Solving a production routing problem of perishable goods by considering the time window of delivery and lost sale

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Solving a production routing problem of perishable goods by considering the time window of delivery and lost sale

ارائه دهنده: Provider: Hamed Fatahi

اساتید راهنما: Supervisors: Dr Amirsaman Khairkhah

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr Samouei-Dr Dezfoolian

زمان و تاریخ ارائه: Time and date of presentation: 2024

مکان ارائه: Place of presentation: 62

چکیده: Abstract: n the management of any supply chain, attention to costs is very important and there is always efforts to identify and reduce costs. One of the issues that pay attention to this is the production routing problem (PRP), which integrates two known problems of determining the accumulation and routing of vehicles to reduce supply chain costs. As expected, the supply chain has a different mechanism in supply, production, maintenance and distribution of perishable goods. In the real world, not delivering timely delivery can impose the cost of falling or lost. Now, if the product is perishable, there will be another cost as the cost of corruption. Therefore, in this study, it has been attempted to provide a mathematical model in which a production center produces several perishable goods and the homogeneous transport fleet under the maximum constraint of travel time is tasked with distributing this. Is in charge of the goods. Due to this issue in the category of NP-Hard issues, the problem was first resolved in small dimensions using GAMS software and validated. In large dimensions, the problem is solved by using genetic meta -metric algorithms and colonial competition. Using the Taguchi test method, these algorithms were adjusted to determine their appropriate surfaces. 9 issues have been randomly produced in various dimensions, which can be resolved by the genetic algorithm and the colonial competition algorithm. To examine the performance of the algorithms used, the quality of the algorithms produced by each performance was compared to each other, which produces better answers in large dimensions of genetic algorithm, but there is little difference between their answers. In another comparison, the two algorithms were compared from the perspective of the time to reach the answer, which in large dimensions of the genetic algorithm performs better performance and are close to the small dimensions of solving times.

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