A note on pre-emptive scheduling with earliness and tardiness costs

The option of pre-emption seems to have escaped the attention of existing literature on scheduling problems with earliness and tardiness costs. This note presents an attempt to accommodate pre-emption in such settings. The focus is on models with a common due date for all jobs under different cost structures ( job dependent versus job independent), and different objectives ( minsum and minmax). It appears that only one of these cases is not polynomially solvable: the case of minsum objective and job-dependent ( linear) cost. An exact dynamic programming algorithm is introduced for this problem, as well as an efficient heuristic, which is shown to perform extremely well in our numerical tests.     

Milton keynes: A model for regenerating our cities?

Milton Keynes is one of the most succesful of Britain's new towns. With new towns such as Peterborough and Telford, this can be seen as the latest stage of the New Town Movement which started at the end of World War II. Of these new towns, Milton Keynes is the largest and the most ambitious in its objectives. The problems facing the United Kingdom today are very much greater than when Milton Keynes was first started but, because of a flexible planning approach, the Development Corporation has responded to these changes and has established policies to assist in solving these problems. The key to this success is a more flexible and effective manufacturing base. Today we face unprecedented changes, unique in our history. Although manufacturing and services will provide the wealth to solve these problems, we will only succeed through co-operation between the private sector and local and national government. A study of Milton Keynes, its growth, objectives, successes and failures could provide a useful model for our future planning.     

中国失业问题与财政政策研究

失业是市场经济运行中各国以及我国社会经济发展面临的一大难题。促进就业、控制失业率是各国政府的宏观调控目标之一和不可推卸的责任。本报告从理论与实践的结合上,深入研究失业与宏观调控及财政政策的关系,揭示当前我国政府及财政促进就业政策的成效和问题,并在借鉴国际经验的基础上,进一步提出了近期我国缓解失业压力、建立促进就业长效机制的宏观对策思路和财政政策取向。     

Fuzzy vs. Minmax Weighted Multiobjective Linear Programming Illustrative Comparisons

This paper compares two approaches to the solution of weighted multiobjective linear programming problems: the fuzzy linear programming method and the minmax distance metric. The two models produce an identical solution for equally weighted objectives, but the solutions differ when the objectives are unequally weighted. This is due to the underlying meaning of the weights attached to each solution method. The paper illustrates the graphical meaning of the weights and the implications to the decision maker.     

A METHOD FOR SOLVING LINEAR PROGRAMS WITH VARIABLE RESOURCES

This paper is concerned with the solution of linear and linear goal programming problems in which the values of the right-hand side parameters are not fixed constants. Specifically, we are concerned with linear optimization problems in which the right-hand sides of the constraining equations are free to vary subject to a set of linear constraining equations. By formulating a relaxed linear program wherein the right-hand sides are treated as variables, we show how it is possible to solve one larger linear program that yields as a solution not only the optimal values for the decision variables, but also the optimal values for the right-hand sides.     

Behavioral Modeling of Adversaries with Multiple Objectives in Counterterrorism

Attacker/defender models have primarily assumed that each decisionmaker optimizes the cost of the damage inflicted and its economic repercussions from their own perspective. Two streams of recent research have sought to extend such models. One stream suggests that it is more realistic to consider attackers with multiple objectives, but this research has not included the adaption of the terrorist with multiple objectives to defender actions. The other stream builds off experimental studies that show that decisionmakers deviate from optimal rational behavior. In this article, we extend attacker/defender models to incorporate multiple objectives that a terrorist might consider in planning an attack. This includes the tradeoffs that a terrorist might consider and their adaption to defender actions. However, we must also consider experimental evidence of deviations from the rationality assumed in the commonly used expected utility model in determining such adaption. Thus, we model the attacker's behavior using multiattribute prospect theory to account for the attacker's multiple objectives and deviations from rationality. We evaluate our approach by considering an attacker with multiple objectives who wishes to smuggle radioactive material into the United States and a defender who has the option to implement a screening process to hinder the attacker. We discuss the problems with implementing such an approach, but argue that research in this area must continue to avoid misrepresenting terrorist behavior in determining optimal defensive actions.     

Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns

The present work aims to support tactical and operational planning decisions of reverse logistics systems while considering economic, environmental and social objectives. In the literature, when addressing such systems economic aspects have been often used, while environmental concerns have emerged only recently. The social component is the one less studied and rarely the combination of the three concerns has been analyzed. This work considers the three objectives and was motivated by the challenge of supporting decision makers when managing a real case study of a recyclable waste collection system, where strategic decisions on the number and location of depots, vehicles and containers were taken beforehand. Tactical and operational decisions are studied involving the establishment of service areas for each depot and the definition and scheduling of collection routes for each vehicle. Such decisions should represent a compromise solution between the three objectives translating a sustainable reverse logistics plan. The problem is modeled as a multi-objective, multi-depot periodic vehicle routing problem with inter-depot routes. A mathematical formulation and a solution approach are proposed. An approximation to the Pareto front is obtained for the case study and the trade-offs between the objectives are discussed. A balanced solution is proposed.     

Corporate planning and project evaluation in urban transport

The paper describes the development of corporate objectives by Greater Manchester Passenger Transport Executive, the largest urban transport operator in the U.K. outside London. Four corporate objectives, each covering a different dimension of performance, have been adopted. The objectives pose problems for project evaluation because a project can contribute positively to one or more, and negatively to another or others. A technique for overcoming these problems, using the ‘trade-off’ concept, is being developed, and the practical application of this technique to two major investments is discussed in detail.     

Quantitative Comparison of Approximate Solution Sets for Bi‐criteria Optimization Problems*

We present the Integrated Preference Functional (IPF) for comparing the quality of proposed sets of near‐pareto‐optimal solutions to bi‐criteria optimization problems. Evaluating the quality of such solution sets is one of the key issues in developing and comparing heuristics for multiple objective combinatorial optimization problems. The IPF is a set functional that, given a weight density function provided by a decision maker and a discrete set of solutions for a particular problem, assigns a numerical value to that solution set. This value can be used to compare the quality of different sets of solutions, and therefore provides a robust, quantitative approach for comparing different heuristic, a posteriori solution procedures for difficult multiple objective optimization problems. We provide specific examples of decision maker preference functions and illustrate the calculation of the resulting IPF for specific solution sets and a simple family of combined objectives.     

Duality of the improved algebraic method (DIAM)

In this note we present a variant of the improved algebraic method (IAM) using a duality analysis to solve linear programming (LP) problems where more insights to the method are presented. When the coordinates of all vertices are computed, any feasible point can be expressed as a linear combination of the vertices. The objective function is expressed as a weighted sum of its evaluation at the feasible vertices and the optimal point is associated with the highest/lowest coefficient of the weighted sum.     

Non‐dominated Time‐Window Policies in City Distribution

Urban freight contributes significantly to pollution, noise disturbance, traffic congestion, and safety problems in city centers. In many cities, local governments have introduced policy measures, in particular time‐access restrictions, to alleviate these problems. However, setting time windows is very challenging due to the conflicting interests and objectives of the stakeholders involved. In this article, we examine whether it is possible to develop time‐window policies that enhance environmental sustainability and distribution efficiencies, while meeting the objectives of the municipalities. We develop a framework for balancing retailer (costs), municipality (satisfaction), and environmental (emissions) objectives, using data envelopment analysis, under different urban time‐window policies. The approach is illustrated by a case study of three Dutch retail organizations, with a large number of stores affected by such time windows. On the basis of an evaluation of 99 different time‐window policies, our results show that harmonizing time windows between neighboring cities leads to the best overall performance. The currently used time‐window policy appears to perform reasonably well, but can be improved on all dimensions . However, harmonizing time‐window policies may be difficult to realize in practice.     

Understanding and Planning for Freight Movement in Cities: Practices and Challenges

ABSTRACT

Planning for freight presents a perpetual challenge for governments. Understanding freight flows has attracted increasing research attention, though such knowledge can be difficult to translate to address problems in planning practice. This paper critically assesses the challenges of understanding and planning for urban freight movement, especially with reference to achieving more sustainable outcomes. In doing so, we elucidate guidance for broader analytical and policy approaches suited to meeting diverse public interest objectives. We review the literature with specific reference to the nature of policy problems, while collating existing conceptual knowledge for broad urban land use types.     

Multi-objective scheduling of two-job families on a single machine

This paper presents a preliminary analysis of the typical scheduling environment in semiconductor manufacturing involving multiple job families, and where more than one objective such as cycle time, machine utilization and the due-date accuracy needs to be simultaneously considered. In this study, the NP-hard problem of scheduling N independent jobs on a single testing machine with due dates and sequence-dependent setup times is addressed, where the multiple objectives are to minimize average cycle time, to minimize average tardiness, and to maximize machine utilization. A Pareto optimal solution, which is not inferior to any other feasible solutions in terms of all objectives, is generated combining the analytically optimal and conjunctive simulated scheduling approach. First, the machine-scheduling problem is modeled using the discrete event simulation approach and the problem is divided into simulation clock based lot selection sub-problems. Then, a Pareto optimal lot is selected using the compromise programming technique for multiobjective optimization at each decision instant in simulated time. With the help of a broad experimental design, this developed solution is then compared with common heuristic-dispatching rules such as SPT and EDD, which show better results for all the objectives over a wide range of problems. The developed scheduling method shows approximately 16.7% reduction in average cycle time, 25.6% reduction in average tardiness, and 21.6% improvement in machine utilization over the common dispatching rules, SPT and EDD.     

Bridging the Gaps between Impact Assessments and Resettlement Planning: A Case Study of Padma Multipurpose Bridge Project,Bangladesh

The traditional methodologies including spatial decision support systems have at times showed their ineffectiveness in terms of gathering, analyzing and incorporating diversified views of affected persons into resettlement planning. This ineffectiveness is in dealing with unstructured problems and uncertainty in analyzing alternative strategies. This paper therefore proposes an integrated methodology (combination of argumentative mapping and qualitative probabilistic networks) to counter these limitations. The new methodology was introduced in a large-scale infrastructure project to verify its applicability. Results derived from the study not only provide better insights into resettlement-related problems, but also indicate critical management options for desired objectives.     

Aggregation and Market Demand: An Exterior Differential Calculus Viewpoint

We analyze under which conditions a given vector field can be disaggregated as a linear combination of gradients. This problem is typical of aggregation theory, as illustrated by the literature on the characterization of aggregate market demand and excess demand. We argue that exterior differential calculus provides very useful tools to address these problems. In particular, we show, using these techniques, that any analytic mapping in Rⁿ satisfying Walras Law can be locally decomposed as the sum of n individual, utility-maximizing market demand functions. In addition, we show that the result holds for arbitrary (price-dependent) income distributions, and that the decomposition can be chosen such that it varies continuously with the mapping. Finally, when income distribution can be freely chosen, then decomposition requires only n/2 agents.     

Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives

Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linear programming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.     

Cost-benefit analysis—a critique

This paper surveys some of the main criticisms of cost-benefit analysis (CBA) that have emerged over the years. It is noted in passing that views on what type of technique CBA really is, or should be, have differed and continue to differ depending on the set of value judgements used. We emphasise that the more recent suggested extensions to or modifications of CBA have sought to make the technique more comprehensive (i.e. to include distributional, environmental quality and other objectives as well as economic efficiency) at the inevitable cost of a loss of precision. The underlying principles of CBA are examined and the problems of multiple objective planning highlighted. Valuation problems are analysed, in particular with regard to amenity and environmental effects. Finally, the issues of uncertainty, irreversibility and intergenerational equity are raised briefly to indicate the complexity of the decision-making task when large-scale technologically advanced projects have to be appraised. In conclusion it is argued that CBA presented in a disaggregated format, as a comprehensive method for the ordering of information and a testing procedure for a range of valuations can perform a useful role in the decision-making process. The term ‘policy analysis’ is probably a better one for this broader view of the use of CBA. Ultimately we seem to be searching for a synthesis of the participatory and the traditional (technocratic) style of decision-making though we have a long and difficult course to chart before we even approach such a goal.     

Solving the Multidimensional Assignment Problem by a Cross-Entropy method

The Multidimensional Assignment Problem (MAP) is a higher dimensional version of the linear assignment problem, where we find tuples of elements from given sets, such that the total cost of the tuples is minimal. The MAP has many recognized applications such as data association, target tracking, and resource planning. While the linear assignment problem is solvable in polynomial time, the MAP is NP-hard. In this work, we develop a new approach based on the Cross-Entropy (CE) methods for solving the MAP. Exploiting the special structure of the MAP, we propose an appropriate family of discrete distributions on the feasible set of the MAP that allow us to design an efficient and scalable CE algorithm. The efficiency and scalability of our method are proved via several tests on large-scale problems with up to 5 dimensions and 20 elements in each dimension, which is equivalent to a 0–1 linear program with 3.2 millions binary variables and 100 constraints.     

Implementation of new technology: towards greater manufacturing flexibility

Changes in market characteristics over the past several years are forcing firms to eliminate/minimize dependency on costly buffers of capacity, lead time, or inventory. One way to accomplish this is via appropriate adaptation and enhancement of existing technological infrastructure. Recent technological advances present firms with a wide array of alternative technologies. In this paper, we discuss the impact of new technology on the manufacturing environment and the problems associated with its implementation. We present an integrated framework for implementing such technology based on a consistent hierarchy of organizational objectives and a general foundation of constraint theory.     

Conditional Linear Combination Tests for Weakly Identified Models

We introduce the class of conditional linear combination tests, which reject null hypotheses concerning model parameters when a data‐dependent convex combination of two identification‐robust statistics is large. These tests control size under weak identification and have a number of optimality properties in a conditional problem. We show that the conditional likelihood ratio test of Moreira, 2003 is a conditional linear combination test in models with one endogenous regressor, and that the class of conditional linear combination tests is equivalent to a class of quasi‐conditional likelihood ratio tests. We suggest using minimax regret conditional linear combination tests and propose a computationally tractable class of tests that plug in an estimator for a nuisance parameter. These plug‐in tests perform well in simulation and have optimal power in many strongly identified models, thus allowing powerful identification‐robust inference in a wide range of linear and nonlinear models without sacrificing efficiency if identification is strong.