997 resultados para Group shop
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Three types of shop scheduling problems, the flow shop, the job shop and the open shop scheduling problems, have been widely studied in the literature. However, very few articles address the group shop scheduling problem introduced in 1997, which is a general formulation that covers the three above mentioned shop scheduling problems and the mixed shop scheduling problem. In this paper, we apply tabu search to the group shop scheduling problem and evaluate the performance of the algorithm on a set of benchmark problems. The computational results show that our tabu search algorithm is typically more efficient and faster than the other methods proposed in the literature. Furthermore, the proposed tabu search method has found some new best solutions of the benchmark instances.
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For the shop scheduling problems such as flow-shop, job-shop, open-shop, mixed-shop, and group-shop, most research focuses on optimizing the makespan under static conditions and does not take into consideration dynamic disturbances such as machine breakdown and new job arrivals. We regard the shop scheduling problem under static conditions as the static shop scheduling problem, while the shop scheduling problem with dynamic disturbances as the dynamic shop scheduling problem. In this paper, we analyze the characteristics of the dynamic shop scheduling problem when machine breakdown and new job arrivals occur, and present a framework to model the dynamic shop scheduling problem as a static group-shop-type scheduling problem. Using the proposed framework, we apply a metaheuristic proposed for solving the static shop scheduling problem to a number of dynamic shop scheduling benchmark problems. The results show that the metaheuristic methodology which has been successfully applied to the static shop scheduling problems can also be applied to solve the dynamic shop scheduling problem efficiently.
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This paper studies the problem of scheduling jobs in a two-machine open shop to minimize the makespan. Jobs are grouped into batches and are processed without preemption. A batch setup time on each machine is required before the first job is processed, and when a machine switches from processing a job in some batch to a job of another batch. For this NP-hard problem, we propose a linear-time heuristic algorithm that creates a group technology schedule, in which no batch is split into sub-batches. We demonstrate that our heuristic is a -approximation algorithm. Moreover, we show that no group technology algorithm can guarantee a worst-case performance ratio less than 5/4.
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Do you approach shopping as if it is a competitive, sport-like activity? Or perhaps you attach social or emotional value in being recognised by your friends and peers as a great shopper, a proficient or efficient shopper? If so, you could be a “sport shopper”, according to an academic paper presented at an international conference earlier this year. This shopper can recount in detail where and when they purchased items - and most importantly, how much they saved. For this shopper, it is not about spending the least, but saving the most. This new “type” should not be confused with the economic shopper; constrained financially and forced to seek out low prices and generic products. And they are definitely not the recreational shopper, who enjoys shopping as a fun activity, engaging in the task to reduce stress and seek pleasure.
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Australian shoppers are predicted to spend nearly A$47 billion this Christmas. But retailers have to work harder and harder to get shoppers to pull out their wallets. Here are five strategies retailers will be using to get us to part with our hard earned coin.
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In this paper, we consider the problem of providing flexibility to solutions of two-machine shop scheduling problems. We use the concept of group-scheduling to characterize a whole set of schedules so as to provide more choice to the decision-maker at any decision point. A group-schedule is a sequence of groups of permutable operations defined on each machine where each group is such that any permutation of the operations inside the group leads to a feasible schedule. Flexibility of a solution and its makespan are often conflicting, thus we search for a compromise between a low number of groups and a small value of makespan. We resolve the complexity status of the relevant problems for the two-machine flow shop, job shop and open shop. A number of approximation algorithms are developed and their worst-case performance is analyzed. For the flow shop, an effective heuristic algorithm is proposed and the results of computational experiments are reported.
Resumo:
We consider the problem of scheduling families of jobs in a two-machine open shop so as to minimize the makespan. The jobs of each family can be partitioned into batches and a family setup time on each machine is required before the first job is processed, and when a machine switches from processing a job of some family to a job of another family. For this NP-hard problem the literature contains (5/4)-approximation algorithms that cannot be improved on using the class of group technology algorithms in which each family is kept as a single batch. We demonstrate that there is no advantage in splitting a family more than once. We present an algorithm that splits one family at most once on a machine and delivers a worst-case performance ratio of 6/5.
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Purpose: The purpose of this paper is to identify factors that facilitate tacit knowledge sharing in unstructured work environments, such as those found in automated production lines. Design/methodology/approach: The study is based on a qualitative approach, and it draws data from a four-month field study at a blown-molded glass factory. Data collection techniques included interviews, informal conversations and on-site observations, and data were interpreted using content analysis. Findings: The results indicated that sharing of tacit knowledge is facilitated by an engaging environment. An engaging environment is supported by shared language and knowledge, which are developed through intense communication and a strong sense of collegiality and a social climate that is dominated by openness and trust. Other factors that contribute to the creation of an engaging environment include managerial efforts to provide appropriate work conditions and to communicate company goals, and HRM practices such as the provision of formal training, on-the-job training and incentives. Practical implications: This paper clarifies the scope of managerial actions that impact knowledge creation and sharing among blue-collar workers. Originality/value: Despite the acknowledgement of the importance of blue-collar workers' knowledge, both the knowledge management and operations management literatures have devoted limited attention to it. Studies related to knowledge management in unstructured working environments are also not abundant. © Emerald Group Publishing Limited.
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Includes index.
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Includes index.