Development of an integrated model for production scheduling and vehicle routing in a multi-factory system considering energy efficient strategies

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Development of an integrated model for production scheduling and vehicle routing in a multi-factory system considering energy efficient strategies

ارائه دهنده: Provider: Amirreza Ghiasi

اساتید راهنما: Supervisors: Amirsaman Kheirkhah

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Javad Behnamian - Hamidreza Dezfoolian

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

مکان ارائه: Place of presentation: 62

چکیده: Abstract: In this research, an integrated production and distribution scheduling problem with energy considerations is examined. A set of jobs (customer orders) must be allocated among factories, and the jobs are processed in flow shop environments at the corresponding factories. Completed jobs must be delivered to customers in different regions using capacitated vehicles, ensuring that the delivery time window is met as closely as possible. In the production phase, machines operate under discrete speed modes, where lower speed levels consume less energy and vice versa. In the distribution phase, the energy consumed by a vehicle varies depending on the load it carries. Operational decisions in the production section include allocating orders to each factory, determining the processing sequence of the orders on the machines within each factory, and selecting the speed mode for each machine to process a job. In the distribution section, decisions must be made regarding packaging, loading, and the sequence in which each customer is visited by the vehicles. To optimally solve the proposed problem, a mixed-integer linear programming (MILP) model was developed with the objective of minimizing the costs associated with the production and distribution system, penalties for early or late deliveries outside the specified time window, and energy consumption costs. A numerical example was solved using the GAMS software with the GUROBI solver. Additionally, to address medium and large-sized instances within a reasonable time, two metaheuristic algorithms, Genetic Algorithm (GA) and Memetic Algorithm (MA), were proposed. The results showed that the Memetic Algorithm provided better solutions compared to the Genetic Algorithm, though the Genetic Algorithm had a shorter runtime than the Memetic Algorithm.