Abstract

Abstract

Abstract
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions. © 2018 Springer Science+Business Media, LLC, part of Springer Nature

Abstract
This paper addresses a distribution problem involving a set of different products that need to be distributed among a set of geographically disperse retailers and transported from the single warehouse to the aforementioned retailers. The disfribution and transportation are made in order to satisfy retailers' demand while satisfying storage limits at both the warehouse and the retailers, transportation limits between the warehouse and the retailers, and other operational constraints. This problem is combinatorial in nature as it involves the assignment of a discrete finite set of objects, while satisfying a given set of conditions. Hence, we propose a genetic algorithm that is capable of finding good quality solutions. The genetic algorithm proposed is used to a real case study involving the disfribution of eight products among 108 retailers from a single warehouse. The results obtained improve on those of company's current practice by achieving a cost reduction of about 13%.

Abstract