Growing attention is being paid to the problem of efficiently designing and operating reverse supply chain systems to handle the return flows of production wastes, packaging, and end-of-life products. Because uncertainty plays a significant role in all fields of decision-making, solution methodologies for determining the strategic infrastructure of reverse production systems under uncertainty are required. This dissertation presents innovative optimization algorithms for designing a robust network infrastructure when uncertainty affects the outcomes of the decisions. In our context, robustness is defined as minimizing the maximum regret under all realization of the uncertain parameters. These new algorithms can be effectively used in designing supply chain network infrastructure when the joint probability distributions of key parameters are unknown. These algorithms only require the information on potential ranges and possible discrete values of uncertain parameters, which often are available in practice. These algorithms extend the state of the art in robust optimization, both in the structure of the problems they address and the size of the formulations. An algorithm for dealing with the problem with correlated uncertain parameters is also presented. Case studies in reverse production system infrastructure design are presented. The approach is generalizable to the robust design of network supply chain systems with reverse production systems as one of their subsystems.

With the election of President Trump, climate deniers feel emboldened and moved from the fringes to the centre of global policy making. We study how an agnostic approach to policy, based on Pascal's wager and allowing for subjective prior probability beliefs about whether climate deniers are right, prices carbon. Using the DICE integrated assessment model, we find that assigning a 10% chance of climate deniers being correct lowers the global price on carbon in 2020 only marginally: from $21 to $19 per ton of carbon dioxide if policymakers apply "Nordhaus discounting" and from $91 to $84 per ton of carbon dioxide if they apply "Stern discounting". Agnostics' reflection of remaining scientific uncertainty leaves climate policy essentially unchanged. The robustness of an ambitious climate policy also follows from using the max-min or the min-max regret principle. Letting the coefficient of relative ambiguity aversion vary from zero, corresponding to expected utility analysis, to infinity, corresponding to the max-min principle, we show how policy makers deal with fundamental climate model uncertainty if they are prepared to assign prior probabilities to different views of the world being correct. Allowing for an ethical discount rate and a higher market discount rate and for a wide range of sensitivity exercises including damage uncertainty, we show that pricing carbon is the robust response under rising climate scepticism. / Series: Ecological Economic Papers