Production planning is a key area of operations management. An important methodology for production planning is mathematical programming. Traditional mathematical programming models for production planning are deterministic, and cannot provide robust production plans in the presence of uncertainty. As such, deterministic planning models may yield unsatisfactory decisions. Stochastic programming...

A dynamic network is a communication network whose communication structure can evolve over time. The dynamic diameter is the counterpart of the classical static diameter, it is the maximum time needed for a node to causally influence any other node in the network. We consider the problem of computing the dynamic diameter of a given dynamic network. If the evolution is known a priori, that is if...

in this article, a finite horizon, multi product and multi period economic order quantity like seasonal items is considered where demand rate is deterministic and known but variable in each period. the order quantities of items come in batch sizes and the end of the period order quantity and, consequently, demand of customers are zero. in addition, storage space is constrained and the problem ...

This paper proposes extensions of sequential programming languages for parallel programming that have the following features: 1. Dynamic Structures The process structure is dynamic: Processes and variables can be created and deleted. 2. Paradigm Integration The programming notation allows shared memory and message passing. 3. Determinism Demonstrating that a program is deterministic | all execu...

Scenario analysis within a multi-stage stochastic programming formulation offers an attractive framework for modelling uncertainties in long-range planning models. However, whether the expected outcome is implicitly / explicitly evaluated, such formulations lead to computationally intensive optimization problems. Focussing here on the scenario planning / multi-period approach for mixed integer ...

In this paper, the advantages and performance of genetic programming in use of error recovery planning in robotic assembly systems is discussed. Existing systems use polynomial time planning techniques or heuristics to produce error recovery plans. However, these systems require translation of the generated plans to working controller codes. An alternative approach could be the use of Genetic P...

Two major approaches to deal with randomness or impression involved in mathematical programming problems have been developed. The one is called stochastic programming, and the other is called fuzzy programming. In this paper, we focus on multiobjective integer programming problems involving random variable coefficients in constraints. Using the concept of simple recourse, such multiobjective st...

This paper presents a convergence analysis of particle swarm optimization system by treating it as a discrete-time linear time-variant system firstly. And then, based on the results of system convergence conditions, dynamic optimal control of a deterministic PSO system for parameters optimization is studied by using dynamic programming; and an approximate dynamic programming algorithm – swarm-b...