A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption

Flexible job shop scheduling problem (FJSP) has been extensively investigated and objectives are often related to time. Energy-related objective should be considered fully in FJSP with the advent of green manufacturing. In this study, FJSP with the minimisation of workload balance and total energy consumption is considered and the conflicting between two objectives is analysed. A shuffled frog-leaping algorithm (SFLA) is proposed based on a three-string coding approach. Population and a non-dominated set are used to construct memeplexes according to tournament selection and the search process of each memeplex is done on its non-dominated member. Extensive experiments are conducted to test the search performance of SFLA and computational results show the conflicting between two objectives of FJSP and the promising advantages of SFLA on the considered FJSP.     

An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines

In this article, an effective shuffled frog-leaping algorithm (SFLA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines (HFSP-IPM). First, some novel heuristic decoding rules for both job order decision and machine assignment are proposed. Then, three hybrid decoding schemes are designed to decode job order sequences to schedules. A special bi-level crossover and multiple local search operators are incorporated in the searching framework of the SFLA to enrich the memetic searching behaviour and to balance the exploration and exploitation capabilities. Meanwhile, some theoretical analysis for the local search operators is provided for guiding the local search. The parameter setting of the algorithm is also investigated based on the Taguchi method of design of experiments. Finally, numerical testing based on well-known benchmarks and comparisons with some existing algorithms are carried out to demonstrate the effectiveness of the proposed algorithm.     

A genetic algorithm heuristic approach to general outsourcing capacitated production planning problems

This paper addresses a dynamic capacitated production planning (CPP) problem with consideration of outsourcing. Specifically, the outsourcing problem considered in this paper has the following features: (1) all demands are met by production or outsourcing without postponement or backlog, (2) production, inventory, and outsourcing levels all have a limit, and (3) the cost functions are considered arbitrarily and time-varying. These features come together, leading to a so-called general outsourcing CPP problem. In our previous work, an algorithm with pseudo-polynomial time complexity was developed, which includes a formation of a feasible solution region and then a search procedure using dynamic programming techniques. Due to the computational complexity with such an approach, only small and medium problems can be solved in a practical sense. In this paper, we present a genetic algorithm (GA) approach to the same problem. The novelty of this GA approach is that the idea of the feasible solution region is used as a heuristic to guide the searching process. We present a computational experiment to show the effectiveness of the proposed approach.     

Capacitated production planning with outsourcing in an OKP company

This paper addresses a dynamic capacitated production planning (CPP) problem in small to medium-sized enterprises (SMEs). These companies practise their businesses based on a manufacturing paradigm called one-of-a-kind production (OKP). Typically, they employ outsourcing in their CPP practice. Therefore, the CPP problem in this paper includes outsourcing, which is called the outsourcing CPP problem. The outsourcing strategy considered in this paper has the following features: (1) outsourcing takes place when the demand exceeds the capacity; (2) all excessive demands are outsourced without backlogging; and (3) production, inventory, and outsourcing levels all have a limit. One important difference of the problem considered in this paper from other problems is that the cost functions are considered arbitrarily time-varying. The outsourcing CPP problem is formulated into an optimization problem which captures the mentioned features. The objective of the problem is to minimize the total cost. It is very likely that there is no solution in our model so we propose a necessary and sufficient condition for feasible solutions. Dynamic programming techniques are employed in searching for a global optimal solution with a pseudo-polynomial time. This paper also presents a case study for a real-life problem in an OKP company to illustrate our approach and to show its effectiveness. The case study has shown that our approach is very effective.     

Optimal design of Raman fibre amplifier based on terminal value optimization strategy and shuffled frog leaping algorithm

This paper introduces an evolutionary algorithm, Shuffled Frog Leaping Algorithm (SFLA), to solve the optimization problem in designing the multi-pumped Raman Fibre Amplifier (RFA). SFLA is a powerful optimizer tool because of its efficient mathematical expressions and global search capability. We utilize SFLA to determine the optimal pump wavelengths and pump powers by minimizing the gain ripple of RFA. To accelerate calculations, a terminal value optimization strategy (TVOS) is incorporated into the evolution of SFLA. This proposed strategy takes the terminal power values of pumps as the decision variables in optimization. Then, the optimal original power values of the pumps are obtained by solving the Power Coupled Equations once, without using the traditional method of repetitive guesses.The combination of SFLA and TVOS enhances the efficiency of optimization and accelerates calculation, while satisfying the design requirements of RFA.The simulation results show that nearly 65% of computational time has been saved compared with the traditional average power analysis. The 4-pumped C+L band of backward multi-pumped RFA with the average net gain of 0 dB, 1 dB and 2 dB are designed individually, where the gain ripple is less than 0.64 dB. The combination of SFLA and TVOS enhance the optimization efficiency and improve the performance of RFA with good gain profile.     

Production scheduling with outsourcing scenarios: a mixed integer programming and efficient solution procedure

When market demand exceeds the company's capacity to manufacture, outsourcing is commonly considered as an effective alternative option. In traditional scheduling problems, processing of received orders is just possible via in-house resources, while in practice, outsourcing is frequently found in various manufacturing industries, especially in electronics, motor and printing companies. This paper deals with the scheduling problem, minimising the cost of outsourcing and a scheduling measure represented by weighted mean flow time, in which outsourcing of manufacturing operations is allowed through subcontracts. Each order can be either scheduled for in-house production or outsourced to an outside supplier in order to meet customer due dates. In this problem, not only should the sequence of orders be determined, but also decision on picking the jobs for outsourcing, selecting the appropriate subcontractor, and scheduling of the outsourced orders are considered as new variables. To formulate the given problem, four different outsourcing scenarios are derived and mixed integer programming models are developed for each one separately. Furthermore, to solve the suggested problem, a computationally effective team process algorithm is devised and then a constraint handling technique is embedded into the main algorithm in order to ensure satisfaction of customer due dates. Numerical results show that the suggested approach possesses high global solution rates as well as fast convergence.     

Job shop scheduling with the option of jobs outsourcing

Incorporating outsourcing in scheduling is addressed by several researchers recently. However, this scope is not investigated thoroughly, particularly in the job shop environment. In this paper, a new job shop scheduling problem is studied with the option of jobs outsourcing. The problem objective is to minimise a weighted sum of makespan and total outsourcing cost. With the aim of solving this problem optimally, two solution approaches of combinatorial optimisation problems, i.e. mathematical programming and constraint programming are examined. Furthermore, two problem relaxation approaches are developed to obtain strong lower bounds for some large scale problems for which the optimality is not proven by the applied solution techniques. Using extensive numerical experiments, the performance of the solution approaches is evaluated. Moreover, the effect the objectives's weights in the objective function on the performance of the solution approaches is also investigated. It is concluded that constraint programming outperforms mathematical programming significantly in proving solution optimality, as it can solve small and medium size problems optimally. Moreover, by solving the relaxed problems, one can obtain good lower bounds for optimal solutions even in some large scale problems.     

Verifiable Diversity Ranking Search Over Encrypted Outsourced Data

Data outsourcing has become an important application of cloud computing. Driven by the growing security demands of data outsourcing applications, sensitive data have to be encrypted before outsourcing. Therefore, how to properly encrypt data in a way that the encrypted and remotely stored data can still be queried has become a challenging issue. Searchable encryption scheme is proposed to allow users to search over encrypted data. However, most searchable encryption schemes do not consider search result diversification, resulting in information redundancy. In this paper, a verifiable diversity ranking search scheme over encrypted outsourced data is proposed while preserving privacy in cloud computing, which also supports search results verification. The goal is that the ranked documents concerning diversification instead of reading relevant documents that only deliver redundant information. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is effective for the diversification of documents and verification.     

Pattern reduction in one-dimensional cutting stock problems

In industrial cutting operations the number of cutting patterns needed to satisfy a given set of orders may be crucial for the capacity load which can be achieved for the cutting equipment, since switching between different patterns often necessitates time-consuming set-ups. Therefore, in planning the cutting operations, one does not necessarily search for input-minimal cutting plans (i.e. cutting plans minimizing material costs) only, but also for plans with a small (or even minimal) number of cutting patterns. Such plans are usually generated in a two-step approach: in a first step, regardless of the number of patterns needed, an inputminimal cutting plan is generated while in a second step the number of patterns is reduced. In this paper a new method for the second step is presented, which can be considered as a generalization of other methods previously suggested for this purpose. The performance of the new method is evaluated on the basis of 1800 randomly generated problem instances. The solution quality of the new method turns out to be clearly superior to the existing methods, however, an increase in computing times occurs for some problem classes. In this case it is possible to speed up the proposed method considerably at the expense of a slight decrease in solution quality.     

Optimization of a line-cutting procedure for ship hull construction by an effective tabu search

A problem of a line-cutting procedure is discussed for ship hull construction. This procedure includes five steps: stock arrangement, piece arrangement, marking, cutting and special processes, and delivering the work. The line-cutting procedure will be optimized when two objectives are considered: minimizing the total trim loss and keeping the working efficiency of the cutting procedure. The developed way for the discussed problem is to optimize the stock arrangement associated with a proposed rule-based piece arrangement where the former is optimized for minimizing the total trim loss and the latter for keeping the working efficiency. A tabu search is chosen for the optimization of stock arrangement. In addition, two proposed replacement moves, the aggregative and the breaking, are adopted to improve the effectiveness of the tabu search. Two real cases and some random instances are used for test and comparison. The results show that the stock arrangement was optimized satisfyingly by the tabu search and the rule-based piece arrangement is capable of keeping the working efficiency as usual. In addition, the effectiveness of the tabu search was apparently improved by two proposed replacement moves.     

Optimum Patch Repair Shapes for Cracked Members

In the present paper the problem of finding the optimal patch repair shape in a cracked structural component with respect to a given objective function, is investigated by using a biological-based procedure known as Genetic Algorithm (GA) in conjunction with a penalty constraint. The search for an optimal patch shape, which can be regarded also as an optimal topology problem, is obtained by determining the corresponding patch’s continuous material’s density distribution: the design optimisation considered in the present study takes into account the minimisation of some mechanical desired performance of the structure’s while keeping constant the total available patched area (optimal constrained problem). The proposed optimisation model is implemented in a F.E. code and some numerical simulations are performed in order to assess its reliability in optimal topology design of patch repairs, applied to mode I cracked members, with respect to some expected performances. For the considered problems, the optimisation algorithm allows a quite important improvement (in reducing the stress intensity factor) with respect to cracked components repaired with simple shape (i.e. rectangular) bonded patches.     

Parallel machine scheduling with step-deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm

This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup–Hermann–Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX^® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.     

The future of prior art searching at the United States patent and trademark office

The United States Patent and Trademark Office has experienced an increased volume and complexity of patent applications in recent years, with resulting problems in maintaining pendency times and quality. As part of the Office’s 21st Century Strategic Plan to deal with this and other problems, the outsourcing of prior art patent searches has been proposed. In this article the author explains the current situation––including the existing partial use of search contractors, briefly surveys some solutions adopted in other patent offices, and then details the outsourcing proposals. It is intended that the contracting out of searches will proceed initially by means of a proof of concept pilot conducted on the search reports prepared by the Office on international applications under the PCT. Contractors will need to be able to demonstrate technical and legal competence, that there is no conflict of interest between these and other searches they carry out, and that they will maintain strict confidentiality. If successful, a wider implementation will be rolled out, with procedures in place to ensure that high quality searches are provided on a continuous basis. Analogous contracting out of classification and reclassification functions is also described.     

Variable neighbourhood search for dual-resource constrained flexible job shop scheduling

Dual-resource constrained flexible job shop scheduling problem (FJSP) is considered and an effective variable neighbourhood search (VNS) is presented, in which the solution to the problem is indicated as a quadruple string of the ordered operations and their resources. Two neighbourhood search procedures are sequentially executed to produce new solutions for two sub-problems of the problem, respectively. The search of VNS is restarted from a slightly perturbed version of the current solution of VNS when the determined number of iterations is reached. VNS is tested on some instances and compared with methods from literature. Computational results show the significant advantage of VNS on the problem.     

Multiple-variable neighbourhood search for the single-machine total weighted tardiness problem

The single-machine total weighted tardiness (SMTWT) problem is a typical discrete combinatorial optimization problem in the scheduling literature. This problem has been proved to be NP hard and thus provides a challenging area for metaheuristics, especially the variable neighbourhood search algorithm. In this article, a multiple variable neighbourhood search (m-VNS) algorithm with multiple neighbourhood structures is proposed to solve the problem. Special mechanisms named matching and strengthening operations are employed in the algorithm, which has an auto-revising local search procedure to explore the solution space beyond local optimality. Two aspects, searching direction and searching depth, are considered, and neighbourhood structures are systematically exchanged. Experimental results show that the proposed m-VNS algorithm outperforms all the compared algorithms in solving the SMTWT problem.     

A dispatching rule and a random iterated greedy metaheuristic for identical parallel machine scheduling to minimize total tardiness

This paper addresses a real-life production scheduling problem with identical parallel machines, originating from a plant producing Acrylonitrile-Butadiene-Styrene (ABS) plate products. In the considered practical scheduling problem, ABS plate has some specific specifications and each specification has several different levels. Because there is at least one different level of specification between two ABS plate products, it is necessary to make a set-up adjustment on each machine whenever a switch occurs from processing one ABS plate product to another product. As tardiness leads to extra penalty costs and opportunity losses, the objective of minimising total tardiness has become one of the most important tasks for the schedule manager in the plant. The problem can be classified as an identical parallel machine scheduling problem to minimise the total tardiness. A dispatching rule is proposed for this problem and evaluated by comparing it with the current scheduling method and several existing approaches. Moreover, an iterated greedy-based metaheuristic is developed to further improve the initial solution. The experimental results show that the proposed metaheuristic can perform better than an existing tabu search algorithm, and obtain the optimal solution for small-sized problems and significantly improve the initial solutions for large-sized problems.     

A price-directed decomposition approach for solving large-scale capacitated part-routing problems

We consider the problem of planning the production steps of several parts through a manufacturing system with both process and routing flexibilities. The problem is formulated as a network flow-based linear programming model which seeks to minimise the total material handling, production, and outsourcing costs subject to satisfying all the part demands and not exceeding any of the machine capacity limits. We develop a price-directed decomposition-based approach that exploits the special structure of the model in order to solve it. An extensive computation experiment is carried out in order to gain some insights into the impacts of flexibility in the manufacturing system on the optimal decision and cost, and to test the efficiency of the procedure in handling large scale problems.     

Productivity measurement of the manufacturing process for outsourcing decisions: the case of a Taiwanese printed circuit board manufacturer

Outsourcing is used to alleviate capacity shortages and increase production flexibility. Generally, the outsourcing decision of manufacturing processes is determined by the capacity utilization rate and focus on individual process. In this article, multicriteria are considered by applying the data envelopment analysis to evaluate the relative efficiencies of all the manufacturing processes to help to assess the allocation of a company's resources. The Malmquist productivity index (MPI) is further employed to measure the productivity changes of each process, on which the outsourcing decision can be based. The empirical case of a Taiwanese printed circuit board manufacturer is applied to illustrate the outsourcing decision process. According to the outsourcing decision matrix proposed in this article, the manufacturing process with low efficiency score and MPI value was identified as a high priority for outsourcing. The outcome shows that the manufacturing process with a higher investment of fixed assets does not guarantee better efficiency. If a company follows the outsourcing decision suggested by the proposed approach, the capacity utility rate and the production quantity per person can be increased. The results and analysis model can provide an alternative for managerial thinking and practical application for outsourcing decisions.     

Sequencing algorithms for multiple arc-welding robots considering thermal distortion

This paper focuses on sequencing welding lines in multiple arc-welding robot systems, which is the problem of assigning individual welding operations to robots as well as determining their sequences with the objective of minimizing the maximum completion time. Each welding operation is denoted by a weld line with two end-points, each of which can be a possible starting point for the welding operation. Thermal distortion is explicitly considered by introducing a delay between welding operations associated with weld lines near each other. Due to the complexity of the problem, this paper suggests three types of search heuristics, genetic algorithms, simulated annealing and tabu search, each of which incorporates new methods to generate neighbourhood solutions. To show the performances of the heuristics, computational experiments are performed on a number of randomly generated test problems, and the results are reported. In particular, some of the search heuristics outperform the exiting method.     

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Content based image retrieval (CBIR) techniques have been widely deployed in many applications for seeking the abundant information existed in images. Due to large amounts of storage and computational requirements of CBIR, outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices. However, owing to the private content contained in images, directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem, so the images should be protected carefully before outsourcing. This paper presents a secure retrieval scheme for the encrypted images in the YUV color space. With this scheme, the discrete cosine transform (DCT) is performed on the Y component. The resulting DC coefficients are encrypted with stream cipher technology and the resulting AC coefficients as well as other two color components are encrypted with value permutation and position scrambling. Then the image owner transmits the encrypted images to the cloud server. When receiving a query trapdoor form on query user, the server extracts AC-coefficients histogram from the encrypted Y component and extracts two color histograms from the other two color components. The similarity between query trapdoor and database image is measured by calculating the Manhattan distance of their respective histograms. Finally, the encrypted images closest to the query image are returned to the query user.