The layout design problem is a kind of nesting problems that is naturally NP-hard and very difficult to solve. Layout designing of machine is even more difficult because of its nesting items are actually machine parts that have both irregular shapes and complex constraints. A feasible way to solve machine layout problem is to employ ameliorative algorithms, such as simulated annealing algorithm...

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization pro...

A procedure is presented which considerably improves the performance of local search based heuristic algorithms for combinatorial optimization problems. It increases the average \gain" of the individual local searches by merging pairs of solutions: certain parts of either solution are transcribed by the related parts of the respective other solution, corresponding to ipping clusters of a spin g...

Abstract—This paper aims to present non-population search algorithms called tabu search (TS), simulated annealing (SA) and variable neighborhood search (VNS) to minimize the total cost of capacitated MRP problem in multi-stage assembly flow shop with two alternative machines. There are three main steps for the algorithm. Firstly, an initial sequence of orders is constructed by a simple due date...

This paper presents a hybrid genetic algorithm to solve the uncapacitated location allocation problems as a combinatorial optimization problem. The proposed method incorporates a modified K-means algorithm that clusters the customers into groups based on the rectilinear distance, and then the initial population of solutions is calculated according to the derived centers of clusters. The hybrid ...

We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Problem by applying Genetic Algorithms. Following the lines of Johnson [1990] we discuss some possibilities for speeding up classical Local Search algorithms by casting them into a genetic frame. In an experimental study two such approaches, viz. Genetic Local Search with 2-Opt neighbourhoods and Li...

The Economic Lot Scheduling Problem (ELSP) has been well-researched for more than 40 years. As the ELSP has been generally seen as NP-hard, researchers have focused on the development of efficient heuristic approaches. In this paper, we consider the time-varying lot size approach to solve the ELSP. A computational study of the existing solution algorithms, Dobson’s heuristic, Hybrid Genetic alg...

An algorithm is developed to statistically find the best global fit of a nonlinear non-convex cost-function over a D-dimensional space. It is argued that this algorithm permits an annealing schedule for ''temperature'' T decreasing exponentially in annealing-time k, T = T 0 exp(−ck 1/D). The introduction of re-annealing also permits adaptation to changing sensitivities in the multi-dimensional ...

We propose a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and can be quicker than both. Key-...