in this paper, a new approach to overlapping operations in job shop scheduling is presented. in many job shops, a customer demand can be met in more than one way for each job, where demand determines the quantity of each finished job ordered by a customer. in each job, embedded operations can be performed due to overlapping considerations in which each operation may be overlapped with the other...

this study aims to deal with a more realistic combined problem of project scheduling and material ordering. the goal is to minimize the total material holding and ordering costs by determining the starting time of activities along with material ordering schedules subject to some constraints. the problem is first mathematically modelled. then a hybrid simulated annealing and genetic algorithm is...

this study aims to minimize the total cost of a four-echelon supply chain including suppliers, an assembler, distributers, and retailers. the total cost consists of purchasing raw materials from the suppliers by the assembler, assembling the final product, materials transportation from the suppliers to the assembler, product transportation from the assembler to the distributors, product transpo...

earth dam is a structure as homogeneous or non-homogeneous forms for raising water level or water supply. earth dam consist of different parts that one of the main parts is clay core. choosing an optimal non permeable core which causes reduction of seepage through dam body and also being stable is necessary. the objective of this research is to optimize the geometry of earth dam clay core such ...

Simulated annealing is a well-established approach to optimization that is robust for irregular objective functions. Recently it has been improved using sequential Monte Carlo. This paper presents further improvements that yield the global optimum with accuracy constrained only by the limitations of floating point arithmetic. Performance is illustrated using a standard set of six test problems ...

With several commercial tools becoming available, the high-level synthesis of application-specific integrated circuits is finding wide spread acceptance in VLSI industry today. Existing tools for synthesis focus on optimizing cost while meeting performance constraints or vice versa. Yet, verification and testing have emerged as major concerns of IC vendors since the repurcussions of chips being...

With increasing interest in human-computer interaction, emotion recognition is becoming a hot spot. Support vector machine(SVM) is a machine learning algorithm based on statistic learning theory, finding the optimal separating hyperplane in the high dimensional feature space, having good classification and generalization ability. According to SVM method becoming very difficult for large data sc...

Metaheuristics are general problem solving algorithms which abstract from the actual problem description. Therefore they can be easily applied to many optimization problems. Simulated annealing is a simple and fast metaheuristic with an analogy to metal processing. As metal particles generate a solid and regular structure when cooling slowing simulated annealing seeks a low-energy solution avoi...

In this paper, we review parallel search techniques for approximating the global optimal solution of combinatorial optimization problems. Recent developments on parallel implementation of genetic algorithms, simulated annealing, tabu search, and greedy randomized adaptive search procedures (GRASP) are discussed.

Simulated annealing is a well-studied local search metaheuristic used to address discrete and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing is that it provides a mechanism to escape local optima by allowing hill-climbing moves (i.e., moves which worsen the objective function value) in hopes of finding a global optimum. A brief history of simulated...