An integrated system solution for supply chain optimization in the chemical process industry

This paper considers a complex scheduling problem in the chemical process industry involving batch production. The application described comprises a network of production plants with interdependent production schedules, multi-stage production at multi-purpose facilities, and chain production. The paper addresses three distinct aspects: (i) a scheduling solution obtained from a genetic algorithm based optimizer, (ii) a mechanism for collaborative planning among the involved plants, and (iii) a tool for manual updates and schedule changes. The tailor made optimization algorithm simultaneously considers alternative production paths and facility selection as well as product and resource specific parameters such as batch sizes, and setup and cleanup times. The collaborative planning concept allows all the plants to work simultaneously as partners in a supply chain resulting in higher transparency, greater flexibility, and reduced response time as a whole. The user interface supports monitoring production schedules graphically and provides custom-built utilities for manual changes to the production schedule, investigation of various what-if scenarios, and marketing queries. RID="*" ID="*" The authors would like to thank Hans-Otto Günther and Roland Heilmann for helpful comments on draft versions of this paper.     

A multi-agent system for chemical supply chain simulation and management support

Modern chemical production is customer-driven and the desired delivery time for the products is often shorter than their campaign length. In addition, the raw materials supplying time is often long. These features make it desirable to provide tools to support collaborative supply chain decision making, preferably over the Internet, and where there are conflicts, compromise decisions can be quickly reached and the effects of the decisions can be quantitatively simulated. This paper des cribes such a multi-agent system (MAS) that can be used to simulate the dynamic behaviour and support the management of chemical supply chains over the Internet. Geographically distributed retailers, logistics, warehouses, plants and raw material suppliers are modelled as an open and re-configurable network of co-operative agents, each performing one or more supply chain functions. Communication between agents is made through the common agent communication language KQML (knowledge query message language). A t the simulation layer, the MAS allows distributed simulation of the chain behaviour dynamically, so that compromise decisions can be rapidly and quantitatively evaluated. Because in a chemical supply chain the scheduling of the plant often dominates the chain performance, an optimum scheduling system for batch plants is integrated into the MAS. The functions of the system are illustrated by reference to a case study for the supply and manufacture using a multi-purpose batch plant of paints and coatings.     

Sequence-dependent batch chemical scheduling with earliness and tardiness penalties

Production volume in the specialized agricultural chemical industry is typically too small to justify the capital expenditure required for continuous processing. As such, there is a trend towards building chemical processing plants for this market segment that are batch plants. Scheduling of this type of chemical plant under just-in-time operations, where both earliness and lateness penalties are included, is critical to the efficient operation of these plants. In this paper, a heuristic scheduling procedure is developed for the problem of minimizing the total weighted earliness and tardiness costs as well as the total set-up cost for the single-stage batch chemical manufacturing environment.     

Revising the master production schedule in a HPP framework context

This paper investigates the stability of the master production schedule (MPS) in a multi-product batch chemical plant, a typical example of manufacturing plants in the process industry. The effects of demand pattern, replanning periodicity, setup costs and unit production cost on the performance of the MPS in a rolling horizon situation are examined. Adopting the hierarchical production planning framework, a two-level mathematical model is developed to conduct the study. A comprehensive simulation work and statistical analyses are reported in this work. The results of the study show that replanning periodicity significantly influences the scheduling instability and the impact of setup cost on scheduling instability is dependent on the demand pattern and the unit production cost. Moreover, the findings indicate that replanning frequency does not affect the total cost of the system if the cost structures are not extreme. Finally, the results show that cost structures affect the batching of orders (converting production quantities into an optimal number of batches to be processed).     

Master production scheduling in capacitated sequence-dependent process industries

Traditional approaches to planning and control of manufacturing (MRPII) focus on discrete parts manufacturing industries (e.g. automotive). The chemical industry, however, presents unique challenges. Cross-contamination of production is a key issue among some chemical facilities. A considerable amount of capacity is lost as a result of changeovers which involve performing thorough clean-ups to wash away the impurities which may contaminate the next product to be produced. Therefore, planning for sequence-dependent changeovers becomes crucial and complicates the master production scheduling process. This paper shows how improved master production scheduling performance can be obtained by using a two-level master production schedule (MPS) to focus on key plant processes, and by incorporating a scheduling heuristic which considers sequence-dependent changeovers and capacity constraints. This approach is illustrated using actual operating data from a chemical firm typical of many process industry operations. Simulation experiments are reported that test the performance of the proposed master scheduling method in a single-stage sequence-dependent process. The experimental factors include both the introduction of the two-level MPS with the scheduling heuristic, and the effect of changes in the MPS batch size. The results demonstrate that important simultaneous improvements in process changeover time and delivery performance can be achieved using the proposed MPS scheduling approach against a more traditional (single-level) MPS approach which does not consider sequence-dependent changeovers. Further, we find that delivery performance is relatively insensitive to adjustments in the MPS batch size when using the two-level MPS approach.     

面向医疗器械企业灭菌工艺的不同容量平行机批调度

针对医疗器械企业灭菌工艺生产过程的调度问题,提出面向灭菌工艺的不同容量平行机批调度方法,建立以最小化总延迟时长、最小化总加工能耗和最大化灭菌柜装载率为目标的不同容量平行机批调度模型。并针对模型的求解提出一种改进的NSGA-III算法(Improved NSGA-III,INSGA-III)。为了获得更高质量的批调度解,采用EDT+MLC启发式规则生成INSGA-III初始种群,并设计一种局部搜索策略以改进算法迭代后期的搜索能力。最后,通过算例仿真与传统调度方法进行对比分析,验证了该模型和算法的有效性和可行性。结果表明,该模型和算法较传统调度方法有明显的优势,可为医疗企业实际生产调度提供新思路。     

Production scheduling of single-stage multi-product batch chemical processes with fixed batch sizes

We address the problem of scheduling a single-stage multi-product batch chemical process with fixed batch sizes. We present a mixed-integer nonlinear programming model to determine the schedule of batches, the batch size, and the number of overtime shifts that satisfy the demand at minimum cost for this process. We introduce a polynomial-time algorithm to solve the problem when the processing times of all batches are identical and the setup and cleaning times are sequence-independent. The solution procedure is based on recognizing that the optimal fixed batch size is a member of a set whose cardinality is polynomial. Given a batch size, the problem may be formulated as an assignment problem. Thus, an optimal solution may be found by iteratively solving a polynomial number of assignment problems. This work was motivated by a pesticide manufacturing company in the design of a new plant where the assumptions of a single bottleneck machine, fixed batch sizes, sequence-independent setup times, and identical batch processing times are all valid. An example is developed for this application.     

考虑工序并行的差异工件批调度研究

针对铸造车间差异工件组批多约束的问题,在工序可并行加工的前提下构建以最小化最大完工时间和最小化沙箱空置率为优化目标的并行工序批调度模型,设计一种改进和声算法求解该调度模型,提出一种单工序编解码方式和2种机器分配规则用于解决工件分批、沙箱选择、工序分配及机器选择的问题。在算法中提出一种新的和声产生方式和更新机制,同时为改善算法的局部搜索能力,加入模拟退火算法执行局部搜索过程。最后根据企业实际生产数据进行仿真实验,验证本文模型的有效性。     

Advanced production scheduling for batch plants in process industries

An Advanced Planning System (APS) offers support at all planning levels along the supply chain while observing limited resources. We consider an APS for process industries (e.g. chemical and pharmaceutical industries) consisting of the modules network design (for long–term decisions), supply network planning (for medium–term decisions), and detailed production scheduling (for short–term decisions). For each module, we outline the decision problem, discuss the specifi cs of process industries, and review state–of–the–art solution approaches. For the module detailed production scheduling, a new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far. The new approach decomposes detailed production scheduling for batch production into batching and batch scheduling. The batching problem converts the primary requirements for products into individual batches, where the work load is to be minimized. We formulate the batching problem as a nonlinear mixed–integer program and transform it into a linear mixed–binary program of moderate size, which can be solved by standard software. The batch scheduling problem allocates the batches to scarce resources such as processing units, workers, and intermediate storage facilities, where some regular objective function like the makespan is to be minimized. The batch scheduling problem is modelled as a resource–constrained project scheduling problem, which can be solved by an efficient truncated branch–and–bound algorithm developed recently. The performance of the new solution procedures for batching and batch scheduling is demonstrated by solving several instances of a case study from process industries.     

Using S-graph to address uncertainty in batch plants

Processes and markets uncertainties make batch plants a complex environment to manage production activities. Uncertainties may cause deviations and infeasibilities in predefined schedules; this may result in poor planning and inefficient utilization of materials. Consequently, the relevance of explicitly incorporating variability in the scheduling formulation in order to offer more efficient plans and robust decisions to changes has become recognized. This work addresses the batch plants scheduling under exogenous uncertainty. The most widely utilized approach to tackle this problem is stochastic programming; however its solution results in high computational expenses. From another standpoint S-graph, a graph-theoretic approach, has proved to be very efficient to deal with deterministic scheduling. In this work, the S-graph framework is enhanced so that stochastic scheduling problems can be handled. For this purpose, a LP model that is used as performance evaluator has been coupled with S-graph framework. One of the main advantages of the proposed approach is that the search space does not increase according to the number of scenarios considered in the problem. Finally, the potential of the proposed framework is highlighted through two illustrative examples.     

基于交货期的单机批量加工排序问题

针对钢铁、化工等工业的生产工件需分批、排序加工的特点,借助车辆路径问题的思想给出了制定批量单位的数学模型和基于禁忌搜索算法的求解方法,进一步讨论了这些批量单位在单机模式下,基于交货期的加工排序问题,给出了相应的优化模型和启发式算法。针对热轧生产进行数值仿真,取得了较为理想的成果。     

Batch scheduling in process industries: an application of resource–constrained project scheduling

The paper deals with batch scheduling problems in process industries where final products arise from several successive chemical or physical transformations of raw materials using multi–purpose equipment. In batch production mode, the total requirements of intermediate and final products are partitioned into batches. The production start of a batch at a given level requires the availability of all input products. We consider the problem of scheduling the production of given batches such that the makespan is minimized. Constraints like minimum and maximum time lags between successive production levels, sequence–dependent facility setup times, finite intermediate storages, production breaks, and time–varying manpower contribute to the complexity of this problem. We propose a new solution approach using models and methods of resource–constrained project scheduling, which (approximately) solves problems of industrial size within a reasonable amount of time. Received: October 15, 1999 / Accepted: March 21, 2000     

Minimising makespan on parallel batch processing machines with non-identical ready time and arbitrary job sizes

This paper considers the parallel batch processing machine scheduling problem which involves the constraints of unequal ready times, non-identical job sizes, and batch dependent processing times in order to sequence batches on identical parallel batch processing machines with capacity restrictions. This scheduling problem is a practical generalisation of the classical parallel batch processing machine scheduling problem, which has many real-world applications, particularly, in the aging test operation of the module assembly stage in the manufacture of thin film transistor liquid crystal displays (TFT-LCD). The objective of this paper is to seek a schedule with a minimum total completion time for the parallel batch processing machine scheduling problem. A mixed integer linear programming (MILP) model is proposed to optimise the scheduling problem. In addition, to solve the MILP model more efficiently, an effective compound algorithm is proposed to determine the number of batches and to apply this number as one parameter in the MILP model in order to reduce the complexity of the problem. Finally, three efficient heuristic algorithms for solving the large-scale parallel batch processing machine scheduling problem are also provided.     

基于改进遗传算法的分批调度研究

针对分批调度的批量划分和排序优化问题,提出两段染色体编码方式,分别描述工序信息和批量信息.提出舍弃比率概念来优化种群初始化过程.采用自适应交叉算子和变异算子,以双种群协同进化方式对遗传算法进行改进.以文献数据为例仿真测试了改进遗传算法的有效性,对于复杂的分批调度问题,改进遗传算法一般能够得到较为满意的解.成功应用分批调度改进遗传算法解决了某公司分批调度问题.     

考虑疲劳效应的伤员应急手术调度模型及算法

为了解决城市突发事件应急救援中批量应急手术的调度问题,并考虑医生长时间连续手术对手术持续时间和挽救病人生命的成功率带来的恶化效应,提出了三阶段批量手术调度模型,将应急手术调度看作是存在并行机的流水车间调度问题。利用改进的飞蛾扑火算法对应急手术模型进行求解,并通过实证来测试模型和算法的有效性。为了验证算法的性能,将经典飞蛾扑火算法、粒子群算法和布谷鸟算法与其对比,取20次运行结果,得知最大手术完成时间均值中改进的飞蛾扑火算法为最小,调度模型给出的调度方案中,3个救治阶段在时间维度上保持连贯。仿真结果表明,改进的飞蛾扑火算法能很好地求解批量手术调度模型,获得较好的调度结果。     

The burn-in test scheduling problem with batch dependent processing time and sequence dependent setup time

The burn-in test scheduling problem (BTSP) is a variation of the complex batch processing machine scheduling problem, which is also a generalisation of the liquid crystal injection scheduling problem with incompatible product families and classical identical parallel machine problem. In the case we investigated on the BTSP, the jobs are clustered by their product families. The product families can be clustered by different product groups. In the same product group, jobs with different product families can be processed as a batch. The batch processing time is dependent on the longest processing time of those jobs in that batch. Setup times between two consecutive batches of different product groups on the same batch machine are sequentially dependent. In addition, the unequal ready times are considered in the BTSP which involves the decisions of batch formation and batch scheduling in order to minimise the total machine workload without violating due dates and the limited machine capacity restrictions. Since the BTSP involves constraints on unequal ready time, batch dependent processing time, and sequence dependent setup times, it is more difficult to solve than the classical parallel batch processing machine scheduling problem with compatible product families or incompatible product families. These restrictions mean that the existing methods cannot be applied into real-world factories directly. Consequently, this paper proposes a mixed integer programming model to solve the BTSP exactly. In addition, two efficient solution procedures which solve the BTSP are also presented.     

MILP-based campaign scheduling in a specialty chemicals plant: a case study

Supply chain management in chemical process industry focuses on production planning and scheduling to reduce production cost and inventories and simultaneously increase the utilization of production capacities and the service level. These objectives and the specific characteristics of chemical production processes result in complex planning problems. To handle this complexity, advanced planning systems (APS) are implemented and often enhanced by tailor-made optimization algorithms. In this article, we focus on a real-world problem of production planning arising from a specialty chemicals plant. Formulations for finished products comprise several production and refinement processes which result in all types of material flows. Most processes cannot be operated on only one multi-purpose facility, but on a choice of different facilities. Due to sequence dependencies, several batches of identical processes are grouped together to form production campaigns. We describe a method for multicriteria optimization of short- and mid-term production campaign scheduling which is based on a time-continuous MILP formulation. In a preparatory step, deterministic algorithms calculate the structures of the formulations and solve the bills of material for each primary demand. The facility selection for each production campaign is done in a first MILP step. Optimized campaign scheduling is performed in a second step, which again is based on MILP. We show how this method can be successfully adapted to compute optimized schedules even for problem examples of real-world size, and we furthermore outline implementation issues including integration with an APS.     

基于流转批量的动态制造提前期算法研究

通过对企业资源计划(ERP)应用中生产过程的流转批量、加工批量与提前期的关系进行分析研究，以离散制造业中的批量生产为应用背景，结合批量因素提出多流转批次下的制造提前期计算方法，以流转批次反映工序间不同生产能力的相互影响，使车间作业中流转批量因素能在生产计划阶段通过提前期反映出来，以降低生产计划与车间作业计划之间的误差。并以实例应用说明该方法在生产计划制定中取得的效果。     

Scheduling a batch processing machine with non-identical job sizes

The problem of scheduling batch processors is important in some industries and, at a more fundamental level, captures an element of complexity common to many practical scheduling problems. We describe a branch and bound procedure applicable to a batch processor model with arbitrary job processing times, job weights and job sizes. The scheduling objective is to minimize total weighted completion time. We find that the procedure returns optimal solutions to problems of up to 25 jobs in reasonable CPU time, and can be adapted for use as a heuristic for larger problems.     

Campaign planning for multi-stage batch processes in the chemical industry

Inspired by a case study from industry, the production of special chemical products is considered. In this industrial environment, multi-purpose equipment is operated in batch mode to carry out the diverse processing tasks. Often, extensive set-up and cleaning of the equipment are required when production switches between different types of products. Hence, processes are scheduled in campaign mode, i.e. a number of batches of the same type are processed in sequence. The production of chem ical products usually involves various stages with significant cumulative lead times. Typically, these production stages are assigned to different plants. A hierarchical modelling approach is presented which co-ordinates the various plant operations within the entire supply network. In the first stage, the length of the campaigns, their timing, the corresponding material flows, and equipment requirements have to be determined. At this stage, an aggregation scheme based on feasibility constraints is employed in order to reflect the limited availability of the various types of production equipment. The second stage consists of an assignment model, which allocates the available equipment units between the production campaigns determined in the first stage of the solution procedure. In the third stage, resource conflicts are resolved, which may occur if clean-out operations and minimal campaign lengths have to be considered. The proposed hierarchical approach allows a more compact model formulation compared to ot her approaches known from the literature. As a result, a very efficient and flexible solution approach is obtained. In particular, commercially available standard solvers can be used to solve a wide range of campaign planning problems arising in the chemical industry.