Economic assessment of use of pond ash in pavements

The paper introduces a new type of industrial waste-based subbase material which can replace conventional subbase material (CSM) in pavement construction. Utilisation of this industrial waste, namely pond coal ash produced from a thermal power plant in road construction will help to reduce the disposal problem of this waste and also will help to reduce the problem of scarcity of CSM. Lime and fibre were also added to the pond ash at various percentages to improve the suitability of this type of mix as subbase material. The optimum service life of pavement is studied with the help of numerical modelling and the cost benefit is also presented in the current study. The study reveals that stabilisation of the coal ash with 2% lime may produce an optimal material and, even though a greater thickness may be required to deliver the same pavement performance, direct cost savings of around 10% may be achieved in addition to less easily quantifiable environmental benefits. Design charts are provided to exploit the findings.     

SINGLE-MACHINE SCHEDULING WITH CONTROLLABLE PROCESSING TIMES AND EARLINESS,TARDINESS AND COMPLETION TIME PENALTIES

The paper considers the problem of scheduling nindependent and simultaneously available jobs on a single machine, where the job processing times are compressible as a linear cost function. The objective is to find an optimal permutation of the jobs, an optimal due date and the optimal processing times which jointly minimize a cost function consisting of the earliness, tardiness, completion time and compressing costs. It shows that the problem can be solved as an assignment problem.     

Economic Design of CSP-1 Plan Under the Dependent Production Process and Linear Inspection Cost

Chen and Chou presented the economic design of type I continuous sampling plan (CSP-1 plan) under the linear inspection cost. However, it can be argued that the production process is seldom independent. In this paper, we further propose the problem concerning the economic design of CSP-1 plan under the dependent production process and linear inspection cost. A solution procedure is developed to find the unique combination (i*, f*) that will meet the average outgoing quality limit (AOQL) requirement while also minimizing the total expected cost per unit produced during one inspection cycle.     

A genetic algorithm based heuristic for adjacent paper-reel layout problem

In this paper, we present an algorithm that solves a paper reel layout problem where the available space is divided into equal-size cells. The problem is to find a layout with the minimum transportation cost subject to adjacency and other constraints. A genetic algorithm is used in a two-stage iterative approach to solve the problem. Computational results seem to indicate the efficiency and effectiveness of the proposed solution method.     

Designing airport checked-baggage-screening strategies considering system capability and reliability

Emerging image-based technologies are critical components of airport security for screening checked baggage. Since these new technologies differ widely in cost and accuracy, a comprehensive mathematical framework should be developed for selecting technology or combination of technologies for efficient 100% baggage screening. This paper addresses the problem of setting threshold values of these screening technologies and determining the optimal combination of technologies in a two-level screening system by considering system capability and human reliability. Probability and optimization techniques are used to quantify and evaluate the cost- and risk-effectiveness of various deployment configurations, which is captured by using a system life-cycle cost model that incorporates the deployment cost, operating cost, and costs associated with system decisions. Two system decision rules are studied for a two-level screening system. For each decision rule, two different optimization approaches are formulated and investigated from practitioner's perspective. Numerical examples for different decision rules, optimization approaches and system arrangements are demonstrated.     

Bi-objective scheduling on uniform parallel machines considering electricity cost

This article investigates a bi-objective scheduling problem on uniform parallel machines considering electricity cost under time-dependent or time-of-use electricity tariffs, where electricity price changes with the hours within a day. The aim is to minimize simultaneously the total electricity cost and the number of machines actually used. A bi-objective mixed-integer linear programming model is first formulated for the problem. An insertion algorithm is then proposed for the single-objective scheduling problem of minimizing the total electricity cost for a given number of machines. To obtain the whole Pareto front of the problem, an iterative search framework is developed based on the proposed insertion algorithm. Computational results on real-life and randomly generated instances demonstrate that the proposed approach is quite efficient and can find high-quality Pareto fronts for large-size problems with up to 5000 jobs.     

Functional lock-in and the problem of design transformation

The act of introducing an innovation into an existing product by substituting or inserting new technologies is thought to be challenging due to the problem of integrating new components and sub-system architectures into existing ones. This article aims to challenge the foundation of this problem and develop new insights into the choice of functional architecture. The article will propose that the choice of functional architecture to achieve an intended purpose locks-in a design by influencing the cost of transformation. This paper studies functional lock-in based on the transformation cost of the functional architectures of products. The transformation cost for a set of biological and biologically inspired products is compared to that of engineered products. The results show that the biological and biologically inspired products have a statistically significant lower transformation cost than the engineered products. The results indicate that the structure of functions and flows in a product will constrain its transformation. More broadly, the paper proposes minimum transformation cost as an essential property of an optimal design.     

带弧费用约束的最短路径问题

对一类带弧费用约束的最短路径问题进行了研究,即对于网络中两个给定的顶点s,t,找出s和t之间的一条路,使得在满足总费用不超过一个给定正整数的s和t之间所有的路中,该条路的长度最短.通过将背包问题多项式时间变换为该问题的判定问题,证明了该问题是NP-完全的.并给出了求解此问题的一个动态规划算法.最后,我们得到了最优值的一个下界估计.     

Modeling multiple plant sourcing decisions

With the recent trend of company acquisitions and mergers, it is likely that the company that results from a merger will own multiple facilities with overlapping manufacturing capabilities. By shifting the production of parts to different plants, capacity can be better utilized, and the overall cost to the company can be reduced. This paper addresses the sourcing decisions of allocating parts to several internal facilities or to an external supplier. This multiple-plant sourcing problem (MPSP) is modeled as a multiple-choice multidimensional knapsack problem that considers the capacity of each facility and the costs associated with supplying a part at each facility and externally. We present results from an analysis of two-, three-, and four-facility MPSP models and find that the two-facility model is the most difficult to solve. Additionally, we model extensions that incorporate allocating a part to more than one facility with various inventory strategies, and consider how these extensions affect the sourcing decisions.     

Scheduling about an unrestricted common due windowwith arbitrary earliness/tardiness penalty rates

We consider the NP-hard problem of scheduling jobs on a single machine about an unrestricted due window to minimize total weighted earliness and tardiness cost. Each job has an earliness penalty rate and a tardiness penalty rate that are allowed to be arbitrary. Earliness or tardiness cost is assessed when a job completes outside the due window, which may be an instant in time or a time increment defining acceptable job completion. In this paper we present properties that characterize the structure of an optimal schedule, present a lower bound, propose a two-step branch and bound algorithm, and report results from a computational experiment. We find that optimal solutions can be quickly obtained for medium-sized problem instances.     

Single-machine scheduling for minimizing total cost with identical,asymmetrical earliness and tardiness penalties

Costs of flowtime, earliness and tardiness should be incorporated in real production scheduling. This paper constructs a single-machine scheduling model with a common due date to minimize the total cost including an identical, asymmetric earliness-tardiness cost. Several dominance conditions necessary for an optimal schedule are derived. A branch and bound algorithm exploiting the conditions is proposed to find an optimal schedule for an unconstrained version of the scheduling problem. Numerical experiments are demonstrated to show the effectiveness of the proposed method.     

A solution procedure for Economic Lot Scheduling Problems even in high utilisation facilities

Economic lot scheduling problem (ELSP) handles the problem of deciding what order quantities to use when different products/items are produced in the same capacity constrained production facility. It has previously been shown, and it is shown in this article, that it is possible to find a feasible solution fulfilling true feasibility conditions. However, if the utilisation of the production facility is high the production often has to start before the inventory reaches zero to avoid future shortages. Such ‘early starts’ creates an extra inventory holding cost that the traditional approximation for the inventory holding cost does not account for. This article presents an iterative solution procedure that computes the true inventory holding cost and minimises the total costs. Contrary to previous solution procedures, this procedure requires that the production is scheduled in detail. The heuristic solution procedure is illustrated by a numerical example, it is programmed in MATLAB and variants of the problem are presented.     

Equivalence of cost generators for minimum cost flow phase unwrapping.

Phase unwrapping represents a crucial step in processing phase data obtained with techniques such as synthetic aperture radar interferometry, speckle interferometry, and magnetic resonance imaging. The so-called branch-cut approaches form an important class of phase unwrapping algorithms. In 1996, Costantini proposed to transform the problem of correctly placing branch cuts into a minimum cost flow problem [Proceedings of the Fringe '96 Workshop (European Space Agency, Munich, 1996), pp. 261-272]. The critical point of this new approach is to generate cost functions that have to represent all the a priori knowledge necessary for phase unwrapping. Any function transforming a priori knowledge into a cost function is called a cost generator. Several types of algorithms ranging from heuristic approaches to generators based on probability-theory interpretations were suggested. A problem arising from the growing diversity of algorithms is to find a criterion for the equivalence of different cost generators. Two cost generators are equivalent if they produce cost functions with the same minimal flow for every residue configuration on every image with all possible a priori knowledge. Comparing the results of different cost generators on test scenes can show only their non-equivalence. We solve this problem by proving the following mathematical classification theorem: Two cost generators are equivalent if and only if one can be transformed into the other by multiplication by a fixed constant.     

Economic design of water distribution systems in buildings

This article discusses an engineering optimization problem which arises in hydraulics and is related to the use of a new criterion for sizing water distribution piping in large buildings. The optimization model aims to find the most suitable interior pipe diameters for the various pipes in the system, using commercial sizes and minimizing the overall installation cost according to some boundary conditions. The problem is formulated as a non-convex nonlinear program and a branch-and-bound algorithm is introduced for its solution. A procedure is proposed to obtain a feasible solution with standard values from the optimal solution of the non-convex program. The performance of the algorithm is analysed for a real-life problem and the cost of the computed solution is assessed, showing the appropriateness of the model and the optimization techniques.     

Multi-objective genetic algorithm for solving N-version program design problem

N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently.We formulate the optimal design problem of NVP as a bi-objective 0–1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process.The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.     

On the effects of the degree of discretion in reporting managerial performance

We consider a principal-agent setting in which a manager??s compensation depends on a noisy performance signal, and the manager is granted the right to choose an (accounting) method to determine the value of the performance signal. We study the effect of the degree of such reporting discretion, measured by the number of acceptable methods, on the optimal contract, the expected cost of compensation, and the manager??s expected utility. We find that a minimal degree of discretion may be necessary for successful contracting. We also find that while an increase in reporting discretion never harms the manager, the effect on the expected cost of compensation is more subtle. We identify three main effects of increased reporting discretion and characterize the conditions under which the aggregate of these three effects will lead to a higher or lower cost of compensation. Finally, we find that when reporting discretion induces costly effort on the part of the manager, the optimal degree of discretion can be higher than when it is costless.     

Infinite horizon production planning with periodic demand: solvable cases and a general numerical approach

In this paper, we consider a single part-type pull manufacturing system, which controls its production rates in response to periodic demand. When tracking the demand results in a product surplus, an inventory storage cost is incurred. Likewise, if an overall shortage occurs then a backlog cost is paid. In addition, production costs accrue when the system is not idle. Given an infinite planning horizon, the objective is to determine the cyclic production rates in order to minimize the total cost. With the aid of the maximum principle, extremal behavior of the system is studied and the continuous-time production planning problem is reduced to a discrete problem with a limited number of switching points at which time the production rates change. Using this result, an efficient numerical algorithm is proposed, which will yield an approximation to the optimal solution within any desired level of accuracy. In addition, we determine the analytical solution to the problem for three special cases: (i) the system capacity is not limited and the inventory storage cost factor is equal to the backlog cost factor; (ii) the production cost is negligible; and (iii) the surplus and shortages costs are negligible.     

Scheduling on a two-machine permutation flow shop under time-of-use electricity tariffs

We consider a two-machine permutation flow shop scheduling problem to minimise the total electricity cost of processing jobs under time-of-use electricity tariffs. We formulate the problem as a mixed integer linear programming, then we design two heuristic algorithms based on Johnson’s rule and dynamic programming method, respectively. In particular, we show how to find an optimal schedule using dynamic programming when the processing sequence is fixed. In addition, we propose an iterated local search algorithm to solve the problem with problem-tailored procedures and move operators, and test the computational performance of these methods on randomly generated instances.     

A robust cell formation approach for varying product demands

In cellular manufacturing environments, manufacturing cells are generally formed based on deterministic product demands. In this paper, we consider a system configuration problem with product demands expressed in a number of probabilistic scenarios. An optimization model integrating cell formation and part allocation is developed to generate a robust system configuration to minimize machine cost and expected inter-cell material handling cost. A two-stage Tabu search based heuristic algorithm is developed to find the optimal or near optimal solutions to the NP-hard problem. Numerical examples show that this model leads to an appropriate compromise between system configuration costs and expected material handling costs to meet the varying product demands. These example problems also show that the proposed algorithm is effective and computationally efficient for small or medium size problems.     

Assembly tolerance allocation using a coalitional game method

Assembly tolerance allocation in modern manufacturing industries is important because it directly affects product quality and manufacturing cost. Loose tolerances may cause quality deficiency while tight tolerances can increase the cost. It is significant to develop a reasonable tolerance allocation strategy for every assembly component combining the cost and quality demands. Traditionally, designers often adopt the single objective optimization with some kind of constraint or establish a comprehensive evaluation function combining several optimization objectives with different weights to solve the tolerance allocation problem. These approaches may not be desirable as it is difficult to adequately consider the interaction and conflict between the cost and quality demands. In this article, an assembly tolerance allocation method using coalitional game theory is proposed in an attempt to find a trade-off between the assembly cost and the assembly quality. First, the assembly tolerance allocation problem is formulated as a multi-objective optimization problem and the concept of the Pareto-optimal solution is introduced. Then, how the assembly tolerance allocation model is transformed into a coalitional game model is discussed, and a key technique of transforming the tolerance design variables into the game strategies is presented. Further, the Shapley value method of coalitional game based on each player's contribution evaluation to the profit of the whole coalition is given. Finally, the feasibility of the procedure is demonstrated through an example of vehicle front structure assembly.