An inexact left-hand-side chance-constrained programming (ILCCP) was proposed and applied to a nonpoint-source water quality management problem within an agricultural system. The ILCCP model can reflect uncertainties presented as interval parameters (manure mass balance, crop nutrient balances, energy and digestible protein requirements, pollutant losses, water quantity constraints, technical constraints, and so on) and left-hand-side random variables (nitrogen requirement of crop i) at the same time. A non-equivalent linearization form of ILCCP was deduced and proved intuitively, which can help handle the left-hand-side random parameters in the constraints. The decision schemes through ILCCP were analyzed under scenarios at different individual probabilities (p ᵢ , denotes the admissible probability of violating the constraint i). The performance of ILCCP was also compared with the corresponding interval linear programming model. A representative nonpoint-source water quality management case was employed to facilitate the analysis and the comparison. The optimization results indicated that the net system benefit in the water quality management case would decrease with increasing probability levels on the whole. This was because that the higher constraint satisfaction of probability would lead to stricter decision space. The optimal scheme shows an obvious downtrend in the application amount of manure as the violation probability levels decreasing from scenarios 1 to 3 (p ᵢ = 0.1, 0.05 and 0.01). This demonstrates that the application amount of manure would be reduced effectively by adjusting strictness of the constraints. This study is the first application of the ILCCP model to water quality management, which indicates that the ILCCP is applicable to other environmental problems under uncertainties.

In the agricultural Altai Krai in Russian Siberia, soil degradation problems are prevalent. Agronomists recommend “reduced tillage systems,” especially no-till, as a sustainable way to cultivate land that is threatened by soil degradation. In the Altai Krai, less is known about the technologies in practice. In this paper, we provide information on plant cultivation technologies used in the Altai Krai and on selected factors preventing farm managers in this region from adopting no-till technology based on our own quantitative survey conducted across 107 farms in 2015 and 2016. The results of the quantitative survey show that farm managers have high uncertainty regarding the use of no-till technology including its economics. To close this gap, we provide systematic analysis of factors influencing the economy of the plant production systems by using a farm optimization model (linear programming) for a real farm, together with expert estimations. The farm-specific results of the optimization model show that under optimal management and climatic conditions, the expert Modern Canadian no-till technology outperforms the farm min-till technology, but this is not the case for suboptimal conditions with lower yields.

An adsorption-enhanced soil aquifer treatment (SAT) system was designed to reduce the level of estrogens below the threshold stipulated by the standards. The 17β-E2 adsorption by graphene and MARs (H103) was investigated and an optimum amount of graphene and MARs in the mixture was determined using the linear programming. The kinetics and isotherm characteristics of both adsorbents were well described by the Lagergren pseudo-second order and the Freundlich model, respectively. The 17β-E2 adsorption on graphene and H103 was 88% and 70.37%, and the high temperature was beneficial to the 17β-E2 adsorption on graphene while the thermodynamic behaviors of H103 were in direct contrast to that of graphene. The study found that the maximum economic benefits could be achieved when the mass of graphene and H103 in the mixture is 2.79 g and 13.20 kg, respectively.

The health, environmental and socio-economic issues related to the massive use of plant protection products are a concern for all the stakeholders involved in the agricultural sector. These stakeholders, including farmers and territorial actors, have expressed a need for decision-support tools for the management of diffuse pollution related to plant protection practices and their impacts. To meet the needs expressed by the public authorities and the territorial actors for such decision-support tools, we have developed a technical-economic model “OptiPhy” for risk mitigation based on indicators of pesticide toxicity risk to applicator health (IRSA) and to the environment (IRTE), under the constraint of suitable economic outcomes. This technical-economic optimisation model is based on linear programming techniques and offers various scenarios to help the different actors in choosing plant protection products, depending on their different levels of constraints and aspirations. The health and environmental risk indicators can be broken down into sub-indicators so that management can be tailored to the context. This model for technical-economic optimisation and management of plant protection practices can analyse scenarios for the reduction of pesticide-related risks by proposing combinations of substitution PPPs, according to criteria of efficiency, economic performance and vulnerability of the natural environment. The results of the scenarios obtained on real ITKs in different cropping systems show that it is possible to reduce the PPP pressure (TFI) and reduce toxicity risks to applicator health (IRSA) and to the environment (IRTE) by up to approximately 50 %.

Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

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 4.24 × 10⁶ t of waste would be diverted from landfills when p ᵢ is 0.01, which accounted for 93% of waste in Xiamen City, and the waste diversion per unit of cost would be 26.327 × 10³ t per $10⁶. The capacities of MSW management facilities including incinerators, composting facility, and landfills would be expanded due to increasing waste generation rate.

Currently, reduction of carbon dioxide (CO₂) emissions and fuel consumption has become a critical environmental problem and has attracted the attention of both academia and the industrial sector. Government regulations and customer demands are making environmental responsibility an increasingly important factor in overall supply chain operations. Within these operations, transportation has the most hazardous effects on the environment, i.e., CO₂emissions, fuel consumption, noise and toxic effects on the ecosystem. This study aims to construct vehicle routes with time windows that minimize the total fuel consumption and CO₂emissions. The green vehicle routing problem with time windows (G-VRPTW) is formulated using a mixed integer linear programming model. A memory structure adapted simulated annealing (MSA-SA) meta-heuristic algorithm is constructed due to the high complexity of the proposed problem and long solution times for practical applications. The proposed models are integrated with a fuel consumption and CO₂emissions calculation algorithm that considers the vehicle technical specifications, vehicle load, and transportation distance in a green supply chain environment. The proposed models are validated using well-known instances with different numbers of customers. The computational results indicate that the MSA-SA heuristic is capable of obtaining good G-VRPTW solutions within a reasonable amount of time by providing reductions in fuel consumption and CO₂emissions.

This paper presents a bi-level optimization waste load allocation programming model under a fuzzy random environment to assist integrated river pollution control. Taking account of the leader-follower decision-making in the water function zones framework, the proposed approach examines the decision making feedback relationships and conflict coordination between the river basin authority and the regional Environmental Protection Agency (EPA) based on the Stackelberg-Nash equilibrium strategy. In the pollution control system, the river basin authority, as the leader, allocates equitable emissions rights to different subareas, and the then subarea EPA, as the followers, reallocates the limited resources to various functional zones to minimize pollution costs. This research also considers the uncertainty in the water pollution management, and the uncertain input information is expressed as fuzzy random variables. The proposed methodological approach is then applied to Tuojiang River in China and the bi-level linear programming model solutions are achieved using the Karush-Kuhn-Tucker condition. Based on the waste load allocation scheme results and various scenario analyses and discussion, some operational policies are proposed to assist decision makers (DMs) cope with waste load allocation problem for integrated river pollution control for the overall benefits.