Traditional Production Planning Research Articles

Risk Hedging for Production Planning

Traditional production planning is primarily a quantity or capacity decision, which must be made at the beginning of a planning horizon before production starts. Adding to this decision a real‐time control, a risk‐hedging strategy carried out throughout the horizon can better mitigate the risk involved in demand volatility. We demonstrate how this can be done in terms of jointly optimizing the capacity and the hedging decisions, addressing both the mean‐variance and the shortfall objectives. Solution techniques, results, and insights are highlighted. In particular, we illustrate that our approach readily accommodates data analytics and explicitly quantifies the improvement to the efficient frontier contributed by hedging.

Optimal Charge Planning Model of Steelmaking Based on Multi-Objective Evolutionary Algorithm

As having an important part of coordination control in steelmaking process, traditional production planning and scheduling technologies are developed with little consideration of the metallurgy mechanism, leading to lower feasibility for actual production. Based on current situation and requirements of steel plants, this paper focuses on the investigation of the charge plan from the view of metallurgy and establishes a charge planning model concerning the minimization of both the open order amount and the difference in due dates of the orders in each charge. A modified multi-objective evolutionary algorithm is proposed to solve the charge planning model of steelmaking process. By presenting a new fitness function, based on the rule of target ranking and introducing the Elitism strategy to construct the non-inferior solution set, the quality of solutions is improved effectively and the convergence of the algorithm is enhanced remarkably. Simulation experiments are carried out on the orders from actual production, and the proposed algorithm produces a group of optimized charge plans in a short time. The quality of the solutions is better than those produced by a genetic algorithm, modified partheno-genetic algorithm, and those produced manually to some extent. The simulation results demonstrate the feasibility and effectiveness of the proposed model and the algorithm.

Rolling-reactive Optimization of Production Processes in a Calcium Silicate Masonry Unit Plant Using Online Simulation

In case of concatenated, complex material flow structures such as those in calcium silicate masonry unit plants, plan deviations pose a major challenge for an efficient order processing in production. In order to overcome the separation between the planning and operational levels in traditional production planning and control systems, this paper presents a validated concept to react immediately to critical events while maintaining efficiency using a modular, rolling-reactive optimization tool. A substantial difference to conventional rescheduling models is the intensive integration of event-discrete simulation, which delivers more realistic results and reduces the threat of an obsolete model due to simple modelling.

Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production planning and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed smalland large-size instances, respectively. The experimental results show that the approach can save 5%–10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%–4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

Comparison of DBR with CONWIP in an unbalanced production line with three stations

The recently developed alternatives to traditional production planning and control systems such as material requirement planning (MRP) and Kanban are the drum–buffer–rope (DBR) and CONWIP (CONstant Work In Process) systems. Each system is best described as a combination push (like an MRP)/pull (like a Kanban) logistical procedure. Materials are pulled into the shop via the appropriate logic, and once released, materials are then pushed to subsequent workcentres. The performance of the DBR and CONWIP control policies are analysed and compared in a three-stage unbalanced tandem production line. Using a continuous Markov process model, steady-state probability distributions for the systems are derived, and then the performance measures of the systems can be evaluated. To compare the two systems, an optimization model for each system is proposed. From sensitivity analyses for the optimization models, the proposed models are validated, the differences of the two systems are investigated, and it is found that DBR is better than CONWIP under the proposed performance measures.

ExPlanTech: Applying multi-agent systems in production planning

This paper presents work carried out within the 'ExPlanTech' project (IST-1999-20171) funded in part by the European Commission's Information Technologies Programme. The mission of the ExPlanTech technology transfer project is to introduce, customize and exploit the multi-agent production planning technology (ProPlanT multi-agent system research prototype) in two specific industrial enterprises. An agent-driven service negotiations and decision process, based on usagecentred knowledge about task requirements, substitutes the traditional production planning activity. We introduce a methodology for integration of the projectdriven production planning based on agent-based engineering within the existing enterprise resource planning system. This novel production planning technology will facilitate optimization of resource utilization and supplier chain while meeting the customer demands. This paper describes a FIPA-compliant implementation of the ExPlanTech technology at the LIAZ Pattern Shop manufacturing company. We describe the structure of the agent community, types of agents, implementation of the planning strategy and its incorporation within the real production environment.

DYNAMIC INTEGRATED PROCESS PLANNING AND SCHEDULING

ABSTRACT Unexpected disturbance to the manufacturing capability and production schedule of a production system may always lead to the stoppage of the production line and delay to fulfill the production order. In order to cope with these disturbances, traditional production planning and control approaches focused on the “localized” problem and only provided limited adjustment of the system. A Dynamic Integrated Process planning and Scheduling system (DIPS) is developed that provides a complete systematic examination to the disturbances. DIPS contains a two-stage, genetic-algorithm-based control mechanism that can dynamically generate both the optimal process plan and production schedule. Statistical optimization technique is adopted to evaluate the performance of DIPS. Simulation results show that DIPS significantly improves the total production cost under all problem domains.

A component-based approach to support order planning in a distributed manufacturing enterprise

Traditional Production Planning and Control systems do not successfully deal with new organisational forms of manufacturing, like production “islands”, product oriented or customer driven production. These current trends lead in practice to a strong decentralisation of production management tasks and to an “object oriented structuring” of the manufacturing process. Planning and operation in this global environment requires new skills and new approaches, namely, the co-ordination of global networks of manufacturing units and of large complex supply chains. In this paper, we present a decentralised information system designed to address the tasks of production planning that result from sales orders, originated in customers located anywhere in the world, and accomplished through a distributed manufacturing network. The system addresses the requirements of a make-to-order environment and is hopefully able to produce realistic satisfactory delivery dates. The information infrastructure designed and implemented using distributed object-oriented technology with a component based architecture has proven to be efficient and powerful, satisfying all the major tight requirements of information systems in an environment of distributed manufacturing.

The Concept and Practice of the Production Seat System

This paper presents features of the production seat system and its industrial applications in Japan. Like an airline reservation system, customers' orders are booked on the production seat table in the production seat system. Then orders are released from the production table to work processes at every production order cycle. To show the effectiveness of the production seat system, a simple simulation was conducted. It was observed that the production seat system would achieve better observance of customer delivery dates and would reduce the average production lead time when compared with the traditional production planning and scheduling system. © 1997 John Wiley & Sons, Ltd.

Hierarchically integrated lot size optimization

The paper describes a new hierarchically integrated approach to improve conceptual deficiencies of traditional Production Planning and Control (PPC-)Systems for a make-to-stock job shop production. The main concern is a restructuring of traditional planning levels and an improvement of their hierarchical interdependence. Thus, already at the medium term planning level lot sizes are determined which, in the traditional concept is a task of the MRP-module. The hierarchical interdependence is not simply a top-down relationship, but is supplemented by an explicit anticipation of the lower levels. Considering the information status at the medium term level the job shop is described as a queueing network and the lot sizes are obtained minimizing simultaneously mean lead time, (medium term) capacities, and (short term) inventories. The approach is computational feasible and, based on some numerical results, is compared with a deterministic multi level capacitated lot sizing model.