A hybrid VNS approach for the short-term production planning and scheduling: A case study in the pulp and paper industry

Mathematical formulations for production planning are increasing complexity, in order to improve their realism. In short-term planning, the desirable level of detail is particularly high. Exact solvers fail to generate good quality solutions for those complex models on medium- and large-sized instances within feasible time. Motivated by a real-world case study in the pulp and paper industry, this paper provides an efficient solution method to tackle the short-term production planning and scheduling in an integrated mill. Decisions on the paper machine setup pattern and on the production rate of the pulp digester (which is constrained to a maximum variation) complicate the problem. The approach is built on top of a mixed integer programming (MIP) formulation derived from the multi-stage general lotsizing and scheduling problem. It combines a Variable Neighbourhood Search procedure which manages the setup-related variables, a specific heuristic to determine the digester's production speeds and an exact method to optimize the production and flow movement decisions. Different strategies are explored to speed-up the solution procedure and alternative variants of the algorithm are tested on instances based on real data from the case study. The algorithm is benchmarked against exact procedures.     

A cutting plane approach for integrated planning and scheduling

In this paper we propose a branch-and-cut algorithm for solving an integrated production planning and scheduling problem in a parallel machine environment. The planning problem consists of assigning each job to a week over the planning horizon, whereas in the scheduling problem those jobs assigned to a given week have to be scheduled in a parallel machine environment such that all jobs are finished within the week. We solve this problem in two ways: (1) as a monolithic mathematical program and (2) using a hierarchical decomposition approach in which only the planning decisions are modeled explicitly, and the existence of a feasible schedule for each week is verified by using cutting planes. The two approaches are compared with extensive computational testing.     

Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling was implemented after process plans had been generated. Considering their complementarity, it is necessary to integrate these two functions more tightly to improve the performance of a manufacturing system greatly. In this paper, a mathematical model of integrated process planning and scheduling has been formulated. And, an evolutionary algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the approach, efficient genetic representation and operator schemes have been developed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the integrated process planning and scheduling is necessary and the proposed approach has achieved significant improvement.     

A hierarchic approach to production planning and scheduling of a flexible manufacturing system

The paper deals with the problem of improving the machine utilization of a flexible manufacturing cell. Limited tool magazine space of the machines turns out to be a relevant bottleneck. A hierarchic approach for this problem is proposed. At the upper level, sets of parts that can be concurrently processed (batches) are determined. At the lower levels, batches are sequenced, linked, and scheduled. Methods taken from the literature are used for the solution of the latter subproblems, and an original mixed integer programming model is formulated to determine batches. The proposed methods are discussed on the basis of computational experience carried out on real instances.     

Multi-behavior agent model for planning in supply chains: An application to the lumber industry

Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for planning systems to present planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, agent-based technology takes advantage of the ability of agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, planning agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior planning agent model using different planning strategies when decisions are supported by a distributed planning system. The implementation of this solution is realized through the FOR@C experimental agent-based platform, dedicated to supply chain planning for the lumber industry.     

A DSS for production planning focused on customer service and technological aspects

Production planning and control in manufacturing systems cover several aspects, at different hierarchical levels, including decisions on production and inventory quantities, resource acquisition, production allocation and sequencing. We consider a problem that is typical of companies that manufacture products in production plants placed in different production areas worldwide. A solution framework for the production allocation and balancing problems based on mathematical programming is proposed. Its computational efficiency is improved using techniques from constraint programming, in order to make it possible to solve real world instances of the problems. An industrial test case is used as a benchmark to prove the effectiveness of the proposed approach.     

Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry

An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each order’s production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed- and multi-product assembly environment.     

Genetic algorithms in integrated process planning and scheduling

Process planning and scheduling are actually interrelated and should be solved simultaneously. Most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. The initial part of this paper describes a genetic algorithm (GA) based algorithm that only considers the time aspect of the alternative machines. The scope of consideration is then further extended to include the processing capabilities of alternative machines, with different tolerance limits and processing costs. In the proposed method based on GAs, the processing capabilities of the machines, including processing costs as well as number of rejects produced in alternative machine are considered simultaneously with the scheduling of jobs. The formulation is based on multi-objective weighted-sums optimization, which are to minimize makespan, to minimize total rejects produced and to minimize the total cost of production. A comparison is done w ith the traditional sequential method and the multi-objective genetic algorithm (MOGA) approach, based on the Pareto optimal concept.     

An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling

Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as the separate tasks to perform sequentially. Recently, a significant trend is to integrate process planning and scheduling more tightly to achieve greater performance and higher productivity of the manufacturing system. Because of the complementarity of process planning and scheduling, and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective integrated process planning and scheduling (IPPS) problem. In this research, the Nash equilibrium in game theory based approach has been used to deal with the multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method on the research of the multi-objective IPPS problem.     

Applications of particle swarm optimisation in integrated process planning and scheduling

Integration of process planning and scheduling (IPPS) is an important research issue to achieve manufacturing planning optimisation. In both process planning and scheduling, vast search spaces and complex technical constraints are significant barriers to the effectiveness of the processes. In this paper, the IPPS problem has been developed as a combinatorial optimisation model, and a modern evolutionary algorithm, i.e., the particle swarm optimisation (PSO) algorithm, has been modified and applied to solve it effectively. Initial solutions are formed and encoded into particles of the PSO algorithm. The particles “fly” intelligently in the search space to achieve the best sequence according to the optimisation strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles’ movements to form a modified PSO algorithm. Case studies have been conducted to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results generated by the genetic algorithm (GA) and the simulated annealing (SA) algorithm, respectively, and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in both applications.     

Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results

This paper summarizes the main existing approaches to propagate resource constraints in Constraint-Based scheduling and identifies some of their limitations for using them in an integrated planning and scheduling framework. We then describe two new algorithms to propagate resource constraints on discrete resources and reservoirs. Unlike most of the classical work in scheduling, our algorithms focus on the precedence relations between activities rather than on their absolute position in time. They are efficient even when the set of activities is not completely defined and when the time window of activities is large. These features explain why our algorithms are particularly suited for integrated planning and scheduling approaches. All our algorithms are illustrated with examples. Encouraging preliminary results are reported on pure scheduling problems as well as some possible extensions of our framework.     

Expert systems in production planning and scheduling: A state-of-the-art survey

Intelligent solutions, based on expert systems, to solve problems in the field of production planning and scheduling are becoming more and more widespread nowadays. Especially the last decade has witnessed a growing number of manufacturing companies, including glass, oil, aerospace, computers, electronics, metal and chemical industries—to name just a few—interested in the applications of expert systems (ESs) in manufacturing. This paper is a state-of-the-art review of the use of ESs in the field of production planning and scheduling. The paper presents famous expert systems known in the literature and current applications, analyzes the relative benefits and concludes by sharing thoughts and estimations on ESs future prospects in this area.     

A linearized polynomial mixed integer programming model for the integration of process planning and scheduling

This paper presents a linearized polynomial mixed-integer programming model (PMIPM) for the integration of process planning and scheduling problem. First, the integration problem is modeled as a PMIPM in which some of the terms are of products of up to three variables, of both binary and continuous in nature. Then, an equivalent linearized model is derived from the polynomial model by applying certain linearization techniques. Although the linearized models have more variables and constraints than their polynomial counterparts, they are potentially solvable to the optimum in comparison to their equivalent polynomial models. Experiments show that the linearized model possesses certain characteristics that are absent from other models in the literature, and provides a fundamental framework for further research in this area.     

Project scheduling under resource constraints: Application of the cumulative global constraint in a decision support framework

This paper concerns project scheduling under resource constraints. Traditionally, the objective is to find a unique solution that minimizes the project makespan, while respecting the precedence constraints and the resource constraints. This work focuses on developing a model and a decision support framework for industrial application of the cumulative global constraint. For a given project scheduling, the proposed approach allows the generation of different optimal solutions relative to the alternate availability of outsourcing and resources. The objective is to provide a decision-maker an assistance to construct, choose, and define the appropriate scheduling program taking into account the possible capacity resources. The industrial problem under consideration is modeled as a constraint satisfaction problem (CSP). It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the tasks realized on each resource, as well as the curves with the profile of the total consumption of resources on time.     

A bi-level evolutionary optimization approach for integrated production and transportation scheduling

This paper investigates an integrated production and transportation scheduling (IPTS) problem which is formulated as a bi-level mixed integer nonlinear program. This problem considers distinct realistic features widely existing in make-to-order supply chains, namely unrelated parallel-machine production environment and product batch-based delivery. An evolution-strategy-based bi-level evolutionary optimization approach is developed to handle the IPTS problem by integrating a memetic algorithm and heuristic rules. The efficiency and effectiveness of the proposed approach is evaluated by numerical experiments based on industrial data and industrial-size problems. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated.     

石化企业生产计划与能量系统集成研究

应用并行工程方法对生产计划与能量系统的集成进行了分析,提出了用于确定业务流程和信息流的业务过程模型,明确了系统的并行特性,采用并行工程方法建立了并行业务过程模型,并确定了用于数据建模的数据流,从而为集成效据库管理和集成软件系统的开发奠定了基础。     

Study on berth planning problem in a container seaport: Using an integrated programming approach

This paper provides an integrated solution for two common processes in a container seaport: namely, the Berth Allocation Process (BAP) and the Crane Scheduling Process (QCSP). These are formulated through a Bi-Level Programming (BLP) approach, which is used to characterize the highly interrelated relationships between the two processes and simultaneously, identify an integrated solution for both. The upper-level problem (BAP) is termed ‘NP-hard’ as its computational complexity increases exponentially with the number of incoming ships for mooring, while the lower-level problem (QCSP) is a mixed-integer linear program. A revised genetic algorithm and a branch-and-bound method (B&B) are then applied for the solutions of upper and lower level problems, respectively.     

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.