SLFP: A stochastic linear fractional programming approach for sustainable waste management

• Published in: Waste management (Elmsford) Vol. 31; no. 12; pp. 2612 - 2619
• Main Authors: Zhu, H., Huang, G.H.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-12-2011; Elsevier
• Subjects: Algorithms; ALLOCATIONS; Applied sciences; Chance-constrained programming; Cities; COMPARATIVE EVALUATIONS; COST; Dealing; Decision Support Techniques; EFFICIENCY; Environment; EVALUATION; Exact sciences and technology; General treatment and storage processes; HAZARDS; Linear fractional programming; MANAGEMENT; MANAGEMENT OF RADIOACTIVE WASTES, AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES; OPTIMIZATION; Other wastes and particular components of wastes; Pollution; PROBABILITY; PROGRAMMING; RANDOMNESS; Refuse Disposal - methods; RELIABILITY; Risk; Solid waste management; SOLID WASTES; Stochastic; STOCHASTIC PROCESSES; Stochasticity; Sustainability; Uncertainty; Urban and domestic wastes; WASTE MANAGEMENT; Waste Management - economics; Waste Management - methods; WASTES; Environment; Chance-constrained programming; Stochastic; Solid waste management; Sustainability; Linear fractional programming; Waste management; Uncertainty; Urban waste; Solid waste; Probability distribution; Reliability; Optimization
• ISSN: 0956-053X; 1879-2456
• DOI: 10.1016/j.wasman.2011.08.009

► A new fractional programming (SLFP) method is developed for waste management. ► SLFP can solve ratio optimization problems associated with random inputs. ► A case study of waste flow allocation demonstrates its applicability. ► SLFP helps compare objectives of two aspects and reflect system efficiency. ► This study supports in-depth analysis of tradeoffs among multiple system criteria.
A stochastic linear fractional programming (SLFP) approach is developed for supporting sustainable municipal solid waste management under uncertainty. The SLFP method can solve ratio optimization problems associated with random information, where chance-constrained programming is integrated into a linear fractional programming framework. It has advantages in: (1) comparing objectives of two aspects, (2) reflecting system efficiency, (3) dealing with uncertainty expressed as probability distributions, and (4) providing optimal-ratio solutions under different system-reliability conditions. The method is applied to a case study of waste flow allocation within a municipal solid waste (MSW) management system. The obtained solutions are useful for identifying sustainable MSW management schemes with maximized system efficiency under various constraint-violation risks. The results indicate that SLFP can support in-depth analysis of the interrelationships among system efficiency, system cost and system-failure risk.