A Hybrid Multi-objective Algorithm for Energy-Efficient Scheduling Considering Machine Maintenance

The hybrid flow shop (HFS) problem is solved in this study through composite dispatching rules (CDRs) generated automatically. CDR is a simple approach to solve the HFS problem rapidly and easily. Genetic programming (GP) is the most common methodology to generate CDRs to solve combinational optimization problems. The disruption in the main operations of GP, such as crossover and mutation, is high. Scatter programming is proposed in this paper to generate CDRs with improved scalability and flexibility for HFS problems. The proposed algorithm includes a novel local improvement method, one-point traversal shaking search, to accelerate convergence. Simulation studies demonstrate that the proposed algorithm outperforms genetic programming and scatter programming with shaking. The dispatching rules generated automatically can also be reused in similar instances and yield better results than existing dispatching rules. The rate of convergence of the proposed algorithm is higher than that of scatter programming with shaking.

This paper considers the hybrid flow shop scheduling problem, where jobs are processed in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> stages with the same route of the stage. Each stage has identical parallel machines for processing jobs. Some mathematical programming formulations and lower bound calculations have been proposed in the literature for such cases. Nevertheless, there is a lack of complete comparisons of these mathematical programming formulations and lower bounds in the hybrid flow shop literature. In this paper, we propose a new mixed integer programming model and two new lower bounds based on the bin-packing concept for the considered problem. To evaluate the proposed model, two sets of small and small-to-medium problems are used to compare our model with the existing models. Moreover, two propositions are discussed for lower bounds. The experimental results show that the proposed mixed integer programming model efficiently found optimal solutions because it needs a smaller number of binary variables and a smaller number of constraints, and the proposed lower bound can also serve as a strong indicator to evaluate the distances between the solutions obtained by heuristic algorithms and the optimal solution.

In recent years, people have paid more and more attention to traditional manufacturing's environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz-Enscore-Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model's effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.

Dynamic Rescheduling Expert System for Hybrid Flow Shop with Random Disturbance

Bi-objective unrelated parallel machine joint scheduling of jobs and preventive maintenance with a dynamic speed-scaling technique

Nodes in the wireless sensor networks are battery operated, and thus, the efficient use of the node's energy during wireless communication is pivotal for the long battery life. This paper addresses the challenges in energy-efficient data aggregation and transmission scheduling for each node by using time division multiple access (TDMA). We introduce multi-channel TDMA scheduling algorithms with the objective of minimizing the total energy consumption in the network. The proposed algorithms utilize multiple radio channels to bestow efficient scheduling while eliminating collisions and overhearing. First, we formulate an integer linear programming (ILP) algorithm that finds the minimum bound for the network's energy consumption. Then, we propose a near-optimal heuristic algorithm based on backtracking, which uses memoization to spurn the suboptimal schedules. Subsequently, we propose a computationally efficient heuristic algorithm by using Langford subset generation. The algorithm reduces the energy consumption in each timeslot while avoiding revisiting the same timeslot. We conducted extensive simulations to evaluate three proposed algorithms. The simulation results demonstrate that the proposed heuristic algorithms provide performance comparable to the optimum results of the ILP algorithm and reduce the magnitude of computation time.

Genetic programming hyper-heuristic-based solution for dynamic energy-efficient scheduling of hybrid flow shop scheduling with machine breakdowns and random job arrivals

Reformulation, linearization, and a hybrid iterated local search algorithm for economic lot-sizing and sequencing in hybrid flow shop problems

The problem of optimal scheduling of construction production is particularly important in the case of production processes of concrete precast structural elements. The most important features characterizing this type of construction production are as follows: - the necessity to produce precast elements in factory conditions in order to make the production process independent of the weather conditions, - the repeatability of production processes of precast elements. The paper presents a model for scheduling of the production process of concrete precast structural elements. The model is based on the hybrid flow shop problem (HFS problem), in which it is possible to include more than one device/equipment or group of workers performing one type of production process. The model respects the constraints that are characteristic of the production processes of such elements. The issue of discrete optimization, presented in the paper, is NP-hard. Therefore, the authors proposed the original solution of an optimization task in the model using the simulated annealing metaheuristic algorithm. The application of the presented scheduling model is illustrated by a practical calculation example showing the effectiveness of the used optimization algorithm. The model can be successfully used to solve planning tasks of the production process in plants producing concrete precast elements in order to significantly shorten the duration of production process.

Production scheduling that involves distributed factories, machine maintenance, and resource constraints plays a crucial role in manufacturing. However, these realistic constraints have rarely been considered simultaneously in the hybrid flow shop (HFS). To address this issue, a distributed resource-constrained hybrid flow shop scheduling problem with machine breakdowns (DRCHFSP-MB) is studied. There are two optimization objectives, i.e., makespan and total energy consumption (TEC). To solve the strongly NP-hard problem, a mathematical model is established and a block–neighborhood-based multi-objective evolutionary algorithm (BNMOEA) is developed. In the proposed algorithm, an efficient hybrid initialization method is adopted to obtain high-quality individuals to participate in the evolutionary process of the population. Next, to enhance the search capability of the BNMOEA, three well-designed crossover operators are used in the global search. Then, the convergence of the proposed algorithm is improved by utilizing eight critical factory-based local search operators combined with block–neighborhood. Finally, the BNMOEA is compared with several of the most advanced multi-objective algorithms; the results indicate that the BNMOEA has an outstanding performance in solving DRCHFSP-MB.

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Modern large-scale steelmaking and continuous casting is usually composed of multiple converters, refining furnaces, continuous casting machines, multi-stage refining and reentrant line, which leads to the difficult scheduling. A mixed integer programming model is established to minimize the waiting time for all charges, and a variable neighborhood search algorithm is proposed to solve the problem. In order to improve the quality of the initial scheduling solution, heuristic scheduling rules are used to generate the initial solution. Six different neighborhood structures are designed and are constructed to generate the neighborhood solution to ensure the feasibility of neighborhood solution and improve search efficiency. On this basis, a scheduling method for steelmaking and continuous casting production is designed based on heuristic algorithm and variable neighborhood search algorithm. Simulation results show that the proposed method can obviously shorten the waiting time by using actual steelmaking and continuous casting production data.

We give an efficient algorithm for solving the following scheduling problem to optimality: Assign n jobs to m processors such that they all meet a common deadline T and energy consumption is minimized by appropriately controlling the clock frequencies of the processors. Jobs are malleable, i.e., their amount of parallelism can be flexibly adapted. In contrast to previous work on energy efficient scheduling we allow more realistic energy consumption functions including a minimum and maximum clock frequency and a linear term in energy consumption. We need certain assumptions on the speedup function of the jobs that we show to apply for a large class of practically occurring functions.KeywordsSchedule ProblemEnergy FunctionEnergy UsageFrequency ScalingBend PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In view of the joint optimization problem of preventive maintenance and production scheduling for modern production systems under time-of-use tariffs, a two-stage joint decision-making policy is proposed to achieve the peak-shifting reduction of production power. In the first stage, a dynamic preventive maintenance schedule is sequentially obtained based on the availability of machine. In the second stage, the production scheduling optimization of multi-workpiece processing is further carried out. The power consumption cost and the delay penalty cost under the time-of-use electricity tariff are considered, and the mixed integer programming model is established to achieve the balance of energy consumption and production delay. Numerical experiments have shown that by reasonably planning the idle time at the time of production batch conversion, the proposed model can effectively shift the on-peak power demand to off-peak, meet the stable electricity demand of enterprises, and improve the sustainable utilization level of power.

This paper considers the characteristics of hybrid flow shop with energy saving. First of all, established the model of hybrid flow shop with energy saving problem. Then Lagrangian relaxation was proposed to solve the energy scheduling problem in hybrid flow shop. Lagrangian relaxation algorithm introduced precedence constraints into the objective function, and the original problem was decomposed into a series of parallel machine sub-problems. A dynamic programming algorithm was then designed to solve these sub-problems. The method of updating of multiples was sub-gradient algorithm. Lastly, a two stage heuristic approach was constructed to convert the infeasible solution into a feasible solution. Testing results demonstrated that the proposed method can generate near optimal schedules in an acceptable computational time.