A THRESHOLD-ACCEPTING METAHEURISTIC METHOD FOR SCHEDULING THE OPERATIONS OF DEHYDRATION PLANTS

ABSTRACT

Dehydration plants are broadly characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed sequentially so that demand constraints are met. Processing of raw materials is implemented through a series of preprocessing operations that together with drying constitute the production procedure of a pre-specified programme. The core of the manufacturing system that a typical dehydration plant involves, is scheduling of operations so that demand is fulfilled within a pre-determined time horizon imposed by production planning. The typical scheduling operation that dehydration plants involve can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of dehydration plants, termed as the Backtracking Adaptive Threshold Accepting (BATA) method. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of this method, towards a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. BATA is described in detail while a characteristic job shop scheduling case study for dehydration plant operations is presented.     

AN EFFICIENT META-HEURISTIC ALGORITHM FOR ROUTING PRODUCT COLLECTING VEHICLES OF DEHYDRATION PLANTS. I. ALGORITHM DEVELOPMENT

Dehydration plants are characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed in parallel so that demand constraints are met. The Just-In-Time production planning policy of these plants require the collection of raw materials to be dehydrated shortly before the actual processing, immediately after harvesting. One of the most important aspects in collection of plant fresh products, is routing of collecting vehicles, so that total collection time is minimized. The aim of this study is to describe a new stochastic search meta-heuristic algorithm for solving the Vehicle Routing Problem (VRP), termed as the Backtracking Adaptive Threshold Accepting (BATA) algorithm. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of the algorithm, toward a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. This adaptation of the value of the threshold, plays an important role in finding high quality routing solutions. BATA is described in detail while its performance and characteristic case studies are presented by Tarantilis and Kiranoudis (2000).     

AN EFFICIENT META-HEURISTIC ALGORITHM FOR ROUTING PRODUCT COLLECTING VEHICLES OF DEHYDRATION PLANTS. I. ALGORITHM DEVELOPMENT

Dehydration plants are characterized by a multi-product nature chiefly attributed to the utilization of different raw materials to be processed in parallel so that demand constraints are met. The Just-In-Time production planning policy of these plants require the collection of raw materials to be dehydrated shortly before the actual processing, immediately after harvesting. One of the most important aspects in collection of plant fresh products, is routing of collecting vehicles, so that total collection time is minimized. The aim of this study is to describe a new stochastic search meta-heuristic algorithm for solving the Vehicle Routing Problem (VRP), termed as the Backtracking Adaptive Threshold Accepting (BATA) algorithm. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions, requiring reasonable computing effort. The main innovation of the algorithm, toward a typical threshold accepting algorithm, is that during the optimization process the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. This adaptation of the value of the threshold, plays an important role in finding high quality routing solutions. BATA is described in detail while its performance and characteristic case studies are presented by Tarantilis and Kiranoudis (2000).     

AN EFFICIENT META-HEURISTIC ALGORITHM FOR ROUTING PRODUCT COLLECTING VEHICLES OF DEHYDRATION PLANTS. II. ALGORITHM PERFORMANCE AND CASE STUDIES

Routing of vehicle fleet for collecting newly cropped raw materials for multi-product dehydration plants is a component of plant production schedule of utmost significance. A meta-heuristic algorithm for efficiently solving the collecting vehicle routing problem was developed and analyzed in detail in Tarantilis and Kiranoudis (2000). Meta-heuristic algorithms are broadly characterized by a stochastic nature in producing tender solution configurations in linear search terms, which sweep the huge solution space in a guided and rational way. Algorithm performance is examined through an analysis of the impact of model parameters on solution procedure during the execution of typical routing problems. The most important model parameter examined was found to be the value of the initial threshold as well as the way that the value of this actual parameter is appropriately adjusted during the optimization process. The main characteristic of the algorithm is the way that threshold is not only lowered but also raised, or backtracked, depending on the success of the inner loop iterations to provide for an acceptable new solution that would replace an older one. An important feature of the algorithm is the fact that appearance of better configurations within a process run is distributed according to the Poisson probability distribution. The suggested algorithm is tested against typical literature benchmarks as well against real-world problem encountered in the production planning procedures of dehydration plants in Greece.     

AN EFFICIENT META-HEURISTIC ALGORITHM FOR ROUTING PRODUCT COLLECTING VEHICLES OF DEHYDRATION PLANTS. II. ALGORITHM PERFORMANCE AND CASE STUDIES

Routing of vehicle fleet for collecting newly cropped raw materials for multi-product dehydration plants is a component of plant production schedule of utmost significance. A meta-heuristic algorithm for efficiently solving the collecting vehicle routing problem was developed and analyzed in detail in Tarantilis and Kiranoudis (2000). Meta-heuristic algorithms are broadly characterized by a stochastic nature in producing tender solution configurations in linear search terms, which sweep the huge solution space in a guided and rational way. Algorithm performance is examined through an analysis of the impact of model parameters on solution procedure during the execution of typical routing problems. The most important model parameter examined was found to be the value of the initial threshold as well as the way that the value of this actual parameter is appropriately adjusted during the optimization process. The main characteristic of the algorithm is the way that threshold is not only lowered but also raised, or backtracked, depending on the success of the inner loop iterations to provide for an acceptable new solution that would replace an older one. An important feature of the algorithm is the fact that appearance of better configurations within a process run is distributed according to the Poisson probability distribution. The suggested algorithm is tested against typical literature benchmarks as well against real-world problem encountered in the production planning procedures of dehydration plants in Greece.     

浅谈化工企业检修作业事故的成因与防范措施

化工企业生产过程高温、高压,使用原料等多具有易燃、易爆、有毒、有害、腐蚀性强等特点,所有这些都给化工企业的检修作业提出了高要求。本文主要针对化工企业在检修作业中发生事故的现状,结合本人所在企业检修作业的特点,有针对性的提出在此类生产型企业中较为有常见的检修事故成因和有效的防范措施,以供借鉴。     

改进的生物地理学优化算法在混合流水车间调度中的应用

调度问题是将有限的资源分配给各项不同任务的决策过程,其目的是优化一个或多个目标,它广泛存在于当今大多数的制造和生产系统中。混合流水车间调度问题是一般流水车间调度问题的推广,更接近实际的生产过程。采用一种新型的算法--生物地理学优化算法求解混合流水车间调度问题,通过引入改进策略,增强了算法的全局搜索能力和局部搜索能力,并提高了算法的收敛速度。通过10个标准调度算例的仿真研究,并与遗传算法进行对比,验证了改进后的生物地理学优化算法在求解混合流水车间调度问题方面的优越性。     

基于P-graph的乙烯裂解原料调度建模与优化

乙烯工业不同的裂解装置间存在着设备、技术上的差别，每一种原料在乙烯工厂不同炉型或工艺的裂解装置的乙烯产品收率、能耗也存在着差别。随着新的乙烯工厂的投产，需要同时运行台数众多的差异化裂解装置，从而为通过优化调度乙烯裂解原料实现提高物效、降低能耗提供了空间。对于此类工厂间原料调度及能耗优化问题提出了一种基于P-graph的建模和优化方法(scheduling generation based on P-graph, SGBP算法)，该算法通过P-graph本身提取过程结构信息的能力，在加速求解的同时，保留了次优解集。之后以两个实际的乙烯厂为研究实例，采用提出的SGBP方法实现了原料调度的建模和优化，该方法与MINLP优化算法的对比分析验证了提出方法的优势：(1)可以同时提供较为丰富的最优解与次优解方案；(2)提出方法的最优结果与MINLP的优化效果相当；(3)优化后的整体能耗下降明显，为生产计划人员选择可采用灵活的原料调配方案提供了多种可选择的运行方案。     

Design and Planning Optimization of Multiplant Complexes in the Food Industry

Multiplant complexes are very common in food industry because residues from one plant become raw materials for others, or several plants can use the same raw material or power resource that comes from a plant inside the complex. The integration problem between several plants that produce different products and share diverse resources among them poses significant challenges. In this work, a detailed non linear programming (NLP) model for the design of a multiplant complex is developed considering the integration between plants simultaneously with the optimal operation and production planning of each plant of the multiplant complex. The simultaneous optimization establishes different trade-offs between design, operation, and production planning decisions which are analysed in this paper. In the food industry raw materials availability, product prices, demands and other seasonal matters can vary throughout the production time horizon. For this reason, a multiperiod model is presented in which design and planning decisions have to be adjusted to the posed seasonal scenario. Several cases are presented which correspond to different scenarios. For each case, a detailed analysis of each plant solution and their integration is made.     

一类流水车间调度问题的合作博弈

对多个客户参与的一类流水车间调度问题,研究客户之间以合作的方式建立联盟,通过加工任务重新排序节省生产成本。一般流水车间调度合作博弈是受限制的,提出一类加工时间和工序相关的流水车间调度问题,相应的合作博弈是平衡的,因而具有非空核。从合作博弈理论出发,以优化多客户线性成本为指标,构建了加工时间和工序相关的流水车间调度合作博弈模型。在获得最优调度排列后,提出了一种加权前后边际成本的客户成本分配的方法,证明了该分配方法是加工时间和工序相关的流水车间调度合作博弈的一个核分配。最后通过一个实例对所提出的基于合作博弈的加工时间和工序相关流水车间调度模型及成本分配方法进行了验证。     

Electric conductivity test for quality assessment of aromatic and medicinal plants after drying

For the production of essential oils and aromatics to be possible throughout the year, it is necessary to continuously supply the plant raw material to the industry or store the plant material for a designated time. However, to allow the plant material to remain in storage with a high quality, it is essential to reduce its water content by drying. This has motivated the oil extraction industries to demand a better yield and quality from the drying process and raw materials, necessitating fast and efficient evaluation parameters. Thus, the aim of this study was to develop an electric conductivity test for evaluating the quality of aromatic and medicinal plants and to quantify the essential oil yield of the dry vegetable material. The drying experiments were conducted at different temperatures and air velocities by altering the control of the drying conditions. Various aromatic plants with different plant mass, volume of deionized water, and temperature of the vegetal material were examined by the electric conductivity test. The qualities of the fresh and dry plants were compared through these tests and yields of essential oil. Increase in the drying air temperature influenced the increase in the dehydration rate of the aromatic and medicinal plants. Higher drying temperatures decreased the physical quality as evaluated by the electric conductivity test and essential oil yield of the aromatic and medicinal plants. The parameters for validation of the electric conductivity test methodology in aromatic and medicinal plants were defined as an exposure time of 33?h, 5?g of fresh plant material in 75?mL of deionized water, 1?g of dry plant material in 50?mL of deionized water, and temperature of 25°C. The electric conductivity test was demonstrated as an appropriate method to be used in the quality control of aromatic plants in essential oil extraction industries. The use of the electric conductivity test will enable the oil extraction industry to monitor the yield and quality of the essential oils extracted from the aromatic plants after drying and storage.     

Scheduling dispensing and counting in secondary pharmaceutical manufacturing

In this article, we describe a general methodology for operations scheduling in dispensing and counting departments of pharmaceutical manufacturing plants. The departments are modeled as a multiobjective parallel machines scheduling problem under a number of both standard and realistic constraints, such as release times, due dates and deadlines, particular sequence‐dependent setup times, machine unavailabilities, and maximum campaign size. Main characteristics of the methodology are the modularity of the solution algorithms, the adaptability to different objectives and constraints to fulfill production requirements, the easiness of implementation, and the ability of incorporating human experience in the scheduling algorithms. Computational experience carried out on two case studies from a real pharmaceutical plant shows the effectiveness of this approach. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Efficient multi-product multi-BOM batch scheduling for a petrochemical blending plant with a shared pipeline network

We present an effective scheduling heuristic for realistic production planning in a petrochemical blending plant. The considered model takes into account orders spanning a multi-product portfolio with multiple bills of materials per product, that need to be scheduled on shared production facilities including a complex pipeline network. Capacity constraints, intermediate storage restrictions, due dates, and the dedication of resources to specific product families have to be respected. The primary objective of the heuristic is to minimize the total order tardiness. Secondary objectives include the minimization of pipeline cleaning operations, the minimization of lead times, and the balanced utilization of filling units.The developed algorithm is based on a dynamic prioritization-based greedy search that schedules the orders sequentially. The proposed method can schedule short to mid-term operations and evaluate different plant configurations or production policies on a tactical level. We demonstrate its performance on various real-world inspired scenarios for different scheduling strategies.Our heuristic was used during the construction phase of a new blending plant and was instrumental in the optimal design of the plant.     

Zukunftssichere und umweltneutrale Beseitigung von Industrieabfällen

Low environmental-impact disposal of solid, liquid and semisolid industrial residues . In many production plants, waste residues are formed which must be treated before their disposal so as to safeguard against environmental pollution. Industrial methods for the treatment and disposal of residues are so multifarious that each disposal problem requires careful consideration so as to ensure selection of the most suitable method, both from the ecological and economical point of view. Dumping grounds cannot accommodate all waste materials without endangering or disturbing the environment. For incineration, proven methods are available which can be modified to suit the particular waste problem at hand. After a suitable pretreatment every effort must be made to achieve a waste free from organic materials, an optimum utilization of heat and a flue gas purification without inadmissible loading of waste gases and waters. In recent years there has been a considerable increase in research and development activities in the area of thermal treatment of solid, liquid and semi-solid wastes. Worth mentioning are the high-temperature methods giving favourable emission values and methods for degasification and gasification directed at the recovery of important raw materials. Plants based on these principles are currently being operated on the pilot scale.     

The quality‐constrained scheduling problem in plastics compounding

Interest is increasing in plastic compounding plants that offer tailor‐made resins. Such plants produce a wide range of products in small quantities and with frequent changeovers. The underlying scheduling problem has been extensively researched; however, the concept of incorporating qualities of the finished product in the problem of plastics compounding has not been considered. We express product qualities as an additional problem constraint so that the production schedule ensures product quality. The additional constraint makes this mixed integer nonlinear program (MINLP) problem more difficult to solve. Several case studies are solved to illustrate the utility of the proposed approach. Experiments demonstrated that qualities of the finished product can be ensured a priori if the appropriate relations are developed and integrated in the optimisation model. As well, this paper provides insight into the economic aspects of the scheduling problem under consideration. Experiments showed that none of the cost components (operation, raw material, inventory, penalty or utilities) can alone play the role of the optimisation criterion. © 2012 Canadian Society for Chemical Engineering     

Multi-product batch scheduling with intermediate due dates using priced timed automata models

In this contribution, we discuss an extension of the earlier work on scheduling using reachability analysis of timed automata (TA) models, specifically addressing the problem of tardiness minimization. In the TA-based approach the resources, recipes and additional timing constraints are modeled independently as sets of priced timed automata. The sets of individual automata are synchronized by means of synchronization labels and are composed by parallel composition to form a global automaton. The global automaton has an initial location where no operations have been started and at least one target location where all operations that are required to produce the demanded quantities of end-products within the specified due dates have been finished. A cost-optimal symbolic reachability analysis is performed on the composed automaton to derive schedules with the objective of minimizing tardiness. The model formulation is extended to include release dates of the raw materials and due dates of the production orders. The meeting of due dates is modeled by causing additional costs (e.g. penalties for late delivery and storage costs for early production). The modeling approach and the performance of the approach are tested for two different case studies and the results are compared with that of a MILP formulation solved using the standard solver CPLEX. The numerical experiments demonstrate, that the TA-based approach is competitive compared to standard commercial solvers and good feasible solutions are obtained with considerably reduced computational effort.     

Short-term site-wide maintenance scheduling

Preventive maintenance is essential for every chemical production site to prevent failure and accidents, however, it upsets material and utility flows inside the site and also causes production loss. In order to minimize the loss, maintenance of each plant unit has to be carefully scheduled together with considerations on site-wide material and utility balances. This will involve both production and utility systems, and indeed is a very complicated problem. A scheduling strategy is then employed to handle the problem efficiently. It divides the scheduling into two steps, long-term and short-term. Long-term maintenance scheduling determines the combination of plant shutdown in a period of 2–5 years. Base upon the long-term schedule, a short-term maintenance scheduling optimizes the exact timing of plant shutdown, overhaul, inspection and startup within a maintenance period of 4–10 weeks. Short-term maintenance scheduling involves pre-set utility and material demand profiles during a plant shutdown, overhaul and startup making it a very challenging task. In this paper, a multi-period mixed integer linear programming (MILP) model, a site-model, is proposed as an aid to optimize short-term site-wide maintenance schedule. A special formulation is also developed to deal with the pre-set utility and material demand profiles in the site-model.     

A mathematical programming approach for cyclic production and cleaning scheduling of multistage continuous plants

The objective of this paper is to address the cyclic scheduling of cleaning and production operations in multiproduct multistage plants with performance decay. A mixed-integer nonlinear programming (MINLP) model based on continuous time representation is proposed that can simultaneously optimize the production and cleaning scheduling. The resulting mathematical model has a linear objective function to be maximized over a convex solution space thus allowing globally optimal solutions to be obtained with an outer approximation algorithm. Case studies demonstrate the applicability of the model and its potential benefits in comparison with a hierarchical procedure for the production and cleaning scheduling problem.     

Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty

Scheduling of steelmaking-continuous casting (SCC) processes is of major importance in iron and steel operations since it is often a bottleneck in iron and steel production. In practice, uncertainties are unavoidable and include demand fluctuations, processing time uncertainty, and equipment malfunction. In the presence of these uncertainties, an optimal schedule generated using nominal parameter values may often be suboptimal or even become infeasible. In this paper, we introduce robust optimization and stochastic programming approaches for addressing demand uncertainty in steelmaking continuous casting operations. In the robust optimization framework, a deterministic robust counterpart optimization model is introduced to guarantee that the production schedule remains feasible for the varying demands. Also, a two-stage scenario based stochastic programming framework is investigated for the scheduling of steelmaking and continuous operations under demand uncertainty. To make the resulting stochastic programming problem computationally tractable, a scenario reduction method has been applied to reduce the number of scenarios to a small set of representative realizations. Results from both the robust optimization and stochastic programming methods demonstrate robustness under demand uncertainty and that the robust optimization-based solution is of comparable quality to the two-stage stochastic programming based solution.     

一种求解阻塞型流水车间调度的离散人工蜂群算法(英文)

A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.