This article studies logical reconfiguration of reconfigurable manufacturing systems (RMS) to minimise production lead times and buffer inventory level when the process variations and worker utilisation are considered...
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This article studies logical reconfiguration of reconfigurable manufacturing systems (RMS) to minimise production lead times and buffer inventory level when the process variations and worker utilisation are considered. Since the RMS must be flexible for different job orders, the design of RMS requires diagnostic methodology and stream of variations (SoV) theory for rapid ramp-up in order to control the process variations that might occur as time goes on. The flexibility of the manufacturing systems is represented by logical elements of RMS in terms of changeable production batch size. The three phases solution is proposed by (1) utilising SoV modelling to find the allowable production lead times, (2) finding the optimum buffer stock level and production capacity at changeable production batch size and (3) finding worker routings at optimum worker utilisation. Monte carlo simulation is employed at Phase 1 to get the optimum production lead times, Phase 2 decision is formulated as a stochastictwo-stages programming where buffer inventory level (first stage decison) has to be established prior to changeable production batching at future period and shortest path problems (SPP) algorithm is used to find an optimum worker routing at Phase 3. A serial inventory production (SIP) is used as an example to answer the following research questions: (1) What is the impact of SoV on both buffer inventory allocation and worker routings? (2) When is logical reconfiguration most beneficial in improving SIP profitability? (3) What is the impact of logical reconfiguration on both cost and lead time reduction? Three instances are used to investigate the effect of logical reconfiguration on the different structure of SIP systems. The results and analysis indicate that consideration of SoV is capable of increasing the profit, reducing operation lead times and maximising worker utilisation. Finally, management decision-making is discussed among other concluding remarks.
The air transport industry is an important branch of the intelligent transportation system (ITS). It is widely admitted that modern ITS technologies and advanced management methods, such as fleet assignment, aircraft ...
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The air transport industry is an important branch of the intelligent transportation system (ITS). It is widely admitted that modern ITS technologies and advanced management methods, such as fleet assignment, aircraft maintenance routing, and crew scheduling, can significantly increase an airline's market share and profit, and also improve customer satisfaction. This paper studies a new airline stochastic fleet assignment problem with random passenger demands under risk aversion. The objective is to maximize the expected total profit at a certain level of risk avoidance (i.e., conditional value-at-risk). To solve this problem, we present a risk-averse two-stagestochastic mixed-integer programming model. The first stage mainly deals with tactic level decisions: assigning aircraft families (e.g., Airbus A380 family) to flight legs. The operational level decisions are made in the second stage to efficiently assign aircraft types (e.g., Airbus A380-800 or A380-800F) to flight legs while meeting the family assignment plan developed in the first stage. Then, a sample average approximation algorithm is proposed to solve the stochasticprogramming problem considering risk aversion. A realistic international airline's numerical experiment is conducted to illustrate the efficiency of the proposed two-stagestochasticprogramming model and algorithm.
To alleviate the range anxiety of drivers and time-consuming charging for electric buses (eBuses), opportunity fast-charging has gradually been utilized. Considering that eBuses have operational tasks, identifying an ...
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ISBN:
(数字)9783030859060
ISBN:
(纸本)9783030859060;9783030859053
To alleviate the range anxiety of drivers and time-consuming charging for electric buses (eBuses), opportunity fast-charging has gradually been utilized. Considering that eBuses have operational tasks, identifying an optimal charging scheduling will be needed. However, in the real world, arrival time and state of charge (SOC) of eBuses are uncertain. Therefore, it is challenging for the charging station to efficiently schedule charging tasks. To solve the problem, this paper develops a two-stagestochastic eBus charging scheduling model. In the first stage, eBuses are assigned to designated chargers. After the arrival time and SOC are realized, the second stage determines the charging sequence of eBuses on each charger. The objective is to minimize the penalty cost of tardiness by determining the charging start time and the corresponding charging duration time. Then, a sample average approximation (SAA) algorithm is applied. Additional numerical experiments are performed to verify the efficiency of the stochasticprogramming model and algorithm.
Bakalářská práce se zabývá optimalizací kapacity kotle spalovny odpadů, a to z jeho ekonomie provozu. V práci byl vytvořen model spalovny. Model byl aproximován diskretizací...
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Bakalářská práce se zabývá optimalizací kapacity kotle spalovny odpadů, a to z jeho ekonomie provozu. V práci byl vytvořen model spalovny. Model byl aproximován diskretizací, protože se v modelu spalovny vyskytují náhodné parametry s normálním rozdělením. K posouzení kvality aproximace byly vytvořeny intervaly spolehlivosti na základě aplikace náhodného výběru, metody Sled nezávislých náhodných čísel (IRN z angl. independent random number streams) a metody Sled společných náhodných čísel (CRN z angl. common random number streams). V závěru této práce byly použité metody porovnány a navržen minimální počet scénářů, který „dostatečně dobře“ úlohu aproximuje.
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