Abstract

Abstract
This paper addresses the joint scheduling of production operations, transport tasks, and storage/retrieval activities in flexible job shop systems where the production operations and transport tasks can be done by one of the several resources available. Jobs need to be retrieved from storage and delivered to a load/unload area, from there, they are transported to and between the machines where their operations are processed on. Once all operations of a job are processed, the job is taken back to the load/unload area and then returned to the storage cell. Therefore, the problem under study requires, concurrently, solving job routing, machine scheduling, transport allocation, vehicle scheduling, and shuttle schedule. To this end, we propose a hybrid biased random-key genetic algorithm (BRKGA) in which the mutation operator resorts to six local search heuristics. The computational experiments conducted on a set of benchmark instances show the effectiveness of the proposed mutation operator.

Abstract
This article addresses the significant issue of job deterioration effects in job-shop scheduling problems and aims to create awareness on its impact within the manufacturing industry. While previous studies have explored deteriorating effects in various production configurations, research on scheduling problems in complex settings, particularly job-shop, is very limited. Thus, we address and optimize the impact of job deterioration in a generic job-shop scheduling problem (JSP). The JSP with job deterioration is harder than the classical JSP as the processing time of an operation is only known when the operation is started. Hence, we propose a biased random key genetic algorithm to find good quality solutions quickly. Through computational experiments, the effectiveness of the algorithm and its multi-population variant is demonstrated. Further, we investigate several deterioration functions, including linear, exponential, and sigmoid. Job deterioration increases operations' processing time, which leads to an increase in the total production time (makespan). Therefore, the management should not ignore deterioration effects as they may lead to a decrease in productivity, to an increase in production time, costs, and waste production, as well to a deterioration in the customer relations due to frequent disruptions and delays. Finally, the computational results reported clearly show that the proposed approach is capable of mitigating (almost nullifying) such impacts. © 2023 IEEE.

Abstract
This paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power-flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi-objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small-sized instances. We also proposed a multi-objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade-off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4-6% increase), while at the same time allowing for a small reduction in energy consumption.

Abstract

Abstract
The Unit Commitment (UC) problem is a wellknown combinatorial optimization problem arising in operations planning of power systems. It is typically formulated as nonlinear mixed-integer programming problem and has been solved in the literature by a huge variety of optimization methods, ranging from exact methods (such as dynamic programming, branch-and-bound) to heuristic methods (genetic algorithms, simulated annealing, particle swarm). Here, we start by formulating the UC problem as a mixed-integer optimal control problem, with both binary-valued control variables and real-valued control variables. Then, we use a variable time transformation method to convert the problem into an optimal control problem with only real-valued controls. Finally, this problem is transcribed into a finite-dimensional nonlinear programming problem to be solved using an optimization solver.