a bi-level multi-objective location-routing model for municipal waste management that considers the interests of both the government and the sanitation companies. The government as the leader decides on the location and scale of the waste recycling centers to reduce the obnoxious effects and ensure cost effectiveness, and the sanitation company as the follower decides on the waste collection routing plans based on the government-approved locations to minimize the logistics cost. An improved hybrid NSGA-II is then developed to solve the proposed model. Two initial solution methods are employed: clustering for the leader and a Clarke and Wright method for the follower. Non-dominated sorting and best-cost route crossover operator are used to improve the effectiveness of NSGA-II. Based on Prins (24 instances) and Barreto (13 instances) benchmarks, the experimental results indicated that the improved operator had strong competitiveness and a better performance than previous methods, with the improved algorithm achieving the best average gaps of 0.18% and 0.24% and improving the best-known solutions in some instances. The model and solution methodology are illustrated using a waste collection problem in Tianjin, from which practical insights are derived.

Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application to a large scale hydropower construction project.

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method.