Production Makespan Research Articles

Lot splitting scheduling procedure for makespan reduction and machine capacity increase in a hybrid flow shop with batch production

The general definition of the hybrid flow shop (HFS) environment is a set of S ≥ 2 production stages where at least one of these stages includes more than one machine, which can process one job at a time. A job can be defined as several operations to be performed by none, one, or more machines at each stage. Usually, these jobs are completed in some sequence between the different production stages, and in the case of setup activities, products are grouped in batches with buffers of work in progress between different production stages. Today, flexible production systems permit in some instances to relax job precedence constrains with alternative process cycles and to group together different batches of similar products in order to reduce setup activity incidence. On the other hand, the availability of multiple parallel machines in a single production stage makes it possible to split the lot size between different resources. This paper aims to solve the HFS scheduling problem in a flexible multistage batch production system, offering a heuristic procedure, to minimize the production makespan and increase the productive capacity utilization using a batch aggregation/splitting strategy while introducing the “workload leveling function” concept. The results are compared with other important scheduling rules widely accepted in the industry and made part of an industrial application. The company used as a test sample is an Italian rotor shaft manufacturer. The final result is illustrated to validate the proposed heuristics.

A genetic algorithm for one-job m-machine flowshop lot streaming with variable sublots

Lot streaming is a technique used to split the processing of lots (batches) into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production makespan. In this technique, a production lot may be split into equal, consistent or variable sublots. Recent literature shows that, when production setup time is considered, significant lead time improvement is possible if variable sublots are used. In this research, however, we noted that lot streaming problems with variable sublots are difficult to solve using off-shelf optimisation packages even for problems of smaller sizes. Thus, efficient solution procedures are needed for solving such problems. In this paper, we develop a hybrid genetic algorithm for a model that appeared in recent literature for one -job m -machine lot streaming problems with variable sublots and setup. Computational results showed that the performance of the proposed genetic algorithm is encouraging.

Lot Splitting and Optimization Strategy for Job-Shop Scheduling

To shorten makespan of Job-Shop production in addition to the optimization scheduling methods,lot splitting may also be utilized opportunely for further optimizing the production plan. A simplified lot splitting Job-Shop scheduling optimization solution is presented and the principle,location,impact on the project,the steps and methods are expounded. According to the restraint of the process route,state variables are used to describe the scheduling process,based on which,a heuristic algorithm is adopted for optimal scheduling. The longest critical path in the optimal scheduling is found out with a kind of algorithm. Three rules should be followed in lot splitting:① After optimizing scheduling first,splitting lot. ② Splitting lot should be chosen at such an equipment that the next equipment on its process route should have free time after finishing the task at the equipment. ③ The equipment -task and at least one of its follow-up processes are on the critical path. Comparing job-process waiting time and track back waiting time,the possible saving makespan between present process and the follow-up processes,the location and size of lot splitting are determined. The lot splitting and optimization reach the goal that the makespan is minimized.

A GA-based parameter design for single machine turning process with high-volume production

This paper presents a Parameter Design (PD) approach that provides near-optimal settings to the process parameters of a single lathe machine with high-volume production. Optimized process parameters include both machining parameters (cutting speed, feed rate, and depth of cut) and production parameters (material order size and inventory safety stock and reorder point). In high-volume production, machining parameters have amplified impacts on the machine performance in terms of productivity (cycle time), reliability (tool life), and product quality (surface finish). In addition, production parameters become critical in high-volume production since they directly impact the overall order fulfillment (production makespan and delivery reliability). Hence, this paper extends the conventional per-part machining cost model into a per-order production cost model by consolidating the production economics of both machining parameters and production controls. Discrete Event Simulation (DES) is utilized to capture the stochastic and dynamic production attributes and to transfer the static machine PD model into a dynamic PD-DES production model. The model is also utilized to accumulate the per-order running cost over production time while incorporating the impacts of process variability in tool life, labor efficiency, machining conditions, order lead time, and demand rate. Using the PD-DES model as a dynamic fitness function, a Simple Genetic Algorithm (SGA) is developed and applied to a CNC lathe machine to determine near-optimal settings to both machining and production process parameters so that the overall per-order production cost is minimized. Results showed effective SGA convergence profile with relatively low number of search generations. Sensitivity analysis with SGA parameters is conducted to demonstrate the search’s robustness. The benefits of the per-order cost model are illustrated by repeating the SGA solution using machine productivity as a fitness criterion. The new SGA solution resulted in a better productivity but at a higher per-order cost. Finally, the effectiveness of SGA search is illustrated by outperforming the solutions obtained from two-level and three-level full factorial designs.

GA-based resource-constrained flow-shop scheduling model for mixed precast production

Precast production tasks mainly consist of reinforcement cage prefabrication, form fabrication, installment of reinforcement cages, mosaic or tiling fitting, placing concrete, steam curing, cleaning and finishing operations. These tasks are generally arranged as a flow shop process, in which all the precast elements are processed in the same processing order through all the tasks. The precast production needs to take into account the impact of limited resources, including equipment and skilled labor, on production makespan. Moreover, to economically and effectively utilize these valuable resources, mixed production gains a potential advantage over the conventional separate production under resource constraints. A resource-constrained mixed-production flow-shop scheduling system is proposed in this paper. A genetic algorithm-based searching technique is adopted in the system to provide the optimal or near-optimal combination of production sequences, resource utilization, and minimum makespan in consideration of resource constraints and mixed production. Sensitivity analysis is further conducted to identify the impact of mixed production strategies on the overall precast production makespan.

The permutation flow shop problem with just in time production considerations

This research presents a variation to the permutation flow shop problem where Just In Time (JIT) production requirements are taken into account. The model developed in this research employs dual objectives. In addition to the traditional objective of minimizing the production makespan, minimization of Miltenburg's material usage rate is also incorporated. In this model, multiple units of any product are permitted in the production sequence. However, the minimization of material usage rates attempts to prevent batch scheduling of products and allows unit flow of products as required in demand flow manufacturing. A solution method is proposed for determining an optimal production sequence via an efficient frontier approach and Simulated Annealing (SA). Test problems and specific performance criteria are used to assess the solutions generated by the proposed method. Experimental results presented in this paper show that the use of the efficient frontier and SA provide solutions that approach the optimal solution for the performance measures used in this research.

Optimal Repetitive Scheduling Model with Shareable Resource Constraint

An optimal repetitive scheduling model for precast production with the consideration of resource constraints and resource sharing is proposed in this paper. A repetitive schedule, like precast production, needs to take into consideration the impact of limited resources, including equipment (such as cranes, forms, and steaming curing facilities) and skilled labor, on production makespan. Moreover, to economically and effectively utilize these valuable resources (such as cranes and work crews), it is worth taking resource sharing into consideration in precast production. A genetic algorithm-based searching technique is adopted to establish an optimal resource-constrained repetitive precast production scheduling system with the consideration of resource sharing. The system can effectively provide the optimal or near-optimal combination of production durations, resource amounts, and minimum makespan under the constraint of limited resources and with the consideration of resource sharing.

A mathematical model for job shop scheduling with multiple process plan consideration per job

In this paper, a mixed integer programming model is formulated for scheduling a set of jobs through a shop when each job is supplied or provided with multiple process plans or process routings. Simultaneous selection of a process plan for each job and the sequencing of the jobs through the machines in the shop based on the set of selected process plans is addressed. The procedure developed seeks to integrate the selection of machines for each job and the sequencing of jobs on each machine based on the objective of minimizing production makespan. the application of the procedure is demonstrated with an example problem. The following conclusions were drawn as a result of the research: (1) the procedure developed produces optimal or near optimal solution; (2) the benefit from the developed approach is that it allows a shop to adaptively select process plans for jobs to optimize on production makespan. By combining solution quality with scheduling flexibility and efficiency, the productivity of a shop can be greatly enhanced.

JIT sequencing for mixed-model assembly lines with setups using Tabu Search

This research presents a heuristic for sequencing mixed-model production schedules for assembly lines when JIT production is an objective, and setup requirements are present. The heuristic examines a sequence and determines an objective function value based upon the parts usage rate (Miltenburg 1989) and the number of setups involved. This sequence is then altered in the hope of finding a better sequence in terms of the objective function via Tabu Search. This technique isapplied toseveral problems, and the resulting sequences are simulated todetermine production performance measures of production makespan, system time and average WIP inventory level. The experiment shows that the multiple objectives of minimizing both parts usage rate and required setups can be addressed provided management has an understanding of the relative importance of usage rate and setups for their specific application.

Scheduling in a manufacturing shop with sequence-dependent setups

In this paper, the problem of scheduling multiple jobs in a flexible manufacturing cell with multiple machine stations is addressed. Due to the large capital investments that usually characterize flexible manufacturing systems (FMS), an area of control of great interest to system users is that of maximizing the system performance through the minimization of machine idle and setup times. The magnitude of total time spent on machine setups and idle times is influenced by the availability of jobs, job mix, similarities of jobs and job scheduling procedure used. Similar jobs on the same machine require less setup times. Similarly, the use of an adequate scheduling method also reduces total idle and setup times. Such reduction improves the flow times of jobs. In this paper, a heuristic algoritm for scheduling jobs with sequence dependent setup times in a FMS is presented. The measure of performance for evaluating schedule adequacy is the production makespan.

Material flow control in AGV/unit load based production lines

The use of automated guided vehicles (AGVs) as transporters and mobile workstations in automated assembly lines is on the increase. This is due in part to the flexibility AGVs introduce in the design of production lines. In this paper, models for the management and control of material flow through AGV based production lines are developed. The use of the models permits the line designer to optimally determine the launch times of successive unit loads of parts into the line to minimize production makespan while balancing between line idleness and the idleness of vehicles employed on the line. The use of the models for AGV based production line design purposes is demonstrated through example problems.

Production Scheduling of Flexible Manufacturing Systems

This paper deals with the production scheduling of a flexible manufacturing system which consists of machine tools, buffer storages, and material and cutting tool transportation systems. The parts are produced randomly, and alternative machine tools for each processing stage of parts are permitted. Each machine tool has a buffer of specified capacity and some particular cutting tools for common use are automatically delivered by a cutting tool transportation system. The problem is to determine the schedules of machining and transporting parts, and of transporting cutting tools simultaneously so as to minimize the makespan of production. A heuristic procedure is presented to obtain better schedules by using the decision rules. A new decision rule, named ESTA (Earliest Starting Time with Alternatives considered), is proposed to achieve a high utilization of the machine tools and the transportation systems. An experimental comparison is performed to investigate the effectiveness of the proposed decision rules through a case study.