H. Zhu

A two-stage programming approach for water resources management under randomness and fuzziness

In this study, a fuzzy stochastic two-stage programming (FSTP) approach is developed for water resources management under uncertainty. The concept of fuzzy random variable expressed as parameters uncertainties with both stochastic and fuzzy characteristics was used in the method. FSTP has advantages in uncertainty reflection and policy analysis. FSTP integrates the fuzzy robust programming, chance-constrained programming and two-stage stochastic programming (TSP) within a general optimization framework. FSTP can incorporate pre-regulated water resources management policies directly into its optimization process. Thus, various policy scenarios with different economic penalties (when the promised amounts are not delivered) can be analyzed. FSTP is applied to a water resources management system with three users. The results indicate that reasonable solutions were generated, thus a number of decision alternatives can be generated under different levels of stream flows, @a-cut levels and different levels of constraint-violation probability. The developed FSTP was also compared with TSP to exhibit its advantages in dealing with multiple forms of uncertainties.

Two-Stage Chance-Constrained Fractional Programming for Sustainable Water Quality Management under Uncertainty

AbstractIn this study, a two-stage chance-constrained fractional programming (TCFP) method is developed for dealing with water quality management problems associated with stochastic inputs. Two-stage chance-constrained fractional programming is a hybrid of stochastic linear fractional programming (SLFP) and two-stage stochastic programming (TSP) methods. It can not only balance objectives of two aspects through converting a bi-objective problem into a ratio one but can also analyze various policy scenarios when the promised production targets are violated. For demonstrating its advantages, the proposed TCFP method is applied to a case study of water quality management where managers have to consider conflicting objectives between economic development and environmental conservation, as well as stochastic features expressed as probability distributions. The obtained solutions under different significance levels can help managers to identify desired policies under various environmental, economic, and constra...

SIFNP: Simulation-Based Interval-Fuzzy Nonlinear Programming for Seasonal Planning of Stream Water Quality Management

A simulation-based interval-fuzzy nonlinear programming (SIFNP) approach was developed for seasonal planning of stream water quality management. The techniques of inexact modeling, nonlinear programming, and interval-fuzzy optimization were incorporated within a general framework. Based on a multi-segment stream water quality simulation model, dynamic waste assimilative capacity of a river system within a multi-season context was considered in the optimization process. The method could not only address complexities of various system uncertainties but also tackle nonlinear environmental–economic interrelationships in water quality management problems. In addition, interval-fuzzy numbers were introduced to reflect the dual uncertainties, i.e., imprecision associated with fixing the lower and upper bounds of membership functions. The proposed method was applied to a case of water quality management in the Guoyang section of the Guo River in China. Interval solutions reflecting the inherent uncertainties were generated, and a spectrum of cost-effective schemes for seasonal water quality management could thus be obtained by adjusting different combinations of the decision variables within their solution intervals. The results indicated that SIFNP could effectively communicate dual uncertainties into the optimization process and help decision makers to identify desired options under various complexities of system components.

An inexact inventory-theory-based chance-constrained programming model for solid waste management

In this study, an inexact inventory-theory-based chance-constrained programming (IICP) model is proposed for planning waste management systems. The IICP model is derived through introducing inventory theory model into a general inexact chance-constrained programming framework. It can not only tackle uncertainties presented as both probability distributions and discrete intervals, but also reflect the influence of inventory problem in decision-making problems. The developed method is applied to a case study of long-term municipal solid waste (MSW) management planning. Solutions of total waste allocation, waste allocation batch and waste transferring period associated different risk levels of constraint violation are obtained. The results can be used to identify inventory-based MSW management planning with minimum system cost under various constraint-violation risks. Compared with the ICP model, the developed IICP model can more actually reflect the complexity of MSW management systems and provide more useful information for decision makers.

Inexact Inventory Theory–Based Waste Management Planning Model for the City of Xiamen, China

AbstractIn this study, an inexact inventory-theory-based waste management planning (IIWMP) model was developed and applied to support long-term planning of the municipal solid waste (MSW) management system in the City of Xiamen, the special economic zone of Fujian, China. In the IIWMP model, the techniques of inventory model, inexact chance-constrained programming, interval-valued fuzzy linear-programming, and mixed-integer linear programming were integrated. The waste inventory problem that existed in Xiamen’s MSW management systems are addressed in association with the complexities of multiple uncertainties. Decision alternatives for waste allocation and capacity expansion with minimized system cost under different risk levels were provided for MSW management in the City of Xiamen. The results indicate that the developed model was useful for identifying desired waste management policies under various uncertainties.

Two-Stage Fractional Programming Method for Managing Multiobjective Waste Management Systems

AbstractIn this study, a two-stage fractional programming (TSFP) method is developed for supporting municipal solid waste (MSW) management under uncertainty. The model can not only balance two conflicting objectives through converting a multiobjective problem into a ratio one, but also can analyze multistage decision effects when promised policy targets are violated. Moreover, the TSFP model can facilitate dynamic analysis of capacity expansions for waste management facilities. The developed method is applied to a case study of long-term MSW management planning. The solutions obtained from TSFP can provide desired waste-allocation schemes and capacity-expansion plans under different policy scenarios. The results allow in-depth analyses in terms of conflicting objectives, policy scenarios, and capacity expansions.

Municipal solid waste management planning for Xiamen City, China: a stochastic fractional inventory-theory-based approach

In this study, a stochastic fractional inventory-theory-based waste management planning (SFIWP) model was developed and applied for supporting long-term planning of the municipal solid waste (MSW) management in Xiamen City, the special economic zone of Fujian Province, China. In the SFIWP model, the techniques of inventory model, stochastic linear fractional programming, and mixed-integer linear programming were integrated in a framework. Issues of waste inventory in MSW management system were solved, and the system efficiency was maximized through considering maximum net-diverted wastes under various constraint-violation risks. Decision alternatives for waste allocation and capacity expansion were also provided for MSW management planning in Xiamen. The obtained results showed that about 4.24xa0×xa0106xa0t of waste would be diverted from landfills when pi is 0.01, which accounted for 93% of waste in Xiamen City, and the waste diversion per unit of cost would be 26.327xa0×xa0103xa0t per

Interval-parameter Two-stage Stochastic Semi-infinite Programming: Application to Water Resources Management under Uncertainty

106. The capacities of MSW management facilities including incinerators, composting facility, and landfills would be expanded due to increasing waste generation rate.