Separating recyclables from municipal solid waste (MSW) before collection reduces not only the quantity of MSW that needs to be treated but also the depletion of resources. However, the participation of residents is essential for a successful recycling program, and the level of participation usually depends on the degree of convenience associated with accessing recycling collection points. The residential accessing convenience (RAC) of a collection plan is determined by the proximity of its collection points to all residents and its temporal flexibility in response to resident requirements. The degree of proximity to all residents is determined by using a coverage radius that represents the maximum distance residents need to travel to access a recycling point. The temporal flexibility is assessed by the availability of proximal recycling points at times suitable to the lifestyles of all residents concerned. In Taiwan, the MSW collection is implemented at fixed locations and at fixed times. Residents must deposit their garbage directly into the collection vehicle. To facilitate the assignment of collection vehicles and to encourage residents to thoroughly separate their recyclables, in Taiwan MSW and recyclable materials are usually collected at the same time by different vehicles. A heuristic procedure including an integer programming (IP) model and ant colony optimization (ACO) is explored in this study to determine an efficient two-shift collection plan that takes into account RAC factors. The IP model has been developed to determine convenient collection points in each shift on the basis of proximity, and then the ACO algorithm is applied to determine the most effective routing plan of each shift. With the use of a case study involving a city in Taiwan, this study has demonstrated that collection plans generated using the above procedure are superior to current collection plans on the basis of proximity and total collection distance.

Recycling municipal solid waste has become a challenging task for municipalities. Appropriate recycling efficiency evaluations are, thus, essential to find practical benchmark learning targets for inefficient municipal solid waste authorities (MSWAs). This study developed a recycling performance evaluation procedure by subgrouping MSWAs with prominent local demographic features, such as population density, ratio of senior citizens, tourism index etc. Principal recycling relevant factors for MSWAs in each group were then collected, and data envelopment analysis (DEA) was applied for efficiency evaluation and benchmark learning targets. A case study of 181 MSWAs in Taiwan demonstrated the suitability of the proposed procedures. An assessment of the required efforts for efficiency improvements revealed that, in an unsegregated scenario, inefficient MSWAs representing a rural subgroup required maximum efforts to fulfill the efficiency targets, which was on average 61% higher than that determined in their respective subgroup. Furthermore, the unsegregated scenario revealed proximal efficiency results for the urban subgroup. The results indicated that consideration of local demographic features was essential for a fair assessment of recycling efficiency. Additionally, evaluating MSWAs with similar local demographic features was superior in obtaining appropriate benchmark learning targets for the inefficient MSWAs and, consequently, exhibited practicality for improving walkthroughs to achieve the efficiency goal.