Planning and scheduling in the process industry

Since there has been tremendous progress in planning and scheduling in the process industry during the last 20 years, it might be worthwhile to give an overview of the current state-of-the-art of planning and scheduling problems in the chemical process industry. This is the purpose of the current review which has the following structure: we start with some conceptional thoughts and some comments on special features of planning and scheduling problems in the process industry. In Section 2 the focus is on planning problems while in Section 3 different types of scheduling problems are discussed. Section 4 presents some solution approaches especially those applied to a benchmark problem which has received considerable interest during the last years. Section 5 allows a short view into the future of planning and scheduling. In the appendix we describe the Westenberger-Kallrath problem which has already been used extensively as a benchmark problem for planning and scheduling in the process industr y.     

Combined strategic and operational planning – an MILP success story in chemical industry

We describe and solve a real world problem in chemical industry which combines operational planning with strategic aspects. In our simultaneous strategic & operational planning (SSDOP) approach we develop a model based on mixed-integer linear (MILP) optimization and apply it to a real-world problem; the approach seems to be applicable in many other situations provided that people in production planning, process development, strategic and financial planning departments cooperate. The problem is related to the supply chain management of a multi-site production network in which production units are subject to purchase, opening or shut-down decisions leading to an MILP model based on a time-indexed formulation. Besides the framework of the SSDOP approach and consistent net present value calculations, this model includes two additional special and original features: a detailed nonlinear price structure for the raw material purchase model, and a detailed discussion of transport times with respect to the time discretization scheme involving a probability concept. In a maximizing net profit scenario the client reports cost saving of several millions US$. The strategic feature present in the model is analyzed in a consistent framework based on the operational planning model, and vice versa. The demand driven operational planning part links consistently to and influences the strategic. Since the results (strategic desicions or designs) have consequences for many years, and depend on demand forecast, raw material availability, and expected costs or sales prices, resp., a careful sensitivity analysis is necessary showing how stable the decisions might be wit h respect to these input data.     

An MILP-based approach to the short-term scheduling of make-and-pack continuous production plants

Many fast moving consumers good manufacturing companies produce a moderate number of intermediates that are combined in many different ways to generate an enormous variety of end products. To do that, such companies usually run continuous production plants in a make-to-stock environment. The process structure includes a fabrication area yielding basic intermediates that are stocked in a large middle storage space, and a packing sector where finished products usually comprising several intermediates are manufactured. Intermediates all undergo the same sequence of processing stages and the production of any campaign is sequentially allocated to an ordered set of end products. An MILP continuous time scheduling problem formulation handling independently assignment and sequencing decisions and considering sequence-dependent setup times and specific due dates for export orders has been developed. The problem objective is to meet all end-product demands at minimum make-span. The proposed model is able to account for assorted products, multiple campaigns for a particular intermediate even at the same unit and the consecutive allocation of an intermediate campaign to different finished products. Moreover, it can easily embed powerful preordering rules to yielding reduced MILP formulations so as to tackle real-world industrial problems at low computational cost. The approach has been successfully applied to large-scale industrial examples. RID="*" ID="*" The authors acknowledge financial support from FONCYT under Grant 14-07004, and from “Universidad Nacional del Litoral” under CAI+D 121. Correspondence to: J. Cerdá     

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.     

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.     

A scheduling method for Berth and Quay cranes

This paper discusses a method for scheduling Berth and Quay cranes, which are critical resources in port container terminals. An integer programming model is formulated by considering various practical constraints. A two-phase solution procedure is suggested for solving the mathematical model. The first phase determines the Berthing position and time of each vessel as well as the number of cranes assigned to each vessel at each time segment. The subgradient optimization technique is applied to obtain a near-optimal solution of the first phase. In the second phase, a detailed schedule for each Quay crane is constructed based on the solution found from the first phase. The dynamic programming technique is applied to solve the problem of the second phase. A numerical experiment was conducted to test the performance of the suggested algorithms. RID="*" ID="*" This research has been supported in part by Brain Korea 21 Program (1999–2002). Correspondence to: Y.-M. Park     

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.     

Strategic technology planning in hospital management

Abstract. Hospital managers regularly are confronted with their patients' demand for increased service performance on the one hand and tight budgets on the other. As medical technologies influence both the costs and the service spectrum, strategic technology planning is among the critical tasks in hospital management. This paper addresses the corresponding problem of selecting the “best” portfolio of medical devices and proposes an appropriate decision support system. It starts with a multiobjective mathematical programming model that determines the solution space of all efficient portfolios of medical technology investments. The members of the planning committee with their diverging interests then are supported by their interactive exploration of that space until they jointly reach a satisfying portfolio and are not willing to make any further compromises between its objective levels. As one of its salient features the system does not require a priori preference information. RID="*" ID="*" A previous version of this paper has been presented at the Wiesbaden 2000 Meeting of the GOR-Working Group “Operations Research in Health Care”. The authors thank Andrea Lührs and Thomas Rollins, both from Siemens AG, for their valuable support. Moreover, thanks are due to the anonymous referees for their comments on a previous version of this paper. Correspondence to: C. Stummer     

Aggregate planning in hybrid flowshops

We present a linear programming based heuristic for the solution of a class of aggregate level planning problems in hybrid flowshops (flowshops with several machines per stage). First, the general planning problem is modelled as multi-level with parallel processors, multi-item, capacitated, lot-sizing with set up times. We suggest a hierarchical approach which sequentially loads the stages; each stage is constrained by the solution of its preceding stage and each stage is treated as a multi-item, capacitated, lot-sizing problem with setup times on parallel processors. We show how this latter problem may be reformulated and solved heuristically as a sequence of network problems (trans-shipment problems) in which the amount of capacity lost in setups is fixed for each period and each processor. The model is within the computing reach of a PC.     

Capacity coordination in hybrid make-to-stock/make-to-order production environments

One of the most important decisions in hybrid make-to-stock/make-to-order (MTS/MTO) production systems is capacity coordination. This paper addresses capacity coordination of hybrid MTS/MTO production systems which deal with MTS, MTO and MTS/MTO products. The proposed model is developed to cope with order acceptance/rejection policy, order due-date setting, lot-sizing of MTS products and determining required capacity during the planning horizon. Additionally, a backward lot-sizing algorithm is developed to tackle the lot-sizing problem. The proposed model presents a general framework to decide on capacity coordination without too many limiting mathematical assumptions. The model combines qualitative and quantitative modules to cope with the aforementioned problems. Finally, a real industrial case study is reported to provide validity and applicability of the proposed model. Having the model applied in the case study, considerable improvement was achieved.     

Integrated planning of acquisition,disassembly and bulk recycling: a case study on electronic scrap recovery

Abstract. Due to national and supranational legislation activities, the recovery of discarded products will attain an increasing momentum. Electronic equipment consists of many different parts and materials. Therefore, the related recovery process is often divided into disassembly to remove harmful substances or reusable parts and into bulk recycling to recover ferrous and non-ferrous metals. In order to consider the interactions between choice of scrap to be recovered (acquisition problem), disassembly and bulk recycling, a mixed-integer linear programming model for integrated planning of these stages is presented in this case study. It is applied to determine the daily allocation of products to processes for a major electronic scrap recovery centre that faces limited processing capacities and market restrictions. The optimization calculations covering typical discarded electronic products to be recycled in the related centre lead to a relevant improvement of the economic success. RID="*" ID="*" The authors would like to thank the German “Bundesministerium für Bildung und Forschung” (Federal Ministry of Education and Research) for supporting the research project “Substance Flow Oriented Closed Loop Supply Chain Management in the Electrical and Electronic Equipment Industry (STREAM)rdquo;. Correspondence to: T. Spengler     

Intensity-modulated radiotherapy – a large scale multi-criteria programming problem

Abstract. Radiation therapy planning is often a tight rope walk between dangerous insufficient dose in the target volume and life threatening overdosing of organs at risk. Finding ideal balances between these inherently contradictory goals challenges dosimetrists and physicians in their daily practice. Todays inverse planning systems calculate treatment plans based on a single evaluation function that measures the quality of a radiation treatment plan. Unfortunately, such a one dimensional approach cannot satisfactorily map the different backgrounds of physicians and the patient dependent necessities. So, too often a time consuming iterative optimization process between evaluation of the dose distribution and redefinition of the evaluation function is needed. In this paper we propose a generic multi-criteria approach based on Pareto's solution concept. For each entity of interest – target volume or organ at risk – a structure dependent evaluation function is defined measuring deviations from ideal doses that are calculated from statistical functions. A reasonable bunch of clinically meaningful Pareto optimal solutions are stored in a data base, which can be interactively searched by physicians. The system guarantees dynamic planning as well as the discussion of tradeoffs between different entities. Mathematically, we model the inverse problem as a multi-criteria linear programming problem. Because of the large scale nature of the problem it is not possible to solve the problem in a 3D-setting without adaptive reduction by appropriate approximation schemes. Our approach is twofold: First, the discretization of the continuous problem results from an adaptive hierarchical clustering process which is used for a local refinement of constraints during the optimization procedure. Second, the set of Pareto optimal solutions is approximated by an adaptive grid of representatives that are found by a hybrid process of calculating extreme compromises and interpolation methods. Correspondence to: K.-H. Küfer     

Conceptual framework for lot-sizing and scheduling of flexible flow lines

The paper considers the lot-sizing and scheduling problem for flexible flow line production facilities. Flexible flow lines are flow lines with parallel machines on some or all production stages. The objective is to find lot-sizes and a feasible schedule that minimizes set-up, inventory holding and back-order costs. Minimization of mean flow time (measured between entering the first stage and leaving the last) is a secondary objective. An integrative solution approach is presented that can incorporate the interdependencies between lot-sizing and scheduling decisions. Applications of the approach in the semiconductor industry are illustrated.     

Risk hedging via options contracts for physical delivery

We develop an analytical framework for the valuation of options contracts for physical delivery that enable risk-sharing between the trading partners. The spot market price risk, the buyer's demand risk and the seller's marginal cost risk, which are key to many industrial settings such as the chemical industry, are explicitly incorporated. Analytical expressions for the buyer's optimal reservation quantity and the seller's optimal tariff are derived and related to the risk management needs in the industry. The ensuing discussion shows how contingency contracts for physical delivery can complement financial derivative instruments within a company's risk management approach.     

Dynamic capacitated lot-sizing problems: a classification and review of solution approaches

This paper presents a review of four decades of research on dynamic lot-sizing with capacity constraints. We discuss both different modeling approaches to the optimization problems and different algorithmic solution approaches. The focus is on research that separates the lot-sizing problem from the detailed sequencing and scheduling problem. Our conceptional point of reference is the multi-level capacitated lot-sizing problem (MLCLSP). We show how different streams of research emerged over time. One result is that many practically important problems are still far from being solved in the sense that they could routinely be solved close to optimality in industrial practice. Our review also shows that currently mathematical programing and the use of metaheuristics are particularly popular among researchers in a vivid and flourishing field of research.     

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     

Plant co-ordination in pharmaceutics supply networks

The production of active ingredients in the chemical-pharmaceutical industry involves numerous production stages with cumulative lead times of up to two years. Mainly because of rigorous purity requirements and the need of extensive cleaning of the equipment units, production is carried out in campaigns, i.e. multiple batches of the same product type are produced successively before changing to another product type. Each campaign requires a specific configuration of equipment units according to the recipes of the particular chemical process. In the chemical-pharmaceutical industry, production stages are often assigned to different locations, even different countries. Hence the co-ordination of plant operations within the resulting multi-national supply network is of major importance. A key issue is the co-ordination of campaign schedules at different production stages in the various plants. In practice, it is almost impossible to determine exact optimal solutions to the corresponding complex supply network problem with respect to overall logistics costs. In order to reduce the required computational effort, we introduce several aggregation schemes and a novel MILP model formulation which is based on a continuous representation of time. Moreover, we propose an iterative near-optimal solution procedure which can be successfully applied to even exceptionally large real life problem instances. The applicability of the approach suggested is shown using a case study from industry. Correspondence to: H.-O. Günther     

Simulation of random tool lives in metal cutting on a flexible machine

Much research into multi-level lot-sizing problems has concentrated on fixed horizon problems ignoring more realistic conditions such as appending new demand(s) in the planning periods. The aim of this paper is to illustrate that a heuristic, which was developed earlier by the authors, is applicable to the multilevel lot-sizing problem with a bottleneck under a rolling schedule environment. The results illustrate that the Silver-Meal (SM) approach performed better than the Economic Order Quantity (EOQ) approach in terms of cost measures, when incorporated within the heuristic procedure. The results also show that, although the total cost of the problem is increased in a rolling schedule environment, the heuristic continues to perform favourably in producing low cost solutions.     

More than Just Robust Design: Why Product Development Organizations Still Contend with Variation and its Impact on Quality

Current market conditions require design and manufacturing companies to continually increase product functionality, reduce design cycles, decrease cost and improve quality. One way to improve quality is to minimize the impact part and process variation has on final product quality. Although companies know they must reduce variation, they are still struggling with executing coherent variation management strategies. To understand why companies still fail to systematically address variation, an ideal model of variation management is proposed, entitled Variation Risk Management (VRM). This model was used to assess the state of industry practice. These results are compared to the current literature available on the subject. It was found that many problems with industry implementation are due to a lack of quantitative models that enable a design team to make quick and accurate decisions. This paper concludes with a list of interesting challenges facing the VRM field.     

A dispatching method for automated guided vehicles by using a bidding concept

A dispatching method is suggested for automated guided vehicles by using an auction algorithm. The dispatching method in this study is different from traditional dispatching rules in that it looks into the future for an efficient assignment of delivery tasks to vehicles and also in that multiple tasks are matched with multiple vehicles. The dispatching method in this study is distributed in the sense that the dispatching decisions are made through communication among related vehicles and machines. The theoretical rationale behind the distributed dispatching method is also discussed. Through a simulation study, the performance of the method is compared with that of a popular dispatching rule. RID="*" ID="*" The research was financially supported by the Sasakawa Scientific Research Grant from The Japan Science Society. The original version of the simulation program is provided by Professor Jae Yeon Kim at Dong Yang University, Korea. Correspondence to: J. K. Lim