Brian W. Baetz

A GREY LINEAR PROGRAMMING APPROACH FOR MUNICIPAL SOLID WASTE MANAGEMENT PLANNING UNDER UNCERTAINTY

Abstract In optimization analysis by linear programming, uncertainties may exist in model coefficients and stipulations (right-hand side constraints). These uncertainties can propagate through the analysis and generate uncertainties in the results. However, among the previous methods dealing with uncertainty, some were too complicated to be applied to actual problems, and some were unable to reflect completely the uncertainties of the input and output information. In this paper, a grey linear programming (GLP) model is introduced to the civil engineering area. This method allows uncertainties in the model inputs to be communicated into the optimization process, and thereby solutions reflecting the inherent uncertainties can be derived. A grey linear programming problem can be solved easily by running a simplex program several times. The modelling approach is applied to a hypothetical problem of waste flow allocation planning within a municipal solid waste management system. The results indicate that reaso...

Grey integer programming: An application to waste management planning under uncertainty

Abstract This paper introduces a grey integer programming (GIP) method for facility expansion planning under uncertainty, by incorporating the concepts of grey number and grey mathematical programming into a mixed integer linear programming optimization framework. The approach is an improvement upon previous integer programming methods in terms of its technical characteristics and applicability. It allows uncertain information to be effectively communicated into the optimization process and the resulting solutions. It also has low computational requirements and is thus applicable to practical problems. The modelling approach is applied to a hypothetical planning problem of waste flow allocation and treatment/disposal facility expansion within a regional solid waste management system. The binary variable solutions provide the ranges of different development alternatives within a multi-period, multi-facility and multi-scale context, and the continuous variable solutions provide optimal schemes for waste flow allocation corresponding to the upper and lower bounds of the objective function value. The results indicate that reasonable and useful solutions can be achieved through the developed approach.

Grey fuzzy integer programming: An application to regional waste management planning under uncertainty

Abstract This paper introduces a grey fuzzy integer programming (GFIP) method and its application to regional solid waste management planning under uncertainty. The GFIP improves upon the existing integer programming methods by incorporating both grey fuzzy linear programming (GFLP) and grey integer programming (GIP) approaches within a general optimization framework. The approach allows uncertainty in both model coefficients and stipulations to be effectively communicated into the optimization process and resulting solutions, such that feasible decision alternatives can be generated through appropriate interpretation of the solutions. Moreover, the GFIP does not lead to more complicated intermediate models in its solution process, thus offering lower computational requirements than existing methods. In addition, it is applicable to practical problems. The modelling approach is applied to a hypothetical planning problem of waste management facility expansion/utilization planning within a regional solid waste (RSW) management system. The results indicate that reasonable solutions were generated for both binary and continuous variables. The binary variable solutions represent the related grey decisions of waste management facility expansion within a multi-period, multi-facility and multi-scale context. Further, they have been interpreted to provide decision alternatives that reflect the effects of uncertainties. The continuous variable solutions relate to grey decisions for waste flow allocation corresponding to the suggested facility expansions.

GREY QUADRATIC PROGRAMMING AND ITS APPLICATION TO MUNICIPAL SOLID WASTE MANAGEMENT PLANNING UNDER UNCERTAINTY

Abstract This paper introduces a grey quadratic programming (GQP) method asa means for decision making under uncertainty. The method improves upon existing grey linear programming (GLP) methods by allowing the consideration of the effects of economies of scale on cost coefficients in the objective function. The approach also has advantages over a grey nonlinear programming method, since a global optimum is obtainable and the model is moderately easy to solve through commercially available quadratic programming packages. The modelling approach is applied to a hypothetical problem of waste flow allocation within a municipal solid waste management system. The results indicate that, compared with the GLP method, GQP provides a more effective means for reflecting system cost variations and may therefore generate more realistic and applicable solutions.

Lysimeter washing of MSW incinerator bottom ash

Stockpiled municipal waste incinerator bottom ash is frequently considered for utilization as a construction material. Two 360 kg lysimeter experiments were conducted to study percolation washing of contaminants from stockpiled MSW bottom ash. One lysimeter was leached with a concentrated sodium hydroxide solution, as a possible pre-treatment for improvement of the bottom ash characteristics prior to utilization, while the other was leached using distilled water. The lysimeter leachate was analysed, and at the end of the 2-year leaching period, the bottom ash from each lysimeter was subjected to several laboratory tests to assess the effect of the treatments. The laboratory tests showed that distilled water leachability of both treated ashes was an order of magnitude lower than that of fresh ash, but long-term contaminant leachability under acidic conditions had not changed. Although alkaline washing clearly resulted in greater contaminant removal than did distilled water washing, the chemical properties of the alkaline-leached bottom ash were not significantly different from those of the water-leached ash.

Trash-Flow Allocation: Planning Under Uncertainty

The materials in trash, or municipal solid waste (MSW), may be recycled, composted, incinerated, or landfilled. Transporting these materials to waste-management facilities consumes part of the operating budget of a municipality. Many factors, the majority uncertain, may influence the planning of waste-flow allocation. MSW managers want to know (1) whether the existing pattern is close to the optimal, and (2) if not, what is the optimal? We used a gray linear-programming (GLP) model to address these questions for the regional municipality of Hamilton-Wentworth, Ontario. This approach can effectively reflect the interactive relationships between uncertain system components. It can also bring uncertain information into the optimization process and resulting solutions, permitting feasible decision alternatives to be generated. The regions waste managers have used the results to verify or adjust the existing waste-flow allocation pattern. They may also be used to formulate related local policies.

GREY FUZZY DYNAMIC PROGRAMMING: APPLICATION TO MUNICIPAL SOLID WASTE MANAGEMENT PLANNING PROBLEMS

Abstract This paper integrates the concepts of grey systems and fuzzy sets into optimization analysis by dynamic programming as a means of accounting for system uncertainty. The developed grey fuzzy dynamic programming (GFDP) model improves upon previous DP methods by allowing uncertain input information to be directly communicated into the optimization process and solutions through the use of different a-cut levels of fuzzy numbers for the input fuzzy information, and the use of a grey fuzzy linear programming (GFLP) method for an embedded LP problem. The modelling approach is applied to a hypothetical problem for the planning of waste flow allocation and treatment/disposal facility expansion within a municipal solid waste management system. The solutions of the GFDP model corresponding to different a-cut levels provide optimal decisions regarding different development alternatives in a multi-period, multi-facility and multi-scale context, as well as the upper and lower limits of waste flow allocation. T...

Capacity planning for waste management systems

Abstract The capacity planning problem examined in this paper has a number of applications in the environmental management area including municipal solid waste management, hazardous waste treatment and disposal, and municipal wastewater sludge management. A dynamic programming formulation is presented for the specific capacity planning problem, and considerations of problem structure that allow computational efficiencies are discussed. This long-term planning model is applied to hypothetical and case-study problems from the municipal solid waste management area. The problem is generalized and compared to other problem types in the capacity planning literature.

WASTE FLOW ALLOCATION PLANNING THROUGH A GREY FUZZY QUADRATIC PROGRAMMING APPROACH

Abstract This paper proposes a grey fuzzy quadratic programming (GFQP) approach as a means for optimization analysis under uncertainty. The method combines the ideas of grey fuzzy linear programming (GFLP) and fuzzy quadratic programming (FQP) within a general optimization famework. It improves upon the previous GFLP method by using n grey control variables, ® (A,j (i = 1,2,..., n), for n constraints instead of one ® (X) for n constraints in order to incorporate the independent properties of the stipulation uncertainties; it also improves upon the FQP method by further introducing grey numbers for coefficients in A and C to effectively reflect the lefthand side uncertainties. Compared with the GFLP method, the GFQP approach is helpful for better satisfying model objective/constraints and providing grey solutions with higher system certainty and lower system cost; compared with the FQP method, more information of the independent uncertain features of not only the stipulations but also the lefthand side coe...

A GIS-based Decision Support System for Neighbourhood Greening

A prototype decision support tool is described which provides options for the management of existing green spaces and for the establishment of new green space in suburban neighbourhoods. Suggested neighbourhood greening techniques include the naturalization of existing parks and increased foliage along streets and rights of way. The naturalization approach involves less frequent cutting in grassy fields, the introduction of native species, and the cessation of pesticide and herbicide applications. Increased plantings along streets and boulevards would improve the aesthetics of neighbourhoods, and may provide some relief from climatic extremes and urban heat island effects. The creation of new green space in already-built suburban neighbourhoods provides a longer term challenge to neighbourhood planners. Potential strategies include the introduction of small pocket parks and community gardens in vacant lots and school yards. Modelled outcomes from such neighbourhood greening strategies could be used in public meetings both to incorporate attitudes of the impacted community and to demonstrate benefits to a wider community. In particular, strategies should take into account issues of safety and perceived safety that commonly arise with the greater use of naturalization in green space management. The developed prototype decision support tool has been coded as an ArcView GIS extension and provides the opportunity to model and evaluate future scenarios better aligned to principles of sustainable community development. Three applications of this tool are discussed to illustrate some of the benefits of undertaking a range of neighbourhood greening strategies.