Disassembly sequence planning using a Simplified Teaching–Learning-Based Optimization algorithm

Disassembly Sequence Planning (DSP) is a challenging NP-hard combinatorial optimization problem. As a new and promising population-based evolutional algorithm, the Teaching–Learning-Based Optimization (TLBO) algorithm has been successfully applied to various research problems. However, TLBO is not capable or effective in DSP optimization problems with discrete solution spaces and complex disassembly precedence constraints. This paper presents a Simplified Teaching–Learning-Based Optimization (STLBO) algorithm for solving DSP problems effectively. The STLBO algorithm inherits the main idea of the teaching–learning-based evolutionary mechanism from the TLBO algorithm, while the realization method for the evolutionary mechanism and the adaptation methods for the algorithm parameters are different. Three new operators are developed and incorporated in the STLBO algorithm to ensure its applicability to DSP problems with complex disassembly precedence constraints: i.e., a Feasible Solution Generator (FSG) used to generate a feasible disassembly sequence, a Teaching Phase Operator (TPO) and a Learning Phase Operator (LPO) used to learn and evolve the solutions towards better ones by applying the method of precedence preservation crossover operation. Numerical experiments with case studies on waste product disassembly planning have been carried out to demonstrate the effectiveness of the designed operators and the results exhibited that the developed algorithm performs better than other relevant algorithms under a set of public benchmarks.     

A GRASP based solution approach to solve cardinality constrained portfolio optimization problems

In the current work, a solution methodology which combines a meta-heuristic algorithm with an exact solution approach is presented to solve cardinality constrained portfolio optimization (CCPO) problem. The proposed method is comprised of two levels, namely, stock selection and proportion determination. In stock selection level, a greedy randomized adaptive search procedure (GRASP) is developed. Once the stocks are selected the problem reduces to a quadratic programming problem. As GRASP ensures cardinality constraints by selecting predetermined number of stocks and quadratic programming model ensures the remaining problem constraints, no further constraint handling procedures are required. On the other hand, as the problem is decomposed into two sub-problems, total computational burden on the algorithm is considerably reduced. Furthermore, the performance of the proposed algorithm is evaluated by using benchmark data sets available in the OR Library. Computational results reveal that the proposed algorithm is competitive with the state of the art algorithms in the related literature.     

An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design

This paper addresses an evaluation of new heuristics solution procedures for the location of cross-docks and distribution centers in supply chain network design. The model is characterized by multiple product families, a central manufacturing plant site, multiple cross-docking and distribution center sites, and retail outlets which demand multiple units of several commodities. This paper describes two heuristics that generate globally feasible, near optimal distribution system design and utilization strategies utilizing the simulated annealing (SA) methodology. This study makes two important contributions. First, we continue the study of location planning for the cross-dock and distribution center supply chain network design problem. Second, we systematically evaluate the computational performance of this network design location model under more sophisticated heuristic control parameter settings to better understand interaction effects among the various factors comprising our experimental design, and present convergence results. The central idea of the paper is to evaluate the impact of geometric control mechanism vis-a-vis more sophisticated ones on solution time, quality, and convergence for two new heuristics. Our results suggest that integrating traditional simulated annealing with TABU search is recommended for this supply chain network design and location problem.     

Bee colony optimization for the p-center problem

Bee colony optimization (BCO) is a relatively new meta-heuristic designed to deal with hard combinatorial optimization problems. It is biologically inspired method that explores collective intelligence applied by the honey bees during nectar collecting process. In this paper we apply BCO to the p-center problem in the case of symmetric distance matrix. On the contrary to the constructive variant of the BCO algorithm used in recent literature, we propose variant of BCO based on the improvement concept (BCOi). The BCOi has not been significantly used in the relevant BCO literature so far. In this paper it is proved that BCOi can be a very useful concept for solving difficult combinatorial problems. The numerical experiments performed on well-known benchmark problems show that the BCOi is competitive with other methods and it can generate high-quality solutions within negligible CPU times.     

AN EFFICIENT META-HEURISTIC ALGORITHM FOR ROUTING PRODUCT COLLECTING VEHICLES OF DEHYDRATION PLANTS. II. ALGORITHM PERFORMANCE AND CASE STUDIES

Routing of vehicle fleet for collecting newly cropped raw materials for multi-product dehydration plants is a component of plant production schedule of utmost significance. A meta-heuristic algorithm for efficiently solving the collecting vehicle routing problem was developed and analyzed in detail in Tarantilis and Kiranoudis (2000). Meta-heuristic algorithms are broadly characterized by a stochastic nature in producing tender solution configurations in linear search terms, which sweep the huge solution space in a guided and rational way. Algorithm performance is examined through an analysis of the impact of model parameters on solution procedure during the execution of typical routing problems. The most important model parameter examined was found to be the value of the initial threshold as well as the way that the value of this actual parameter is appropriately adjusted during the optimization process. The main characteristic of the algorithm is the way that threshold is not only lowered but also raised, or backtracked, depending on the success of the inner loop iterations to provide for an acceptable new solution that would replace an older one. An important feature of the algorithm is the fact that appearance of better configurations within a process run is distributed according to the Poisson probability distribution. The suggested algorithm is tested against typical literature benchmarks as well against real-world problem encountered in the production planning procedures of dehydration plants in Greece.     

Optimization of a plate-fin heat exchanger design through an improved multi-objective teaching-learning based optimization (MO-ITLBO) algorithm

Teaching-learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching-learning process. In the present work, multi-objective improved teaching-learning-based optimization (MO-ITLBO) algorithm is introduced and applied for the multi-objective optimization of plate-fin heat exchangers. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive. Minimizing total annual cost and the total weight of heat exchanger as well as minimization of total pressure drop and maximization of heat exchanger effectiveness for specific heat duty requirement are considered as objective functions. Two application examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm.     

基于改进蚁群算法的车辆路径仿真研究

针对基本蚁群算法收敛速度慢、易陷于局部最优等缺陷,提出了一种改进蚁群算法.通过车辆的满载率调整搜索路径上的启发信息强度变化,对有效路径采取信息素的局部更新和全局更新策略,并对子可行解进行3-opt优化,在实现局部最优的基础上保证可行解的全局最优.通过对22城市车辆路径实例的仿真,仿真结果表明,改进型算法性能更优,同基本蚁群相比该算法的收敛速度提高近50%,效果显著,该算法能在更短时间内求得大规模车辆路径问题满意最优解,说明其具有较好的收敛速度和稳定性.     

Multi-rule Multi-objective Simulated Annealing Algorithm for Straight and U Type Assembly Line Balancing Problems

The task of balancing of assembly lines is of considerable industrial importance. It consists of assigning operations to workstations in a production line in such a way that (1) no assembly precedence constraint is violated, (2) no workstations in the line takes longer than a predefined cycle time to perform all tasks assigned to it, and (3) as few workstations as possible are needed to perform all the tasks in the set. This paper presents a new multiple objective simulated annealing (SA) algorithm for simple (line) and U type assembly line balancing problems with the aim of maximizing “smoothness index” and maximizing the “line performance” (or minimizing the number of workstations). The proposed algorithm makes use of task assignment rules in constructing feasible solutions. The proposed algorithm is tested and compared with literature test problems. The proposed algorithm found the optimal solutions for each problem in short computational times. A detailed performance analysis of the selected task assignment rules is also given in the paper.     

Near optimal process plan selection for multiple jobs in networked based manufacturing using multi-objective evolutionary algorithms

The networked manufacturing offers several advantages in current competitive atmosphere by way of reducing the manufacturing cycle time and maintenance of the production flexibility, thereby achieving several feasible process plans. In this paper, we have addressed a Multi Objective Problem (MOP) which covers-minimize the makespan and to maximize the machine utilization while generating the feasible process plans for multiple jobs in the context of network based manufacturing system. A new multi-objective based Territory Defining Evolutionary Algorithm (TDEA) to resolve the above computationally challenge problem have been developed. In particular, with two powerful Multi-Objective Evolutionary Algorithms (MOEAs), viz. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Controlled Elitist-NSGA-II (CE-NSGA-II) the performance of the proposed TDEA has been compared. An illustrative example along with three complex scenarios is presented to demonstrate the feasibility of the approach. The proposed algorithm is validated and the results are analyzed and compared.