Makespan with plant simulation
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Makespan with plant simulation plus#
Law (Conservation of Material): In a stable system, over the long run, the rate out of a system will equal the rate in, less any yield loss, plus any parts production within the system. TimeĬorollary (Buffer Flexibility): Flexibility reduces the amount ofvariability buffering required in a production system. Law (Variability Buffering): Variability in a production system will be buffered by some combination of 1.
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Corollary (Variability Placement): In a line where releases are independent of completions, variability early in a routing increases cycle time more than equivalent variability later in the routing.
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Law (Variability): Increasing variability always degrades the peljormance ofa production system. Law (CONWIP with Flexible Labor): In a CONWIP line with n identical workers and w jobs, where w 2: n, any policy that never idles workers when unblocked jobs are available will achieve a throughput level TH(w) bounded by THew(n) :::: TH(w) :::: THew(w) where THew (x) represents the throughput ofa CONWIP line with all machines staffed by workers and x jobs in the system. Law (Labor Capacity): The maximum capacity ofa line staffed by n cross-trained operators with identical work rates is n THmax =. The PWC throughput for a given WIP level w is given by THpwe = The worst-case throughput for a given WIP level w is given by 1 THworst = -ĭefinition (Practical Worst-Case Performance): The practical worst-case (PWC) cycle time for a given WIP level w is given by w -1 CTpwe = To+-rb Law (Worst-Case Performance): The worst-case cycle time for a given WIP level w is given by CTworst = w To The maximum throughputfor a given WIP level w is given by THbest = Law (Best-Case Performance): The minimum cycle time for a given WIP level w is given by For facilitating effective trade-off decision-making, two different MO approaches are implemented and tested within MOGA: a weighted-sum based approach and a Pareto-based approach.FindingsExperiments over a set of fuzzified test problems show the effect of these approaches on the performance of MOGA while verifying its efficiency in terms of both solution and time quality.Originality/valueTo the author’s knowledge, no previous published work in the literature has studied the biobjective assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times.Factory Physics Principles Law (Little's Law): WIP=THxCT MOGA is devoted to the search for Pareto-optimal solutions. Then, we present a multiobjective genetic algorithm (MOGA) to solve the problem. Two criteria are simultaneously considered for minimization, namely, fuzzy cycle time and fuzzy smoothness index.Design/methodology/approachFirst, we show how fuzzy concepts can be used for managing uncertain task times. This problem is an extension of the (simple) SALBP-2 in which task times are worker-dependent and concurrently uncertain. PurposeThis paper considers the assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times. Finally, a relatively better improvement plan is determined, which can better achieve the goal of balancing production line, improving efficiency, and reducing cost. Applying theoretical knowledge such as production line balancing, lean production, motion study and time analysis, the improvement two plans are put forward, and the corresponding simulation results are given subsequently. By analyzing these results, the bottlenecks in the process are found out.
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Makespan with plant simulation software#
According to each activity, operation time and worker assignment of the garment manufacturing process, the corresponding simulation model based on Em-Plant software is created to reveal the current problems existing in the manufacturing process. This paper takes a garment manufacturing factory as the research object, and innovatively uses the MOST method and Em-Plant simulation to improve the efficiency of its production line. Garment manufacturers need to shorten the production cycle to win development opportunities. Quality and scale competition gradually turn into speed competition. In recent years, the way of competition has changed in terms of garment manufacturing.