This work develops an interactive fuzzy multi-objective linear programming (i-FMOLP) method for solving the fuzzy multiobjective transportation problems with piecewise linear membership function. The proposed i-FMOLP method aims to simultaneously minimize the total distribution costs and the total delivery time with reference to fuzzy available supply and total budget at each source, and fuzzy ...

We consider the problem of optimal investment in cybersecurity by an enterprise. Optimality is measured with respect to the overall (1) monetary cost of implementation, (2) negative side-effects of cyber-security controls (indirect costs), and (3) mitigation of the cybersecurity risk. We consider “passive” and “reactive” threats, the former representing the case where attack attempts are indepe...

Unmanned Aerial Vehicles (UAVs) are emerging as an ideal platform for a wide range of civil applications such as disaster monitoring, atmospheric observation and outback delivery. However, the operation of UAVs is currently restricted to specially segregated regions of airspace outside of the National Airspace System (NAS). Mission Flight Planning (MFP) is an integral part of UAV operation that...

This paper presents a novel dynamic programming approach for multi-objective planning of electrical distribution systems. In this planning, the optimal feeder routes and branch conductor sizes of a distribution system are determined by simultaneous optimization of cost and reliability. The multiple planning objectives are minimization of: (i) installation and operational cost, and (ii) interrup...

The Generation expansion planning (GEP) problem is a large scale, mixed integer and the most complicated optimization problem. So we are using Multi-objective differential evolution (MODE) for solving the GEP problem. To calculate for best combinations of conventional sources with and without wind farm, GEP problem is considered for test system of and satisfy the constraints by calculating EENS...

Real world production planning is involved in optimizing different objectives while considering a spectrum of parameters, decision variables, and constraints of the corresponding cases. This comes from the fact that production managers desire to utilize from an ideal production plan by considering a number of objectives over a set of technological constraints. This paper presents a new multi-ob...

This paper explores the application of multiobjective Genetic Algorithms (mGAs) to rural land use planning, a spatial allocation problem. Two mGAs are proposed. Both share an underlying structure of: fitness assignment using Pareto-dominance ranking, niche induction and an individual replacement strategy. They are differentiated by their representations: a fixedlength genotype composed of genes...

In decision-theoretic planning problems, such as (partially observable) Markov decision problems [Wiering and Van Otterlo, 2012] or coordination graphs [Guestrin et al., 2002], agents typically aim to optimize a scalar value function. However, in many real-world problems agents are faced with multiple possibly conflicting objectives, e.g., maximizing the economic benefits of timber harvesting w...

Knowledge acquisition is one major bottle-neck in using planning systems. Model-lite planning reduces this burden by placing responsibility on the planning system to cope with partially specified models. For example, eliciting the planning objective can be difficult in applications where it is necessary to reason about multiple plan metrics, such as cost, time, risk, human life, etc. Traditiona...