Chance-constrained programming in activity networks: A critical evaluation

Elmaghraby, Salah E.; Soewandi, Hanijanto; Yao, Ming-Jong

doi:10.1016/s0377-2217(00)00086-2

Cited by 11 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When all c i , b j and a ij are deterministic, then is the classical linear programming problem. But when at least one of c i , b j and a ij is stochastic, the problem becomes an SP problem . In this paper, I have considered an SP problem whose a ij parameters are stochastic and the remaining ones are deterministic.…”

Section: The Ccp Approachmentioning

confidence: 99%

A simulated annealing approach for reliability‐based chance‐constrained programming

Sakallı

2013

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

The chance-constrained programming (CCP) is a well-known and widely used stochastic programming approach. In the CCP approach, determining the confidence levels of the constraints at the beginning of solution process is a critical issue for optimality. On one hand, it is possible to obtain better solutions at different confidence levels. On the other hand, the decision makers prefer to simplify their choices instead of grappling with the details such as determining confidence levels for all chance constraints. Reliability is an effective tool that enables the decision maker to look over the system integrity. In this paper, the CCP is considered as a reliability-based nonlinear multiobjective model, and a simulated annealing (SA) algorithm is developed to solve the model. The SA represents different solution alternatives at the different reliability degrees to the decision makers by performing different confidence levels. Thus, the decision makers have the opportunity to make more effective decisions.

show abstract

Section: The Ccp Approachmentioning

confidence: 99%

A simulated annealing approach for reliability‐based chance‐constrained programming

Sakallı

2013

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

show abstract

“…It is hard and also takes a long Applied Computational Intelligence and Soft Computing 3 time to find a feasible solution in searching space which optimizes a constraint problem with traditional strategies. Since one of the crucial problems is to gain a feasible answer in the searching spans, different concepts proposed by researchers and a variety of methods implemented for different optimization situations [33]. Hillier [34] proposed a procedure to predict the chaotic constraints which called linear constraints.…”

Section: The Mutable Smart Bee Algorithmmentioning

confidence: 99%

Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm

Gorji-Bandpy

Mozaffari

2012

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

A new method called mutable smart bee (MSB) algorithm proposed for cooperative optimizing of the maximum power output (MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization (IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). According to obtained results, it can be concluded that Mutable Smart Bee (MSB) is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.

show abstract

“…In CCP, the basic idea is to convert the probabilistic nature of the problem into an equivalent deterministic model (Charnes et al, 1965;Elmaghraby et al, 2001). The deterministic equivalent of the CCP formulation, which is used to find the optimal x, is given by…”

Section: Related Literaturementioning

confidence: 99%

Optimal project portfolio selection with carryover constraint

Gurgur

2009

Journal of the Operational Research Society

View full text Add to dashboard Cite

In a typical capital rationing problem, a project portfolio is selected to maximize expected return on investment while adhering to the capital budget constraint. Sometimes projects may be delayed and they have to be funded beyond their planned completion time. This type of 'unplanned carryovers' represents a financial obligation to the company. If future years' capital budgets cannot be expanded to cover such obligations, future projects may be cancelled or postponed to fund the unplanned carryover. In this paper, we develop a methodology based on multi-attribute utility theory and chance-constrained programming to optimize portfolio selection subject to the constraints that the selected portfolio does not exceed the available budget and that the carryover of the unspent funds to the next fiscal year does not exceed predetermined limits. We use this technique to select an optimal project portfolio for Lockheed Martin Space Systems' infrastructure investments.

show abstract

Chance-constrained programming in activity networks: A critical evaluation

Cited by 11 publications

References 15 publications

A simulated annealing approach for reliability‐based chance‐constrained programming

A simulated annealing approach for reliability‐based chance‐constrained programming

Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm

Optimal project portfolio selection with carryover constraint

Contact Info

Product

Resources

About