Abstract
This work addresses the energy-efficient job shop scheduling problem and transport resources with speed scalable machines and vehicles which is a recent extension of the classical job shop problem. In the environment under consideration, the speed with which machines process production operations and the speed with which vehicles transport jobs are also to be decided. Therefore, the scheduler can control both the completion times and the total energy consumption. We propose a mixed-integer linear programming model that can be efficiently solved to optimality for small-sized problem instances. © 2021, IFIP International Federation for Information Processing.

Abstract
This work considers sustainable scheduling of manufacturing operations and preventive maintenance activities in a single-machine environment where the machine works continuously in three eight-hour shifts per day. The jobs can be produced at different processing speeds, which reduces energy consumption and/or processing times. In a tri-objective mixed integer linear programming model, sustainability is attained through minimizing total weighted earliness/ tardiness - economic pillar, total energy consumption - environmental pillar, and number of undesired activities - social pillar. Moreover, a multi-objective genetic algorithm finds near optimal solutions in a timely manner. Numerical results will be presented at the conference.

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