Modeling and optimization of the intermodal terminal Mega Hub

The convergence of European states can be expected to lead to an increase in the trading of goods within the next few years and thus to a growing demand for transport. Overland intermodal transport is an important development, because it combines the advantages of rail for long distance transportation with the effective area cover offered by road. Different terminal concepts and production forms have been developed to increase the flexibility of intermodal transport and to make it more attractive for the customer. The intermodal terminal concept investigated in this paper is called Mega Hub. The configuration and the control of the terminal is a complex and challenging task. Here, the terminal is modeled as a multi-stage transshipment problem. In this approach, sequence-dependent duration of empty moves, alternative assignments (of containers to cranes) and a sequence-dependent number of operations have to be handled. An optimization model based on Constraint Satisfaction is formulated and heuristics for the search procedure, especially value and variable ordering are developed. Received: May 2, 2000 / Accepted: July 4, 2001     

Resource–constrained project scheduling: a heuristic for the multi–mode case

This paper presents a heuristic solution procedure for a very general resource–constrained project scheduling problem. Here, multiple execution modes are available for the individual activities of the project. In addition, minimum as well as maximum time lags between different activities may be given. The objective is to determine a mode and a start time for each activity such that the temporal and resource constraints are met and the project duration is minimized. Project scheduling problems of this type occur e.g. in process industries. The heuristic is a multi–pass priority–rule method with backplanning which is based on an integration approach and embedded in random sampling. Its performance is evaluated within an experimental performance analysis for problem instances of real–life size with 100 activities and up to 5 modes per activity.

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Received: September 22, 2000 / Accepted: May 18, 2001     

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     

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.     

求解集装箱装载问题的混合蚁群模拟退火算法

目的 针对物流行业中存在的大规模、复杂、多规格货物的集装箱装载问题,提出一种基于塔装载启发式算法、二维装载点启发式算法、蚁群模拟退火算法的混合算法。方法 首先,采用塔装载启发式算法将三维待装箱装载成塔集,即将三维装箱问题降为二维装箱问题,有效降低集装箱的装载规模;其次,蚁群算法通过融入信息素选择更新策略,并利用自适应信息素挥发系数来提升算法整体的收敛速度,同时结合模拟退火算法对每代优秀路径集进行局部搜索,避免算法因收敛过快而陷入局部最优;最后,将蚁群模拟退火算法与二维装载点启发式算法相结合,优化每座塔的装载顺序和放置姿态,寻找最优的装载方案。结果 实验证明,在250组算例中,采用混合算法后,集装箱的平均空间利用率为90.92%,优于其他3种对比算法。结论 设计的混合蚁群模拟退火算法适用于解决大规模集装箱装载问题。     

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     

Designing Assemblies

This paper presents a theory to support the design of assemblies. It brings together prior work in a new synthesis, resulting in a top-down process for designing assemblies so that they deliver geometric Key Characteristics (KCs) that achieve top level customer requirements. The theory applies to assemblies that take the form of mechanisms (e.g. engines) or structures (e.g. aircraft fuselages), but has less relevance to assemblies that take the form of connective or distributive systems (e.g. hydraulic piping). The theory shows how kinematically constrained (statically determinate) assemblies can be unambiguously designed to satisfy geometrically-defined customer requirements. The top-down process presented here begins by creating a kinematic constraint structure and a systematic scheme by which parts are located in space relative to each other, followed by declaration of assembly features that join parts in such a way as to create the desired constraint relationships. This process captures design intent by creating a connective data model that contains information to support relevant analyses such as variation buildup, constraint analysis, and establishment of constraint-consistent assembly sequences. Adjustable assemblies, assemblies built using fixtures, and selective assemblies can also be described by this theory. Problems arising from multiple KCs and KC conflict can be identified. Issues unresolved by the theory are also noted.     

Improving Systems by Combining Axiomatic Design, Quality Control Tools and Designed Experiments

This paper presents an approach for solving design problems in existing designs. A design analysis with Axiomatic Design, called Design Object Analysis, describes a product or a system in terms of Customer Needs (CNs), Functional Requirements (FRs), Design Parameters (DPs) and Process Variables (PVs), as well as their associated Design Matrices (DMs). In this paper, the design analysis is combined with a thorough investigation of possible problems within the design, utilizing the seven quality tools, noise factor analysis, and designed experiments to form an approach for quality improvements and problem solving. The Design Object Analysis helps secure valid input-factors to the designed experiments, and the designed experiments correct or improve the assumptions made in the Design Object Analysis. Thus, a combination of product modeling by Axiomatic Design and designed experiments overcomes shortcomings of the two methods. The benefits of performing a Design Object Analysis, as compared to other methods, become clear when it comes to evaluating the results from the designed experiment, and preventing the problem. Once the critical parameters are confirmed, and the design matrices are updated, suggested design improvements can then be checked against the design matrices, and the system effect of a design-change-order can be estimated. The approach described in this paper was successfully applied and verified in a case study at a large automotive company.     

Basic considerations on the calculation of the trippage number for returnable containers

The trippage number of returnable containers has a central position in economic, calculations and in comparing the environmental impacts of disposable and returnable container systems. Extremely varying trippage numbers are frequently reported for the same type of container, usually without any reference to the method of calculation. Only a few authors have published basic considerations on the calculation of the trippage number. Different methods of calculation have been recommended and different definitions of what should be understood by the ‘trippage number’ have been given. To investigate what is really achieved by these kinds of calculation, a model system, performing all the functions of a returnable container system in practice, for system analysis by computer simulation was established. By means of this model system it was found that in the lifetime of a container population one can differentiate between three kinds of populations, each one with an individual mean trippage number:

• (i) the average trippage number of all containers put into circulation
• (ii) the average trippage number of all lost containers
• (iii) the average trippage number of all existing containers, representing the total stock of circulating containers

Emanating from these basic considerations, it was found that the actual average trippage number of all containers put into circulation after ‘x’ fillings (U_x) can be calculated exactly by the quotientof the sum of all filled containers (ΣN) and the sum of all containers put into circulation (ΣE) since the first day of use of a container type according to the following expression It must be stated that correct results can only be achieved by taking into account all containers filled or put into circulation from the very first day of use. Despite this drawback, this kind of calculation has the advantage of being based on figures that are available and easily retraceable for almost all filling-plants. After the first circulation, the average trippage number (U_x) is equal to 1.0, rising with each circulation because of its tendency to approximate to a constant final value after an infinite number of fillings (U_x→∞), which can be calculated by the quotient of the sum of all filled containers (ΣN) and the sum of all lost containers (ΣA) since the first day of use of a container type, represented by the inverse value of the proportion of lost containers (a) or its complement, the share of refillable containers (r), according to This (theoretical) final value is approximated by each partial population (the population of all input new containers, of all lost and all existing containers) but with a different velocity, dependent on the parameters a=1?r (the loss rate) and r_z =1?b (the rate of containers returning to the filling-plant from the total stock of existing containers). The number of circulations needed to approach this final value rises with a decreasing share of lost containers (a) and with a decreasing share of containers returning to the filling plant (r_z) from the total container stock. The calculation by the inverse value of the share of lost containers (l/a ) is therefore not relevant to the calculation of the actual average trippage number of any partial population (number of new containers put into circulation, lost containers or stock of all existing containers). To receive correct results, also for this calculation, it is necessary to take into account all containers filled or lost from the very first day of use. This may be troublesome with a container type used by many filling-plants and with traditional old containers, where the necessary figures are no longer available. A further drawback of this kind of calculation is that the number of lost containers cannot be calculated exactly, because of the uncertainty of whether all containers, for which the plant holds the deposit, exist or not. Therefore the share of lost containers is mostly set too low, which results in the calculation of too high a trippage number.     

Numerical Modeling of a Post & Dome Snap-Fit Feature

Snap-fits are often designed using guides that rely on classic beam theory, with the basic assumption that the beam undergoes small rotations and displacements. This is a poor assumption, for they typically experience both large rotations and displacements due to loading offset from the neutral axis and axial loading. This paper investigates the performance of the post & dome feature, establishes its nomenclature, and derives the equations needed to intelligently design different variations of it. The post & dome feature was selected for analysis because it is a high performance snap-fit that is self-datuming and can withstand some shear loading in addition to retention. The design equations were generated in three steps. First, an experimental array was created using a design of experiments approach. Finite element methods and multiple regression techniques were used in lieu of beam equations models for each of the trials in the experimental array. Finally, response surface methods were used to develop response curves based on the performance data generated by the finite element models. Sensitivity data was plotted for both the main effects and selected variable interactions. The traditional benchmarks for defining high performance snap-fits are retention strength, insertion force, and insertion strain. This paper uses an expanded definition of these benchmarks that also includes locking ratio (the ratio of retention force to insertion force).     

Icelandic experience in storing fish in chilled seawater

Several species of eviscerated or ungutted fish have been the subject of storage trials in containers filled with chilled sea water (CSW), container capacities ranging from 600 to 40 000 l. A study was made of onboard conditions in a fishing vessel, in order to test the reaction of fishermen to this new technique. The internal temperatures of containers were recorded and the storage duration was assessed in terms of organoleptical, chemical (trimethylamine) and bacteriological parameters. In most of the experiments, the control samples were fish held in ice in 70 to 90 l plastic boxes; the results of experiments on storage durations have confirmed earlier results. Storage durations for fish held in CSW compare with those of ice storage, but in the case of onboard handling operations, container storage is easier and involves less labour. The relationship ice-water-fish comes out at 15-15-70.Ice can be stored onboard in insulated containers which can also be used to store fish on land prior to transformation. It will be necessary to convince fishermen of the technical advantages of this technique but, in any event, major alterations will have to be made to unloading constructions and installations before CSW container-storage can be generally accepted.     

Determination of the number of containers,production kanbans and withdrawal kanbans; and scheduling in kanban flowshops - Part 2

We consider the problem of determination of the number of containers or container size, production kanbans and withdrawal kanbans, and scheduling of containers in presence of material handling between workstations, and dual blocking mechanisms operative on both workstations and material handling. A heuristic is proposed to address the problem of determining the number of containers, and production and withdrawal kanbans, and also determining the sequence of containers to minimize the sum of weighted flowtime, weighted earliness and weighted tardiness of containers. Recursive equations, developed in part 1 of this paper, are used for time-tabling of the sequence of containers. An extensive performance evaluation of the heuristic and a benchmark procedure, based on random search, has been carried out by generating a number of problems of various sizes.     

Advanced methods for container stacking

In this paper, we study stacking policies for containers at an automated container terminal. It is motivated by the increasing pressure on terminal performance put forward by the increase in the size of container ships. We consider several variants of category stacking, where containers can be exchanged during the loading process. The categories facilitate both stacking and online optimization of stowage. We also consider workload variations for the stacking cranes.     

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     

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.     

Flexible space-sharing strategy for storage yard management in a transshipment hub port

In this paper, a storage yard planning problem is studied for a transshipment port with limited space and high-throughput level. Generally, the consignment strategy is used in the yard for a transshipment port, where containers to the same destination vessel are stored together. This is to facilitate faster loading process as it reduces reshuffles as well as long distance movements of yard cranes. However, the consignment strategy is known to be inefficient in space utilization since each storage location must be dedicated to a particular vessel. To improve the space utilization while retaining the advantage of consignment, a new approach named the “flexible space-sharing strategy” is proposed. The idea is that the container space can be shared by two different vessels as long as their containers do not occupy the space at the same time. This strategy allows the same storage location to be reserved for two vessels. The amount of space will only be allocated to a specific vessel on the arrival of corresponding containers. By controlling where to stack the containers in the storage locations, the containers to each vessel are not mixed and the consignment feature can be preserved. This strategy is first formulated as a mixed integer program (MIP). As the MIP model has a block diagonal structure, we develop a search algorithm which combines MIP and heuristics to find the solution. The numerical experiments show that the “flexible space-sharing strategy” can handle much more containers within the same storage space compared with the “non-sharing strategy”.     

A yard storage strategy for minimizing traffic congestion in a marine container transshipment hub

This paper studies a storage yard management problem in a transshipment hub where the loading and unloading activities are both heavy and concentrated. In order to reduce the number of reshuffles, which helps to reduce the vessel turnaround time, the port operator uses the consignment strategy to group export and transshipment containers according to their destination vessel. To reduce the potential traffic congestion of prime movers, a high–low workload balancing protocol is used. A mixed integer programming model is formulated to determine the storage locations of incoming containers, the number of incoming containers and the smallest number of yard cranes to deploy in each shift. An iterative improvement method is developed to solve the problem, in which a tabu search based heuristic algorithm is used to generate an initial yard template, and then the generated yard template is improved by an improvement algorithm iteratively until an optimal or a satisfactory solution is obtained. Experiment results show that the proposed method can generate excellent results within a reasonable time, even for the extreme cases.     

Method to estimate the consumption of ice during fish storageMéthode d'estimation de la consommation de glace au cours de l'entreposage du poisson

The amount of ice needed for the storage of fresh fish is economically important especially in warm climates. It is affected by the insulation of the fish boxes and containers and by the procedures used in the handling and transporting of iced fish. This paper describes a simple method to estimate ice consumption during fish storage. The method was tried with plastic boxes containing hake (Merluccius hubbsi) when ice losses were caused by heat transfer due only to natural convection. It can be used also to test any kind of box or container, and can give valuable indications with other heat transfer conditions.