A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling

This paper addresses the multiobjective flexible job shop scheduling problem (MOFJSP) regarding minimizing the makespan, total workload, and maximum workload. The problem is solved in a Pareto manner, whose goal is to seek for the set of Pareto optimal solutions. We propose a multiobjective evolutionary algorithm, which utilizes effective genetic operators and maintains population diversity carefully. A main feature of the proposed algorithm is its simplicity—it needs only two parameters. Performance of our algorithm is compared with seven state-of-the-art algorithms on fifteen popular benchmark instances. Only our algorithm can find 70% or more non-dominated solutions for every instance.     

Unrelated parallel machine scheduling with setup consideration and a total weighted completion time objective

This paper addresses the problem of scheduling a set of independent jobs on unrelated parallel machines with job sequence dependent setup times so as to minimize a weighted mean completion time. The study of the problem stemmed from a real service industry problem. This problem is at least NP-hard in the ordinary sense, even when there are only two identical machines with no setups. Seven heuristic algorithms are proposed and tested by simulation. The results and analysis of quite extensive computational experiments are reported and discussed. The findings through the computational results are presented. Whether this problem is strongly NP-hard is left as an open question.     

A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search

The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches.     

A multiple ant colony optimization algorithm for the capacitated location routing problem

The success of a logistics system may depend on the decisions of the depot locations and vehicle routings. The location routing problem (LRP) simultaneously tackles both location and routing decisions to minimize the total system cost. In this paper a multiple ant colony optimization algorithm (MACO) is developed to solve the LRP with capacity constraints (CLRP) on depots and routes. We decompose the CLRP into facility location problem (FLP) and multiple depot vehicle routing problem (MDVRP), where the latter one is treated as a sub problem within the first problem. The MACO algorithm applies a hierarchical ant colony structure that is designed to optimize different subproblems: location selection, customer assignment, and vehicle routing problem, in which the last two are the decisions for the MDVRP. Cooperation between colonies is performed by exchanging information through pheromone updating between the location selection and customer assignment. The proposed algorithm is evaluated on four different sets of benchmark instances and compared with other algorithms from the literature. The computational results indicate that MACO is competitive with other well-known algorithms, being able to obtain numerous new best solutions.     

Heuristics for operational fixed job scheduling problems with working and spread time constraints

Operational fixed job scheduling problems select a set of jobs having fixed ready and processing times and schedule the selected jobs on parallel machines so as to maximize the total weight. In this study, we consider working time and spread time constrained versions of the operational fixed job scheduling problems. The working time constraints limit the total processing load on each machine. The spread time constraints limit the time between the start of the first job and the finish of the last job on each machine. For the working time constrained problem, we present a filtered beam search algorithm that evaluates the promising nodes of the branch and bound tree. For the spread time constrained problem we propose a two phase algorithm that defines the promising sets for the first jobs and finds a solution for each promising set. The results of our computational tests reveal that our heuristic algorithms perform very well in terms of both solution quality and time.     

A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times,sequence-dependent setup times and learning effect

This paper addresses multi-objective (MO) optimization of a single-model assembly line balancing problem (ALBP) where the operation times of tasks are unknown variables and the only known information is the lower and upper bounds for operation time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the total equipment cost, and (3) minimizing the smoothness index. In order to reflect the real industrial settings adequately, it is assumed that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and sequence-dependent setup time exists between tasks. Finding an optimal solution for this complicated problem especially for large-sized problems in reasonable computational time is cumbersome. Therefore, we propose a new solution method based on the combination of particle swarm optimization (PSO) algorithm with variable neighborhood search (VNS) to solve the problem. The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Comparison with an existing multi-objective evolutionary computation method in the literature shows the superior efficiency of our proposed PSO/VNS algorithm.     

Co-development as a marketing strategy in the construction industry

This paper deals with a specific issue in project marketing, namely project co-development, presented as a relational and cooperative form of exchange in the project network. It endeavours to find out under what conditions project co-development is considered as a viable marketing strategy as opposed to competitive bidding. A model is developed, which is then used to analyze two cases of project co-development in the construction industry. Of the two cases, one is a successful case and the other is a failure case. This enabled us to analyze factors and conditions that can lead to a successful co-development strategy. Factors such as, project complexity, conflicting objectives, lack of compatibility, lack of trust and commitment and lack of coherence between the budget and the objectives turned out to influence a co-development strategy. The role played by the key decision-makers (project manager) also influenced the success/failure of co-development strategy.     

Cellular economies of scale and why disparities in spectrum holdings are detrimental

Now that traffic volumes are increasing rapidly, the cost of expanding capacity has become a large portion of expenditures for Mobile Network Operators (MNOs). This paper uses an engineering-economic model to show that there are strong economies of scale when expanding capacity, because an MNO with more spectrum benefits more from every new cell tower, and an MNO with more towers benefits more from every new MHz of spectrum. While it is technically possible to expand capacity by increasing either towers or spectrum holdings, we find that the cost-effective approach is to increase both types of assets at a similar rate. In the absence of countervailing policies, the big MNOs are well positioned to get bigger, in terms of spectrum holdings, towers, and ultimately market share. For policymakers, this economy of scale creates a trade-off between two important objectives: reducing the cost of cellular capacity, and increasing competition. This paper derives the Pareto optimal division of spectrum with respect to these two competing objectives, and shows that any Pareto optimal assignment will split the spectrum fairly evenly among competing MNOs. This is not simply a method of ensuring that there are many competitors; spectrum should be divided fairly evenly regardless of whether the number of competitors is large or small. A large disparity in spectrum holdings may yield poor results with respect to both objectives, i.e. the lower cost-effectiveness of a larger number of MNOs, and the lower competitive pressure of a smaller number of MNOs. One effective way to achieve a division of spectrum that is close to Pareto optimal is a spectrum cap, provided that this cap is set at a level consistent with other policies and policy objectives, including antitrust policy.     

Two-machine flowshop scheduling with a truncated learning function to minimize the makespan

Scheduling with learning effects has continued to attract the attention of scheduling researchers. However, the majority of the research on this topic has been focused on the single-machine setting. Moreover, under the commonly adopted learning model in scheduling, the actual processing time of a job drops to zero precipitously as the number of jobs increases, which is at odds with reality. To address these issues, we study a two-machine flowshop scheduling problem with a truncated learning function in which the actual processing time of a job is a function of the job's position in a schedule and the learning truncation parameter. The objective is to minimize the makespan. We propose a branch-and-bound and three crossover-based genetic algorithms (GAs) to find the optimal and approximate solutions, respectively, for the problem. We perform extensive computational experiments to evaluate the performance of all the proposed algorithms under different experimental conditions. The results show that the GAs perform quite well in terms of both efficiency and solution quality.     

Metaheuristic algorithms for the multistage hybrid flowshop scheduling problem

We consider the multistage hybrid flowshop scheduling problem, in which each stage consists of parallel identical machines. The problem is to determine a schedule that minimizes the makespan for a given set of jobs over a finite planning horizon. Since this problem class is NP-hard in the strong sense, there seems to be no escape from appealing to heuristic procedures to achieve near-optimal solutions to real life problems. First, a series of new global lower bounds to be used to estimate the minimum makespan are derived. Then, two new metaheuristic algorithms first sequence and then allocate jobs to machines based on a particular partition of the shop. The optimization procedure is based on simulated annealing and the variable-depth search. Computational experiments show the efficiency of the proposed procedures.     

Coordinated supply chain scheduling

A mixed integer programming approach is proposed for a long-term, integrated scheduling of material manufacturing, material supply and product assembly in a customer driven supply chain. The supply chain consists of three distinct stages: manufacturer/supplier of product-specific materials (parts), producer where finished products are assembled according to customer orders and a set of customers who generate final demand for the products. The manufacturing stage consists of identical production lines in parallel and the producer stage is a flexible assembly line. The overall problem is how to coordinate manufacturing and supply of parts and assembly of products such that the total supply chain inventory holding cost and the production line start-up and parts shipping costs are minimized. A monolithic approach, where the manufacturing, supply and assembly schedules are determined simultaneously, is compared with a hierarchical approach. Numerical examples modeled after a real-world integrated scheduling in a customer driven supply chain in the electronics industry are presented and some computational results are reported.     

A survey of the application of fuzzy set theory in production and operations management: 1998–2009

The objectives of this research are to identify the research trends in and publication outlets for the applications of the fuzzy set theory technique in production and operations management (POM). The major findings indicate that (1) the most popular applications are capacity planning, scheduling, inventory control, and product design, (2) some application areas make more use of particular types of fuzzy techniques, (3) the percentage of applications that address semi/unstructured types of POM problems is increasing, (4) the most common technologies integrated with the fuzzy set theory technique are genetic/evolutionary algorithms and neural networks, and (5) the most popular development tool is C Language and its extension. Our survey confirms several research trends, some of which are unexpected and some of which contradict previous findings.     

Mediating Internal Competition for Resources*

We consider a model of internal competition, where projects developed by agents with different preferences compete for resources in an organization. Allowing a manager—who has moderate preferences—to control the allocation of resources has benefits when preferences are not too diverse. In particular, the manager acts as a mediator, forcing agents to compromise when competing projects succeed, thus providing better insurance to agents and increasing their effort. Our framework provides a theoretical foundation for two influential views of a manager—as the “visible hand” that allocates resources, and as a “power broker” who resolves conflict in an organization.     

Permutation flowshop scheduling to minimize the total tardiness with learning effects

Scheduling with learning effects has received considerable attention recently. Often, numbers of operations have to be done on every job in many manufacturing and assembly facilities. However, it is seldom discussed in the general multiple-machine setting, especially without the assumptions of identical processing time on all the machines or dominant machines. With the current emphasis of customer service and meeting the promised delivery dates, we consider a permutation flowshop scheduling problem with learning effects where the objective is to minimize the total tardiness. A branch-and-bound algorithm and two heuristic algorithms are established to search for the optimal and near-optimal solutions. Computational experiments are also given to evaluate the performance of the algorithms.     

Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families

Motivated by a bottleneck operation in a multi-layer ceramic capacitor production line, we study a scheduling problem of batch processing machine in which a number of jobs are processed simultaneously as a batch. The performance measures considered include makespan, total completion time, and total weighted completion time. We first present a new simple integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms and design a hybrid genetic algorithm to solve practical big-size problems in a reasonable computational time. To verify performance of the algorithms, we compare them with lower bounds for the problems. From the results of these computational experiments the heuristic algorithms including the genetic algorithm show different performances for the three problems.     

A unified solution approach for the due date assignment problem with tardy jobs

We analyze a number of due date assignment problems with the weighted number of tardy jobs objective and show that these problems can be solved in O(n²) time by dynamic programming. We show that the effects of learning or the effects of past-sequence-dependent setup times can be incorporated into the problem formulation at no additional computational cost. We also show that some single-machine due date assignment problems can be extended to an identical parallel machine setting. Finally, we improve the complexity of the solution algorithms for two other due date assignment problems.     

Two-stage flowshop earliness and tardiness machine scheduling involving a common due window

This paper studies a non-preemptive two-stage flowshop scheduling problem to minimize the earliness and tardiness under the environment of a common due window. The window size and the window location are considered to be given parameters. The just-in-time problem exists naturally and has many practical applications. The problem is shown to be NP-complete in the strong sense. We develop a branch and bound algorithm and a heuristic to solve the problem. We conduct the computational experiments to test the performances of the algorithms. A strong lower bound is derived for the branch and bound algorithm that can efficiently solve 15 jobs problem for about 5 minutes. The heuristic is shown to be efficient and effective, which can solve the problem of 150 jobs for about 20 seconds and provide near-optimal solution. We justify that the heuristic is an excellent solution approach for large problem instances. We also show that four special cases are either polynomial solvable or NP-complete in the ordinary sense.     

Top manager and network effects on the adoption of innovative management practices: a study of TQM in a public hospital system

This paper reports findings from a study that combined two theoretical perspectives—top manager and network/institutional—to examine the factors influencing organizations to adopt innovative management practices. The study setting was a system of public hospitals and the innovation was Total Quality Management (TQM). Study results indicate that both top manager and network/institutional factors are important determinants of whether and when organizations adopt innovations. However, as predicted, the relative importance of these two sets of factors appears to change as an innovation becomes more widely diffused. Copyright © 2001 John Wiley & Sons, Ltd.     

Regulating network communication in Asia: a different balancing act?

Because of often conflicting policy objectives, whether, and how, to regulate network communication has become an art of seeking the best balance. One of the most notable controversies has been balancing between freedom of expression and the need to protect the public from harmful materials. As policy objectives and priorities are set by the political, economic, social and cultural contexts in which laws and regulations are formulated and carried out, policymakers may be caught by different sets of conflicting concerns and end up with solutions which are unique to a particular situation. Using China and Singapore, this article examines two East Asian examples of balancing between economic growth, national sovereignty and social harmony, and the implications of such measures for policy research.     

Optimal flow-line conditions with worker variability

When classical scheduling theory is applied to task sequencing problems, human characteristics are often not the primary consideration and their impact is often assumed away. In this paper, we examine the workforce scheduling problem in sequential flow line systems. We investigate the impact of within-worker and between-worker variability and discuss the selection of scheduling policies between fixed and work-sharing systems. The methodology includes human performance modeling with the objective to maximize throughput with general results with respect to productivity model. We are interested in determining and characterizing the optimal switching time between workers in work-sharing systems. A closed form solution is established in the case of two and three tasks with two workers. For a general number of workers and tasks we establish the maximum number of changes between assignments. We also establish a bound on throughput. Results allow one to solve workforce scheduling problems reduced complexity given the current set of bounds and conditions.