Optimal irrigation planning in river basin development: The case of the Upper Cauvery river basin

The study deals with the irrigation planning of the Cauvery river basin in peninsular India which is extensively developed in the downstream reaches and has a high potential for development in the upper reaches. A four-reservoir system is modelled on a monthly basis by using a mathematical programming (LP) formulation to find optimum cropping patterns, subject to land, water and downstream release constraints, and applied to the Cauvery basin. Two objectives, maximizing net economic benefits and maximizing irrigated cropped area, considered in the model are analysed in the context of multiobjective planning and the trade-offs discussed.     

A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry

This paper describes the models, algorithms and implementation results of a computerised scheduling system for the steelmaking-continuous casting process of a steel plant in Austria. The basis for the scheduling task is a preliminary production schedule for the continuous casters (sequence of charges that must be consecutively cast and their allocation to the continuous casters). The scheduling task can be structured as four sub-problems: (1) scheduling the continuous casters. (2) Allocation of the charges to the parallel facilities at the upstream stages (converter and refining facilities). (3) Sequencing the charges at the converters and refining facilities. (4) Exact timing of all operations. The heuristic algorithm consists of three planning levels: (1) scheduling the continuous casters, considering the capacity restrictions at the upstream stages and the limited availability of hot metal. (2) Scheduling of the converter and refining facilities according to priorities, performing allocation and sequencing. (3) Improving the schedule by means of an LP model. The system visualises the schedules as Gantt charts. Extensive numerical tests with real-life data and more than two years of experience with the implementation demonstrate that the system produces reasonable schedules and is accepted by the planners.     

Material requirements planning with flexible bills-of-material

In conventional Material Requirements Planning (MRP), a Bills-of-Materials (BOM) for products is fixed. If time or quantity as stated in the Master Production Schedule (MPS) is not flexible for the final product, flexible BOM may be introduced to compensate for the inflexibility in the MPS. This paper addresses situations where a flexible BOM could be used to deal with unexpected shortages when using MRP to plan for requirements of dependent demand items. The requirements stated in the MPS are met in a timely manner by allowing the substitution of items for one another in the case of a shortage. A linear programming (LP) formulation is provided to help deal, if possible, with a shortage using the flexibility in the BOM. The LP model identifies a ‘mix’ of lower-level items that satisfy both the reality of shortages and ensure as small a deviation from the default BOM as possible. A detailed example in the food production environment is presented to explain how the flexible BOM concept may be applied.     

A hybrid optimisation approach to configure a supply chain for new product diffusion: a case study of multiple-sourcing strategy

Configuring a supply chain for a new product is a challenging task due to the lack of historical demand data and the dynamic/uncertain nature of the new product diffusion process. An integrated supply chain configuration (SCC) and new product diffusion (NPD) model is developed to explicitly account for the impact of demand dynamics during a new product's diffusion on an optimal supply chain configuration. Our hybrid NPD-SCC model allows a manufacturer to source from multiple suppliers, vendors or modes for its supply chain entities. Such a multiple-sourcing approach not only helps the manufacturer to diversify its pool of suppliers and maintain bargaining power, but also builds redundancy into the supply chain to hedge against potential demand surge and supply disruption during the new product life cycle. Through a case study and a comprehensive computational study, we find that although the single-sourcing solution is able to achieve lower unit-manufacturing cost (UMC), the multiple-sourcing approach is superior to single-sourcing on the overall supply chain performance in the environment with random supply disruptions. By building-in redundancy as multiple suppliers and modes, the resultant supply chain has less chance of being disrupted and achieves higher overall profit on average. We also draw several other managerial insights closing the gap between some supply chain operations and marketing strategies.     

A linear programming model to optimize the decision-making to managing cogeneration system

A mathematical linear programming (LP) model was developed to optimize the decision-making for managing a cogeneration facility as a potential clean-development mechanism project in a hospital in Palestine. The model was developed to optimize the cost of energy and the cost of installation of a small cogeneration plant under constraints on electricity-and-heat supply and demand balances. In the model, the sources of electricity are either from cogeneration or public utilities and it was calculated the least cost to supply electricity and heat to the hospital. The hospital is using heat for their operation and that made the application for the cogeneration to be attractive and feasible. In this study, we will develop the LP model and will show the results and the time schedule for the cogeneration. This developed LP model can be used and run to any cogeneration application with little modification.     

The fit of planning methods for the part manufacturing industry,a case study

This paper investigates the specific planning environment of an automotive part manufacturer and characterizes this environment based on variables regarding the product,its demand and the manufacturing process.The fit of different planning methods,namely material requirements planning,reorder point,cyclic planning,kanban,and heijunka to this specific planning environment is analyzed and evaluated by exploratory research.The results show that different product characteristics make it hard to apply a single planning method.None of the investigated methods show the ability to cover the complex requirements of a combined planning environment of two partly divergent product types with a deep value chain.     

A bi-objective model for design and analysis of sustainable intermodal transportation systems: a case study of Turkey

This paper presents a mixed-integer linear optimisation model to analyse the intermodal transportation systems in the Turkish transportation industry. The solution approach includes mathematical modelling, data analysis from real-life cases and solving the resulting mathematical programming problem to minimise total transportation cost and carbon dioxide emissions by using two different exact solution methods in order to find the optimal solutions. The novel approach of this paper generates Pareto solutions quickly and allows the decision makers to identify sustainable solutions by using a newly developed solution methodology for bi-objective mixed-integer linear problems in real-life cases.     

形状误差统一算法的研究

本文运用线性极差和优化理论,将形状误差的评定归为线性规划的求解,研究出简单、精确、有效的统一算法,克服了置换、组合中多次循环的弊端,为基层厂矿提供了有效的实施方法。     

Decision monotonicity in incremental design: A case study of design for manufacture

Design has often been described as searching the space of solutions to a given problem for either a feasible or optimal solution. Usually, this search is conducted in an incremental, iterative manner. Unfortunately, there is a tendency for the feasible space in some domains to exhibit such poor structure that the incremental design process becomes both difficult and time consuming. This is especially true in the domain of design-for-manufacture by machining. In this paper we present the view that in such cases the sources of ill-structure must be aggressively eliminated by making strategic improvements and modifications to the target technology. To illustrate this point, we show that the domain of design-for-manufacture is ill-structured due to a class of interactions known as global interactions. We then show how global interactions can be virtually eliminated by a combination of a new workholding technology and a new interfacing technique. In the absence of global interactions, we show how the design structure exhibits a property of monotonicity. We describe the ramifications of this monotonicity on the design process, and show how design can then be considered to be deterministic. As a backdrop, we draw examples from other fields where similar strategies have resulted in greater designability.     

Production planning problems with joint service-level guarantee: a computational study

We consider a class of single-stage multi-period production planning problems under demand uncertainty. The main feature of our paper is to incorporate a joint service-level constraint to restrict the joint probability of having backorders in any period. This is motivated by manufacturing and retailing applications, in which firms need to decide the production quantities ex ante, and also have stringent service-level agreements. The inflexibility of dynamically altering the pre-determined production schedule may be due to contractual agreement with external suppliers or other economic factors such as enormously large fixed costs and long lead time. We focus on two stochastic variants of this problem, with or without pricing decisions, both subject to a joint service-level guarantee. The demand distribution could be nonstationary and correlated across different periods. Using the sample average approximation (SAA) approach for solving chance-constrained programs, we reformulate the two variants as mixed-integer linear programs (MILPs). Via computations of diverse instances, we demonstrate the effectiveness of the SAA approach, analyse the solution feasibility and objective bounds, and conduct sensitivity analysis for the two MILPs. The approaches can be generalised to a wide variety of production planning problems, and the resulting MILPs can be efficiently computed by commercial solvers.     

Production planning in automotive powertrain plants: a case study

Based on a real case study from the automotive industry, this paper deals with production planning in powertrain plants. We present an overview of the production planning process and propose a mixed integer linear programme to determine the production quantities of each product over a planning horizon of several days. Then, using real data of an engine assembly line, we simulate the performance obtained through the proposed model within a rolling horizon planning process. We perform multiple tests in order to evaluate the impact of two parameters involved in this process: planning frequency and frozen horizon length. Furthermore, in order to illustrate the value of improving coordination between engine plants and their customers, we evaluate the impact of the quality of demand information (orders and forecasts). We analyse the simulation results and provide insights and recommendations in order to achieve a good trade-off between service level, inventory, and planning stability.     

A review of discrete-time optimization models for tactical production planning

This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.     

A case study on monitoring polynomial profiles in the automotive industry

In some statistical process control applications, the quality of a process or product can be characterized by a relationship between a response variable and one explanatory variable, which is referred to as profile. We give an example here of a profile that can be described using a polynomial model. This example comes from the automotive industry, where one of the most important quality characteristics of an automobile engine is the relationship between the torque produced by an engine and the engine speed in revolutions per minute. We find for this data set that a second‐order polynomial works well. In addition, we show that there is autocorrelation within each profile, thus an ordinary least‐square method that ignores the autocorrelation is inappropriate. We propose a linear mixed model method as an alternative approach. After the reduction of the data to a series of parameter estimates, we then conduct a step‐by‐step Phase I analysis of the polynomial profiles monitoring using a T²‐based procedure to check the stability of the process and whether or not there are outlying profiles. The remaining profiles are used to form the estimated mean vector and variance–covariance matrix to be used in Phase II studies. Finally, a brief discussion is presented to show how one can use these parameters in Phase II. Copyright © 2009 John Wiley & Sons, Ltd.     

Zonotopes and the LP-Newton method

Although linear programming problems can be solved in polynomial time by the ellipsoid method and interior-point algorithms, there still remains a long-standing open problem of devising a strongly polynomial algorithm for linear programming (or of disproving the existence of such an algorithm). The present work is motivated by an attempt toward solving this problem. Linear programming problems can be formulated in terms of a zonotope, a kind of greedy polyhedron, on which linear optimization is made easily. We propose a method, called the LP-Newton method, for linear programming that is based on the zonotope formulation and the minimum-norm-point algorithm of Philip Wolfe. The LP-Newton method is a finite algorithm even for real-number input data with exact arithmetic computations. We show some preliminary computational results to examine the behavior of the LP-Newton method. Major part of this paper was presented as a plenary talk with the same title at ICOTA7 (December 12–15, 2007, Kobe, Japan) by the first author. The fourth author’s research was carried out while visiting RIMS in August 2007.     

A membership function approach for aggregate production planning problems in fuzzy environments

This paper proposes a novel approach for the aggregate production planning (APP) problem with fuzzy parameters. Different from the results of previous studies, in this paper the membership function of the fuzzy minimal total cost is constructed based on Zadeh's extension principle and fuzzy solutions are provided. A pair of mathematical programs parameterised by possibility level α is formulated to calculate the lower and upper bounds of the fuzzy total cost at α. By enumerating different values of α, the membership function of the fuzzy total cost is constructed. To illustrate the validity of the proposed approach, the example studied by Lai and Hwang (1992 Lai, YJ and Hwang, CL. 1992. Fuzzy mathematical programming: methods and applications, Berlin: Springer.  [Google Scholar]) using Chanas's approach is investigated. Since the objective value is expressed by a membership function rather than by a crisp value, the proposed approach can represent APP systems more accurately, thus obtained solutions which contain more information can offer more chance to achieve the feasible disaggregate plan, and it is beneficial to the decision-maker in practical applications. The proposed approach can also be applied to APP problems with other characteristics.     

A new efficient algorithm for computing the imprecise reliability of monotone systems

Reliability analysis of complex systems by partial information about reliability of components and by different conditions of independence of components may be carried out by means of the imprecise probability theory which provides a unified framework (natural extension, lower and upper previsions) for computing the system reliability. However, the application of imprecise probabilities to reliability analysis meets with a complexity of optimization problems which have to be solved for obtaining the system reliability measures. Therefore, an efficient simplified algorithm to solve and decompose the optimization problems is proposed in the paper. This algorithm allows us to practically implement reliability analysis of monotone systems under partial and heterogeneous information about reliability of components and under conditions of the component independence or the lack of information about independence. A numerical example illustrates the algorithm.     

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.     

Predicting lifetime by degradation tests: A case study of ISO 10995

ISO 10995 is the international standard for the reliability testing and archival lifetime prediction of optical media. The standard specifies the testing conditions in terms of the combinations of stress variables—temperature and relative humidity. The periodically collected data from tests are the error rate of the device, and failure is defined as the error rate exceeding a predetermined level. The standard assumes that the projected failure time is the actual failure time, and these projected failure times are then analyzed by using an Eyring or Arrhenius model. Since true failure times are often not directly observed, the uncertainties in the failure time must be taken into account. In this paper, we present a hierarchical model for degradation that can directly infer failure time at the use condition and compare this model with the International Standard Organization (ISO) standard through a simulation study. Not accounting for the uncertainty in the projected failure times leads to unjustified confidence in the estimation for the median lifetime at both the stress conditions used in the experiments and at the use condition.     

A hybrid simulation approach for integrating safety behavior into construction planning: An earthmoving case study

One of the key challenges in improving construction safety and health is the management of safety behavior. From a system point of view, workers work unsafely due to system level issues such as poor safety culture, excessive production pressure, inadequate allocation of resources and time and lack of training. These systemic issues should be eradicated or minimized during planning. However, there is a lack of detailed planning tools to help managers assess the impact of their upstream decisions on worker safety behavior. Even though simulation had been used in construction planning, the review conducted in this study showed that construction safety management research had not been exploiting the potential of simulation techniques. Thus, a hybrid simulation framework is proposed to facilitate integration of safety management considerations into construction activity simulation. The hybrid framework consists of discrete event simulation (DES) as the core, but heterogeneous, interactive and intelligent (able to make decisions) agents replace traditional entities and resources. In addition, some of the cognitive processes and physiological aspects of agents are captured using system dynamics (SD) approach. The combination of DES, agent-based simulation (ABS) and SD allows a more “natural” representation of the complex dynamics in construction activities. The proposed hybrid framework was demonstrated using a hypothetical case study. In addition, due to the lack of application of factorial experiment approach in safety management simulation, the case study demonstrated sensitivity analysis and factorial experiment to guide future research.