Production Scheduling Activities Research Articles

Improving Delivery Performance in High-Mix Low-Volume Manufacturing by Model-Based and Data-Driven Methods

In a high-mix and low-volume (HMLV) manufacturing environment where demand fluctuation is the rule rather than the exception, daily production management in face of conflicting key performance indicators such as high delivery precision, short lead time, and maximal resource utilization is a most challenging task. This situation may even be hampered by unreliable supplier performance. This paper presents a generic decision support workflow, which first identifies the most critical external and internal factors which have a serious impact on delivery performance. Next, it suggests a method which combines traditional manufacturing system simulation with advanced machine learning techniques to support the improved daily routine lot-sizing and production scheduling activities in a HMLV company. Argumentation is motivated and illustrated by a detailed industrial case study.

An Optimization Tool for Production Planning: A Case Study in a Textile Industry

The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete Event Simulation (DES) and optimization through genetic algorithms. The focus is on production planning for weaving processes and optimization to help make decisions about batch sizing and production scheduling activities. In addition, the correlations between some current technological trends and their implications for the textile industry are also highlighted. Another important contribution of this study is to detail the use of the commercial software Tecnomatix Plant Simulation 13®, to simulate and optimize a production problem by applying genetic algorithms with real production data.

An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem

Purpose With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed. Design/methodology/approach After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization. Findings Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence. Research limitations/implications This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown. Practical implications IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum. Social implications This research provides an efficient scheduling method for solving the FJSP problem. Originality/value This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

Hierarchical scheduling and disturbance management in the process industry

The integration of scheduling and control in the process industry is a topic that has been frequently discussed during the recent years, but many challenges remain in order to obtain integrated solutions that may be implemented at large-scale industrial sites. This paper introduces a general framework for production scheduling (PS) and detailed production scheduling (DPS) using a two-level hierarchical approach. The PS activity generates a monthly production schedule based on information on orders and forecasts, and the DPS activity handles disturbances in production on an hourly basis. The focus is on disturbances in the supply of utilities, which often cause great losses at process industrial sites. The research has been conducted in close collaboration with Perstorp, a world-leading company within several sectors of the specialty chemicals market. A specification list provided by Perstorp has been used as a starting point for formulating the PS and DPS activities as optimization problems. An example that is inspired by a real industrial site is presented to show how the PS and DPS may operate and how the integration of these two functions behaves.

A FRAMEWORK OF SCHEDULING MODELS IN MACHINING WORKSHOP FOR GREEN MANUFACTURING

Reducing environmental impacts and resource consumption in manufacturing processes is one of the important issues in green manufacturing. However, this attention has focused primarily on the activities of the design and the process planning, and the production scheduling activity has been overlooked especially for discrete manufacturing systems such as machining workshop. This paper presents a framework of the scheduling models in machining workshop that integrated resource and environmental considerations into production scheduling activities. The scheduling framework builds upon the schematic model and the analysis of resource and environmental dimensions of machining processes, and is used to guide the scheduling model construction which seeks to reduce resource consumption and environmental impacts at the operational scheduling level. The example is presented to demonstrate the application of the framework.

Impact of forecasting error on the performance of capacitated multi-item production systems

A computer model is built to simulate master production scheduling activities in a capacitated multiitem production system under demand uncertainty and a rolling time horizon. The output from the simulation is analyzed through statistical software. The results of the study show that forecasting errors have significant impacts on total cost, schedule instability and system service level, and the performance of forecasting errors is significantly influenced by some operational factors, such as capacity tightness and cost structure. Furthermore, the selection of the master production schedule freezing parameters is also significantly influenced by forecasting errors. The findings from this study can help managers optimize their production plans by selecting more reasonable forecasting methods and scheduling parameters, thus improving the performance of production systems.

Freezing the master production schedule under single resource constraint and demand uncertainty

This paper investigates the impact of freezing the master production schedule (MPS) in multi-item single-level systems with a single resource constraint under demand uncertainty. It also examines the impact of environmental factors on the selection of MPS freezing parameters. A computer model is built to simulate master production scheduling activities in a multi-item system under a rolling time horizon. The result of the study shows that the parameters for freezing the MPS have a significant impact on total cost, schedule instability and the service level of the system. Furthermore, the selection of freezing parameters is also significantly influenced by some environmental factors such as capacity tightness and cost structure. While some findings concerning the performance of MPS freezing parameters without capacity constraints can be generalised to the case of limited capacity, other conclusions under capacity constraints are different from those without capacity constraints.

A greedy algorithm for scheduling tasks on production lines

Production scheduling activities allocates tasks to achieve efficient or optimal utilization of the system configuration. Due to the large combinatorial complexity of most real-world scheduling and resource allocation problems, true optimization is very difficult, requiring the consideration of numerous constraints and objectives. Heuristic solution methods are concerned to minimize the total project duration, without violate the priority constraints. The greedy algorithm presented in this paper uses a systematic partition of the tasks to be executed, scheduling tasks and communication loads to the production lines. The algorithm is suitable on solving problems with arbitrary execution and transmission times and is applicable on systems with arbitrary topologies of the communication network, scheduling hundreds of tasks on a limited number of production lines.The paper uses terms of graph theory to modelize the scheduling problem and is completed with a numerical example of a small task graph. The effectiveness of the proposed algorithm is demonstrated through a comparison between scheduling results obtained with the proposed method and those obtained with sequential and random methods.

A rule-based master production scheduling system for an electro-mechanical manufacturing company

It is a hard process to modify master production schedule in a firm having various types of products and final assembly groups. Therefore, the system should automatically make the needed and proper modifications when they are input. Besides, it is of great importance to the firm to remove the errors of demand forecasting so as not to reject customer orders. In this study, a rule-based system for an electromechanical manufacturing company, which is able to rearrange the master production schedule, is presented. This system will provide considerableeasinessofusein themaster production scheduling activities.