Most relevant decisions to be made by market agents within an energy market involve a
significant level of data uncertainty. For agents to make informed decisions within such a …

In this chapter we begin with a brief explanation of linear programming (LP) since integer
programming (IP) is usually regarded as an extension of LP. Also most practical methods of …

In this work, the design and operation planning of a multi-period, multi-product closed-loop
supply chain is addressed. Recovered end-of-life products from customers are evaluated in …

We derive a nested risk measure for a maximization problem and implement it in a scenario-
based formulation of a multi-stage stochastic mixed-integer programming problem. We apply …

We present a framework for modeling multistage mixed 0–1 problems for the air traffic flow
management problem with rerouting (ATFMRP) under uncertainty in the airport arrival and …

Stochastic dominance constraints allow a decision maker to manage risk in an optimization
setting by requiring his or her decision to yield a random outcome which stochastically …

For many decision-making problems under uncertainty, it is crucial to develop risk-averse
models and specify the decision makers' risk preferences based on multiple stochastic …

Deterministic mine planning models along a time horizon have proved to be very effective in
supporting decisions on sequencing the extraction of material in copper mines. Some of …

This paper provides a methodology for supply chain network (SCN) design under
uncertainty. The problem is initially casted as a two-level organizational decision process …

This paper presents mixed-integer programming models to hybrid manufacturing and
remanufacturing lot-sizing problems in which remanufactured products are treated as new …