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 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.     

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.     

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 novel framework for the carbon reduction performance of power grids:A case study of provincial power grids within the China Central Power Grid

Power grids play a crucial role in connecting electricity suppliers and consumers.They facilitate efficient power transmission and energy management,significantly contributing to the transition toward low-carbon practices across both upstream and downstream sectors.Effectively managing carbon reduction in the power industry is essential for enhancing carbon reduction efficiency and achieving dual-carbon goals.Recent studies have focused on the outcomes of carbon reduction efforts rather than the management process.However,when power grids prioritize the process of carbon reduction in their management,they are more likely to achieve better results.To address this gap,we propose an evaluation model for managing carbon reduction activities in power grids,comprising the carbon management efficiency(CME)module based on the matu-rity model and the carbon reduction efficiency(CRE)module based on the entropy method.The CME module provides a scorecard corresponding to a detailed and continuous evaluation model for carbon management processes to calculate its performance.Simultaneously,the CRE module relates carbon reduction results to the devel-opment direction of the government and power grid,allowing for effective adjustments and updates based on actual situations.The evaluation model was applied to provincial power grids within the China Central Power Grid.The results reveal that despite some fluctuations in carbon reduction performance,provincial power grids within the China Central Power Grid have made continuous progress in carbon reduction efforts.According to the synergy model,there is evidence suggesting that power grids are steadily improving their carbon reduction perfor-mance,and a more organized approach would lead to a greater degree of synergy.The evaluation model applies to power grids,and its framework can be extended to other industries,providing a theoretical reference for evaluating their carbon reduction efforts.     

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.     

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.     

A comparison of mixed integer programming formulations of the capacitated lot-sizing problem

We propose a novel mixed integer programming formulation for the capacitated lot-sizing problem with set-up times and set-up carryover. We compare our formulation to two earlier formulations, the Classical and Modified formulations, and a more recent formulation due to Suerie and Stadtler. Extensive computational experiments show that our formulation consistently outperforms the Classical and Modified formulations in terms of CPU time and solution quality. It is competitive with the Suerie–Stadtler (S&S) formulation, but outperforms all other formulations on the most challenging instances, those with low-capacity slack and a dense jobs matrix. We show that some of the differences in the performance of these various formulations arise from their different use of binary variables to represent production or set-up states. We also show that the LP relaxation of our Novel formulation provides a tighter lower bound than that of the Modified formulation. Our experiments demonstrate that, while the S&S formulation provides a much tighter LP bound, the Novel formulation is better able to exploit the intelligence of the CPLEX solution engine.     

Hierarchical scheduling of continuous casters and hot strip mills in the steel industry: a block planning application

This work addresses the joint scheduling of continuous caster and hot strip mill processes in the steel industry. Traditionally, slab yards are used to decouple these two stages. However, the rising importance of energy costs and reduced logistic effort gives motivation for a combined scheduling. For each of the processes, a mixed-integer linear optimisation model based on the block planning principle is presented. This approach develops production schedules that take technological sequences of steel grades and milling programmes into account. We consider the integrated steel plant of an international steel company as a case study. Numerical results demonstrate the practicability of this approach under experimental conditions, which reflect typical settings from an industrial application in the steel industry.     

Vehicle routing concepts in the closed-loop container network of ARN—a case study

In this paper we discuss a real-life case study to optimize the logistics network for the collection of containers from end-of-life vehicle dismantlers in the Netherlands. Advanced planning concepts, such as dynamic assignment of dismantlers to logistic service providers, are analyzed using a simulation model. Based on this model, we periodically solve a vehicle routing problem to gain insight into the long-term performance of the system. The vehicle routing problem considered is a multi-depot pickup and delivery problem with alternative delivery locations. A special characteristic of the problem is the limited vehicle capacity of two containers. We solve this problem with a heuristic based on route generation and set partitioning.     

Neutrality of the elliptic inhomogeneity in the case of non-uniform loading

In this paper, we consider the problem of a single elliptic elastic inhomogeneity embedded within an infinite elastic matrix in antiplane shear. In particular, we examine the (stress) neutrality of this inhomogeneity when a non-uniform stress field is prescribed in the surrounding matrix. Since it is known that neutral elastic inhomogeneities do not exist when the inhomogeneity is assumed to be perfectly bonded to the matrix, the method presented here is based on the assumption of imperfect interface and the appropriate choice of the (single) interface parameter (characterizing the imperfect interface) to achieve the desired neutrality. Specifically, neutrality is established for specific (polynomial) classes of prescribed states of stress in the surrounding matrix. The results in this paper affirm the feasibility of designing a neutral elastic inhomogeneity by controlling the (imperfect) interface parameter describing the inhomogeneity-matrix interface.     

Value chain management for commodities: a case study from the chemical industry

We present a planning model for chemical commodities related to an industry case. Commodities are standard chemicals characterized by sales and supply volatility in volume and value. Increasing and volatile prices of crude oil-dependent raw materials require coordination of sales and supply decisions by volume and value throughout the value chain to ensure profitability. Contract and spot demand differentiation with volatile and uncertain spot prices, spot sales quantity flexibility, spot sales price–quantity functions and variable raw material consumption rates in production are problem specifics to be considered. Existing chemical industry planning models are limited to production and distribution decisions to minimize costs or makespan. Demand-oriented models focus on uncertainty in demand quantities not in prices. We develop an integrated model to optimize profit by coordinating sales quantity, price and supply decisions throughout the value chain. A two-phase optimization approach supports robust planning ensuring minimum profitability even in case of worst-case spot sales price scenarios. Model evaluations with industry case data demonstrate the impact of elasticities, variable raw material consumption rates and price uncertainties on planned profit and volumes.