Lot sizing in capacitated production planning and control systems

Current production planning and control (PPC) systems often separate material requirements from capacity planning. As a result, practitioners often complain about the infeasibility of production schedules regarding capacity, which causes long and unpredictable lead times and poor customers service. This paper describes a hierarchically structured PPC system that explicitly considers production capacity at each stage of the planning process. The impact of the certainty of demand data on the integration of lot sizing and sequencing decisions is discussed. A decision model for lot sizing applicable to changing demand data is proposed. It distinguishes between resources that are critical or uncritical with respect to batching decisions. Several currently available solution procedures are discussed and compared that support lot sizing decisions in multi-level production systems subject to multiple capacity constraints, setup times and dynamic demand rates.     

A data-driven approach to multi-product production network planning

The clearing function models the non-linear relationship between work-in-process and throughput and has been proposed for production planning in environments with queuing (congestion) effects. One approach in multi-product, multi-stage environments has been to model the clearing function at the bottleneck machine only. However, since the bottleneck shifts as the product release mix changes, this approach has its limitations. The other approach is the Alternative Clearing Function formulation, where the clearing function is first estimated at the resource level using piecewise linear regression from simulation experiments, and then embedded into a linear programme. This paper develops an alternative to the Allocated Clearing Function formulation, wherein system throughput is estimated at discrete work-in-process points. A mixed integer programming formulation is then presented to use these throughput estimates for discrete release choices. The strength of the formulation is illustrated with a numerical example and the new approach is compared with the ACF.     

Experiential exercises with four production planning and control systems

In the authors’ experience, students have difficulty in understanding the differences between production planning and control techniques. Presumably, current business managers, although they have heard about these systems, may also lack clarity on the differences between them. We outline manual games for simulating production runs in four systems (Materials Requirements Planning, Just-In-Time, Theory of Constraints and CONWIP) to give managers and students’ insight into the mechanics of different production planning control techniques. We then provide excel-based simulation tools to allow users to vary parameters for each system and see the impact on inventory and throughput. We believe that the combination of manual and excel-based games significantly enhances understanding of the systems as well as their differences.     

Methodological framework for developing decision support systems (DSS) for hazardous materials emergency response operations

The production, storage, and transportation of hazardous materials are processes of vital economic importance for any advanced and technologically complex society. Although the production and distribution of hazardous materials is associated with economic development, there is a significant potential danger to the natural and social environment in the event of their accidental release, a fact that prompts for the development and implementation of methods and techniques that aim to improve hazardous materials risk management decisions. The objective of this paper is to present a unified framework for developing a Decision Support System (DSS) for supporting a vital function of risk management, namely the management of emergency response operations. The proposed framework recognizes the peculiarities of the hazardous materials decision-making environment which is characterized by: (i) multiple stakeholders, i.e., persons and organizations involved in and affected by hazardous materials risk management decisions; (ii) lack of a formal management structure for monitoring and controlling in a unified manner all Emergency Response Resources; (iii) lack of clear distinction and fragmentation of responsibilities of the actors involved in risk management operations; and (iv) dynamic/real-time decisions, i.e., risk determinants change over time. The proposed framework was used in order to develop a DSS for managing emergency response operations for large scale industrial accidents in Western Attica, Greece.     

A service-level-oriented heuristic approach to production planning in a robotized spinning-mill

This paper presents a heuristic approach to production planning and inventory control. The heuristic was developed for scheduling the production of N-products on many machines when both production and demand rates are stochastic. The key for scheduling production is a target inventory level to be maintained at the beginning of each period; hysteresis factors within the heuristic make the production plan more or less sensitive to transient conditions in both demand and production. Experimental results of its application in a production environment are provided.     

A methodology integrating Petri nets and knowledge-based systems to support process family planning

Planning production processes for product families have been well recognised as an effective means of achieving successful product family development. However, most existing approaches do not lend themselves to planning production processes with focus on the optimality of the cohort of a product family. This paper addresses process family planning for product families. In view of the advantages of Petri nets (PNs) for modelling large systems, the potential of knowledge-based systems (KBSs) for solving complex problems and the analogy in between, we develop a methodology by integrating PNs and KBSs to support process family planning. An integrated product–process family structure, called IP²S, is proposed to organise all data pertaining to a product family and the corresponding process family, thereby anchoring planning to one platform. With the IP²S, a formal PN model of process family planning is further developed by integrating the principles of several well-defined PN extensions. Thus, this paper also contributes to visualising the dynamic behaviours and reasoning behind process family planning. The methodology is applied to process family planning for a truck family. The preliminary results demonstrate the feasibility and potential of using the methodology to support process family planning.     

Material requirements planning in hierarchical production planning systems

Material requirements planning (MRP) is a way to develop the time phased material and other resource requirements to satisfy the needs of a master production schedule. Hierarchical production planning (HPP) is a framework for analysis and decision making in complex production environments. They are complementary approaches to the decision support system needs of multi-stage production systems. The hierarchical approach provides for efficient management review of proposed plans at corporate, plant, and shop levels while MRP provides a sound and detailed basis for understanding the implications of proposed plans and for executing the selected plan. The relationships between these two approaches are illustrated with an example application.     

A project scheduling approach to production planning with feeding precedence relations

In Manufacturing-to-Order or Engineering-to-Order systems producing complex and highly customised items, each item has its own characteristics that are often tailored for a specific customer. Project scheduling approaches are suitable for production planning in such environments. However, when we consider the production of complex items, the distinct production operations are often aggregated into activities representing whole production phases. In such cases, the planning and scheduling problem works on the aggregate activities, considering that, in most cases, such activities also have to be manually executed. Moreover, simple finish-to-start precedence relations no longer correctly represent the real production process, but overlapping among activities should be allowed. In this paper, a project scheduling approach is proposed for production planning in Manufacturing-to-Order systems. The Variable Intensity formulation is used to allow the effort committed to the execution of activities to vary over time. Feeding precedences are developed to model generalised precedence relations when the execution mode of activities is not known a priori. Two mathematical formulations of these precedence relations are proposed. The formulations are applied both to randomly generated instances and to an industrial system producing machining centres and are compared in terms of computational efficiency.     

A neuroevolutionary approach to stochastic inventory control in multi-echelon systems

Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form.     

A robust optimization approach to enhancing reliability in production planning under non-compliance risks

Certain regulated industries are monitored by inspections that ensure adherence (compliance) to regulations. These inspections can often be with very short notice and can focus on particular aspects of the business. Failing such inspections can bring great losses to a company; thus, evaluating the risks of failure against various inspection strategies can help it ensure a robust operation. In this paper, we investigate a game-theoretic setup of a production planning problem under uncertainty in which a company is exposed to the risk of failing authoritative inspections due to non-compliance with enforced regulations. In the proposed decision model, the inspection agency is considered an adversary to the company whose production sites are subject to inspections. The outcome of an inspection is uncertain and is modeled as a Bernoulli-distributed random variable whose parameter is the mean of non-compliance probabilities of products produced at the inspected site and, therefore, is a function of production decisions. If a site fails an inspection, then all its products are deemed adulterated and cannot be used, jeopardizing the reliability of the company in satisfying customers’ demand. In the proposed framework, we address two sources of uncertainty facing the company. First, through the adversarial setting, we address the uncertainty arising from the inspection process as the company does not know a priori which sites the agency will choose to inspect. Second, we address data uncertainty via robust optimization. We model products’ non-compliance probabilities as uncertain parameters belonging to polyhedral uncertainty sets and maximize the worst-case expected profit over these sets. We derive tractable and compact formulations in the form of a mixed integer program that can be solved efficiently via readily available standard software. Furthermore, we give theoretical insights into the structure of optimal solutions and worst-case uncertainties. The proposed approach offers the flexibility of matching solutions to the level of conservatism of the decision maker via two intuitive parameters: the anticipated number of sites to be inspected, and the number of products at each site that are anticipated to be at their worst-case non-compliance level. Varying these parameters when solving for the optimal products allocation provides different risk-return tradeoffs and thus selecting them is an essential part of decision makers’ strategy. We believe that the robust approach holds much potential in enhancing reliability in production planning and other similar frameworks in which the probability of random events depends on decision variables and in which the uncertainty of parameters is prevalent and difficult to handle.     

A capacity-oriented hierarchical approach to single-item and small-batch production planning using project-scheduling methods

Most production planning and control (PPC) systems used in practice have an essential weakness in that they do not support hierarchical planning with feedback and do not observe resource constraints at all production levels. Also, PPC systems often do not deal with particular types of production, for example, low-volume production. We propose a capacity-oriented hierarchical approach to single-item and small-batch-production planning for make-to-order production. In particular, the planning stages of capacitated master production scheduling, multi-level lot sizing, temporal and capacity planning, and shop floor scheduling are discussed, where the degree of aggregation of products and resources decreases from stage to stage. It turns out that the optimization problems arising at most stages can be modelled as resourceconstrained project scheduling problems.     

Technology transfer in production planning for developing countries

In the area of production planning, the technology transfer from the developed world to developing countries should take into account the expensive machinery and economies of scale. In developed countries, equipment is cheap but manpower is expensive. Thus production planning models are built so that manpower is never idle. In developing countries the machines are much more expensive and maintenance plays a bigger role. Thus production planning models should be built which include the machine idle cost and maintenance costs. This paper explains how such models can be analytically developed. Finally, it is -shown that demand can be stimulated though economies of scale and inductrial goods can be provided at much cheaper rates.     

A framework for ICT-enabled real-time production planning and control

This paper aims to develop a conceptual framework for real-time production planning and control(PPC).Firstly,we discuss the most prominently applied contemporary information and communication technologies for PPC.Enterprise resource planning(ERP) systems that integrate the value chain in an enterprise,manufacturing execution systems that manage and control the production on shopfloor,and advanced planning and scheduling(APS)systems that develop solutions for complex planning problems are the planning and control systems that have been analyzed.We emphasize the application of radio frequency identification as the most advanced and promising emerging real-time data capture technology that is currently available to manufacturers.Having analyzed the features and shortcomings of the individual systems perse,and by considering the advantages that may be realized through effective integration of these otherwise discrete systems,we propose a framework for real-time PPC.     

A genetic algorithm heuristic approach to general outsourcing capacitated production planning problems

This paper addresses a dynamic capacitated production planning (CPP) problem with consideration of outsourcing. Specifically, the outsourcing problem considered in this paper has the following features: (1) all demands are met by production or outsourcing without postponement or backlog, (2) production, inventory, and outsourcing levels all have a limit, and (3) the cost functions are considered arbitrarily and time-varying. These features come together, leading to a so-called general outsourcing CPP problem. In our previous work, an algorithm with pseudo-polynomial time complexity was developed, which includes a formation of a feasible solution region and then a search procedure using dynamic programming techniques. Due to the computational complexity with such an approach, only small and medium problems can be solved in a practical sense. In this paper, we present a genetic algorithm (GA) approach to the same problem. The novelty of this GA approach is that the idea of the feasible solution region is used as a heuristic to guide the searching process. We present a computational experiment to show the effectiveness of the proposed approach.     

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.     

A fuzzy approach to multi-objective routing problem with applications to process planning in manufacturing systems

In this paper a method is developed for the selection of an efficient path in a fuzzy multi-objective network. The application of the methodology developed is illustrated by a process plan selection problem in a manufacturing environment.     

Workware decision support systems: A comprehensive methodological approach to work-scheduling problems

Around-the-clock continuous operations are expanding as global business activity, government de-regulation, flexible manufacturing and lean operation become more common. These developments often require night work, long work hours and/or irregular work schedules. As a result of these requirements, decisions about work schedule assignments are frequently made with little knowledge, information and/or warning. Some of the resulting work-scheduling practices undoubtedly increase operation and worker exposure to health and safety risks. To counter these work-scheduling problems, a comprehensive work schedules knowledge information system is outlined. Within this model, a Workware Warehouse is proposed as an Internet gateway where decision support systems are readily and freely available. This model suggests a new paradigm for work-scheduling ergonomics, one where designing, filling and maintaining a Workware Warehouse is the primary focus of the human factors professional.