Heuristics for the variable sized bin-packing problem

We investigate the one-dimensional variable-sized bin-packing problem. This problem requires packing a set of items into a minimum-cost set of bins of unequal sizes and costs. Six optimization-based heuristics for this problem are presented and compared. We analyze their empirical performance on a large set of randomly generated test instances with up to 2000 items and seven bin types. The first contribution of this paper is to provide evidence that a set covering heuristic proves to be highly effective and capable of delivering very-high quality solutions within short CPU times. In addition, we found that a simple subset-sum problem-based heuristic consistently outperforms heuristics from the literature while requiring extremely short CPU times.     

Hybrid flexible flowshops with sequence-dependent setup times and machine availability constraints

One of the common assumptions in the field of scheduling is that machines are always available in the planning horizon. This may not be true in realistic problems since machines might be busy processing some jobs left from previous production horizon, breakdowns or preventive maintenance activities. Another common assumption is the consideration of setup times as a part of processing times, while in some industries, such as printed circuit board and automobile manufacturing, not only setups are an important factor but also setup magnitude of a job depends on its immediately preceding job on the same machine, known as sequence-dependent setup times. In this paper, we consider hybrid flexible flowshops with sequence-dependent setup times and machine availability constraints caused by preventive maintenance. The optimization criterion is the minimization of makespan. Since this problem is NP-hard in the strong sense, we propose three heuristics, based on SPT, LPT and Johnson rule and two metaheuristics based on genetic algorithm and simulated annealing. Computational experiments are performed to evaluate the efficiencies of the algorithms.     

Bottleneck-based heuristics to minimize total tardiness for the flexible flow line with unrelated parallel machines

This paper considers the flexible flow line problem with unrelated parallel machines at each stage and with a bottleneck stage on the line. The objective of the problem is to minimize the total tardiness. Two bottleneck-based heuristics with three machine selection rules are proposed to solve the problem. The heuristics first develop an indicator to identify a bottleneck stage in the flow line, and then separate the flow line into the upstream stages, the bottleneck stage, and the downstream stages. The upstream stages are the stages ahead of the bottleneck stage and the downstream stages are the stages behind the bottleneck stage. A new approach is developed to find the arrival times of the jobs at the bottleneck stage. Using the new approach, the bottleneck-based heuristics develop two decision rules to iteratively schedule the jobs at the bottleneck stage, the upstream stages, and the downstream stages. In order to evaluate the performance of the bottleneck-based heuristics, seven commonly used dispatching rules and a basic tabu search algorithm are investigated for comparison purposes. Seven experimental factors are used to design 128 production scenarios, and ten test problems are generated for each scenario. Computational results show that the bottleneck-based heuristics significantly outperform all the dispatching rules for the test problems. Although the effective performance of the bottleneck-based heuristics is inferior to the basic tabu search algorithm, the bottleneck-based heuristics are much more efficient than the tabu search algorithm. Also, a test of the effect of the experimental factors on the dispatching rules, the bottleneck-based heuristics, and the basic tabu search algorithm is performed, and some interesting insights are discovered.     

A novel hybrid immune algorithm for global optimization in design and manufacturing

This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The purpose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation.     

Application of ant colony, genetic algorithm and data mining-based techniques for scheduling

In this paper, we have proposed a novel use of data mining algorithms for the extraction of knowledge from a large set of flow shop schedules. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an ant colony algorithm performing a scheduling operation and to develop a rule set scheduler which approximates the ant colony algorithm's scheduler. Ant colony optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The natural metaphor on which ant algorithms are based is that of ant colonies. Fascinated by the ability of the almost blind ants to establish the shortest route from their nests to the food source and back, researchers found out that these ants secrete a substance called ‘pheromone’ and use its trails as a medium for communicating information among each other. The ant algorithm is simple to implement and results of the case studies show its ability to provide speedy and accurate solutions. Further, we employed the genetic algorithm operators such as crossover and mutation to generate the new regions of solution. The data mining tool we have used is Decision Tree, which is produced by the See5 software after the instances are classified. The data mining is for mining the knowledge of job scheduling about the objective of minimization of makespan in a flow shop environment. Data mining systems typically uses conditional relationships represented by IF-THEN rules and allowing the production managers to easily take the decisions regarding the flow shop scheduling based on various objective functions and the constraints.     

Perimeter discovery in wireless sensor networks

In this paper, we focus on the perimeter detection problem using wireless sensor networks, as perimeter detection has a wide range of uses in several areas. We present a decentralized localized algorithm where sensor nodes determine if they are located along the perimeter of a wireless sensor network. Our proposed algorithm uses the location neighborhood information in conjunction with the Barycentric technique to determine if the sensor node enclosed by neighboring nodes, and consequently, if it is located within the interior of the wireless sensor network. We define performance metrics to analyze the performance of our approach and the simulation shows that the algorithm gives fairly accurate results.     

On neighbor discovery in cognitive radio networks

A cognitive radio node is a radio device capable of operating over multiple channels. As a result, a network consisting of one or more cognitive radio nodes can adapt to varying channel availability in its geographical region by dynamically changing the channel (or channels) nodes are using for communication.