Meta Heuristics

The integration of entire supply and value chain into a closed loop network is gaining more im-portance in recent times in order to ensure a business to be economically and environmentally sustainable with the changing trends in business and social environments, growing environmental consciousness in the society and government legislations to protect the environment as well as the business. In this context, this paper considers a multi-echelon closed loop supply chain network design with forward and reverse logistics components. An attempt has been made to develop a mixed integer non-linear programming model for this problem with different costs so that the sum of the total cost is minimized subject to different constraints pertaining to capacities of the entities of the system, demands of first customers and second customers. A generalized model is presented and then its application is illustrated using an example problem by solving the model using LINGO14. This model forms as a to...

Two-mode partitioning is a relatively new form of clustering that clusters both rows and columns of a data matrix. In this paper, we consider deterministic two-mode partitioning methods in which a criterion similar to k-means is optimized. A variety of optimization methods have been proposed for this type of problem. However, it is still unclear which method should be used, as various methods may lead to non-global optima. This paper reviews and compares several optimization methods for two-mode partitioning. Several known methods are discussed, and a new fuzzy steps method is introduced. The fuzzy steps method is based on the fuzzy c-means algorithm of Bezdek (1981) and the fuzzy steps approach of Heiser and Groenen (1997) and Groenen and Jajuga (2001). The performances of all methods are compared in a large simulation study. In our simulations, a two-mode k-means optimization method most often gives the best results. Finally, an empirical data set is used to give a practical example of two-mode partitioning.

In this study, a multi-objective model for the reverse logistics network design (RLND) problem and a novel methodology are proposed. The proposed methodology is comprised of two stages: the centralised return centre (CRC) evaluation stage and the reverse logistics network design (RLND) stage. In the first stage an integrated ANP and fuzzy-TOPSIS methodology is utilised. In the second stage, using the CRC weights obtained in the first stage, the RLND model is solved via genetic algorithms (GAs). The proposed methodology is applied to a case from the Turkish white goods industry. The results are discussed and analysed.

Calculus has widespread applications in science and engineering. Optimization is one of its major subjects, where a problem can be mathematically formulated and its optimal solution is determined by using derivatives. However, this calculus-based derivative technique can only be applied to real-valued or continuous-valued functions rather than discrete-valued functions while there are many situations where design variables contain not continuous values but discrete values by nature. In order to consider these realistic design situations, this study proposes a novel derivative for discrete design variables based on a harmony search algorithm. Detailed analysis shows how this new stochastic derivative works in the bench-mark function and fluid-transport network design. Hopefully this new derivative, as a fundamental technology, will be utilized in various science and engineering problems.

Meta-heuristics have been successfully applied to solve different types of problems. However, the contributions of meta-heuristics that deal with high dimensional problems are still very limited compared with low and middle dimensional problems. Tabu Search (TS) represents popular class of meta-heuristics. In this paper, a short term TS algorithm based on variable partitioning is proposed for solving high dimensional problems.

Process planning and scheduling functions strongly influence profitability of manufacturing a product, resource utilisation and product delivery time. Several researchers have addressed the need for integration of process planning and scheduling (IPPS) functions to facilitate ...

Feature selection is one of the issues that have been raised in the discussion of machine learning and statistical identification model. We have provided definitions for feature selection and definitions needed to understand this issue, we check. Then, different methods for this problem were based on the type of product, as well as how to evaluate candidate subsets of features, we classify the following categories. As in previous studies may not have understood that different methods of assessment data into consideration, we propose a new approach for assessing similarity of data to understand the relationship between diversity and stability of the data is selected. After review and meta-heuristic algorithms to implement the algorithm found that the cluster algorithm has better performance compared with other algorithms for feature selection sustained.

This multi-disclipinary forum addresses how Communication Centers bridge the gaps between scientists and their public constituents, provides ways to teach scientific communication from the voice of a scientist, and invokng the perspective of science and technology studies, a reflection on how to bridge epistemological divides that often lead to confrontational relationships between scientists and non-scientists is offered.

ABSTRACT In this paper we develop a stochastic programming approach to solve a multi-period multi-product multi-site aggregate production planning problem in a green supply chain for a medium-term planning horizon under the assumption of demand uncertainty. The proposed model has the following features: (i) the majority of supply chain cost parameters are considered; (ii) quantity discounts to encourage the producer to order more from the suppliers in one period, instead of splitting the order into periodical small quantities, are considered; (iii) the interrelationship between lead time and transportation cost is considered, as well as that between lead time and greenhouse gas emission level; (iv) demand uncertainty is assumed to follow a pre-specified distribution function; (v) shortages are penalized by a general multiple breakpoint function, to persuade producers to reduce backorders as much as possible; (vi) some indicators of a green supply chain, such as greenhouse gas emissions and waste management are also incorporated into the model. The proposed model is first a nonlinear mixed integer programming which is converted into a linear one by applying some theoretical and numerical techniques. Due to the convexity of the model, the local solution obtained from linear programming solvers is also the global solution. Finally, a numerical example is presented to demonstrate the validity of the proposed model.

Recently, many healthcare institutions became interested in automating the process of personnel scheduli ng. Nurse Rostering is a difficult scheduling problem, which is defined as generating rosters by assigning working shifts to a number of nurses over a certain planning period, such that a predefined set of constraints is satisfied. There is a high demand to automate this process, since many healthcare institutions still make nurse rosters manually . In this research we design a scheduling tool that is based on a Genetic Algorithm (GA) approach to solve the nurse rostering problem for the surgical unit in a Riyadh hospital. Our GA-based tool will automatically create a schedule while taking into account a number of difficult constraints. Some constraints are specified by the hospital, and mainly relate to maximizing the coverage of shifts and adhering to workload requirements and certain shift succession patterns. Other constraints relate to preferences of the nurses regarding t h...

AbstractCurrently, meta-heuristics including the genetic algorithms (GA) and simulated annealing (SA) have been used extensively to solve non-deterministic polynomial-time hard (NP-hard) problems. Continued efforts of researchers to upgrade the performance of the meta-heuristics in use resulted in the evolution of new ones. Shuffled frog-leaping algorithm (SFLA) is one of the recently introduced heuristics. The few applications of the SFLA in the literature in different areas demonstrated the capacity of the SFLA to provide high-quality solutions. The main objective of this paper is to further bring the SFLA to the attention of researchers as a potential technique to solve the NP-hard combinatorial problem of finance-based scheduling. The performance of the SFLA is evaluated through benchmarking its results against those of the GA and SA. The traditional problem of generating infeasible solutions in scheduling problems is adequately tackled in the implementations of the GA, SA, and SFLA. Fairly large proj...