Critical acceptance values and sample sizes of a variables sampling plan for very low fraction of defectives

Acceptance sampling plans are practical tools for quality control applications, which involve quality contracting on product orders between the vendor and the buyer. Those sampling plans provide the vendor and the buyer rules for lot sentencing while meeting their preset requirements on product quality. In this paper, we introduce a variables sampling plan for unilateral processes based on the one-sided process capability indices _CPU$C_{\mathrm{PU}}$ (or _CPL)$C_{\mathrm{PL}})$, to deal with lot sentencing problem with very low fraction of defectives. The proposed new sampling plan is developed based on the exact sampling distribution rather than approximation. Practitioners can use the proposed sampling plan to determine accurate number of product items to be inspected and the corresponding critical acceptance value, to make reliable decisions. We also tabulate the required sample size n$n$ and the corresponding critical acceptance value _C0$C_0$ for various α$\alpha$-risks, β$\beta$-risks, and the levels of lot or process fraction of defectives that correspond to acceptable and rejecting quality levels.     

Optimization models for the tool change scheduling problem

Traditional machine scheduling literature generally assumes that a machine is available at all times. Yet this assumption may not be accurate in real manufacturing systems. In many cases, a machine's tool must be changed after it has continuously worked for a period of time. This paper deals with a single machine scheduling problem subject to tool wear, given the allowed maximum continuous working time of the machine is _TL$T_{\mathrm{L}}$ (tool life) and the tool change time is _TC$T_{\mathrm{C}}$. Job processing and tool changes are scheduled simultaneously. In this paper, we examine this problem to minimize the total tardiness of jobs. Two mixed binary integer programming models are developed to optimally solve this problem. Computational experiments are performed to evaluate the models’ efficiency.     

Efficient composite heuristics for total flowtime minimization in permutation flow shops

In this paper, permutation flow shops with total flowtime minimization are considered. General flowtime computing (GFC) is presented to accelerate flowtime computation. A newly generated schedule is divided into an unchanged subsequence and a changed part. GFC computes total flowtime of a schedule by inheriting temporal parameters from its parent in the unchanged part and computes only those of the changed part. Iterative methods and LR (developed by Liu J, Reeves, CR. Constructive and composite heuristic solutions to theP∥Σ_Ci$P\parallel \Sigma C_i$ scheduling problem, European Journal of Operational Research 2001; 132:439–52) are evaluated and compared as solution improvement phase and index development phase. Three composite heuristics are proposed in this paper by integrating forward pair-wise exchange-restart (FPE-R) and FPE with an effective iterative method. Computational results show that the proposed three outperform the best existing three composite heuristics in effectiveness and two of them are much faster than the existing ones.     

A column generation heuristic for dynamic capacitated lot sizing with random demand under a fill rate constraint

This paper deals with the dynamic multi-item capacitated lot-sizing problem under random period demands (SCLSP). Unfilled demands are backordered and a fill rate constraint is in effect. It is assumed that, according to the static-uncertainty strategy of Bookbinder and Tan [1], all decisions concerning the time and the production quantities are made in advance for the entire planning horizon regardless of the realization of the demands. The problem is approximated with the set partitioning model and a heuristic solution procedure that combines column generation and the recently developed _ABCβ${\mathit{ABC}}_{\beta }$ heuristic is proposed.     

Improving routing and scheduling decisions at a distributor of industrial gasses

This paper investigates cyclical inventory replenishment for a company's regional distribution center that supplies, distributes, and manages inventory of carbon dioxide (_CO2)$({\mathrm{CO}}_2)$ at over 900 separate customer sites in Indiana. The company previously experienced high labor costs with excessive overtime and maintained a regular back-log of customers experiencing stockouts. To address these issues we implemented a three-phase heuristic for the cyclical inventory routing problem encountered at one of the company's distribution centers. This heuristic determines regular routes for each of three available delivery vehicles over a 12-day delivery horizon while improving four primary performance measures: delivery labor cost, stockouts, delivery regularity, and driver–customer familiarity. It does so by first determining three sets of cities (one for each delivery vehicle) that must be delivered to each day based on customer requirements. Second, the heuristic assigns the remaining customers in other cities to one of the three “backbone routes” determined in phase 1. And third, it balances customer deliveries on each daily route over the schedule horizon. Through our methodology, we were able to significantly reduce overtime, driving time, and labor costs while improving customer service.     

A simple linear time approximation algorithm for multi-processor job scheduling on four processors

Multiprocessor job scheduling problem has become increasingly interesting, for both theoretical study and practical applications. Theoretical study of the problem has made significant progress recently, which, however, seems not to imply practical algorithms for the problem, yet. Practical algorithms have been developed only for systems with three processors and the techniques seem difficult to extend to systems with more than three processors. This paper offers new observations and introduces new techniques for the multiprocessor job scheduling problem on systems with four processors. A very simple and practical linear time approximation algorithm of ratio bounded by 1.5 is developed for the multi-processor job scheduling problem P ₄|fix|C _max, which significantly improves previous results. Our techniques are also useful for multiprocessor job scheduling problems on systems with more than four processors.     

Probabilistic Modeling of Flood Hazard and its Risk Assessment for Eastern Region of India

This article models flood occurrence probabilistically and its risk assessment. It incorporates atmospheric parameters to forecast rainfall in an area. This measure of precipitation, together with river and ground parameters, serve as parameters in the model to predict runoff and subsequently inundation depth of an area. The inundation depth acts as a guide for predicting flood proneness and associated hazard. The vulnerability owing to flood has been analyzed as social vulnerability $(V_S)$, vulnerability to property $(V_P)$, and vulnerability to the location in terms of awareness $(V_A)$. The associated risk has been estimated for each area. The distribution of risk values can be used to classify every area into one of the six risk zones—namely, very low risk, low risk, moderately low risk, medium risk, high risk, and very high risk. The prioritization regarding preparedness, evacuation planning, or distribution of relief items should be guided by the range on the risk scale within which the area under study falls. The flood risk assessment model framework has been tested on a real‐life case study. The flood risk indices for each of the municipalities in the area under study have been calculated. The risk indices and hence the flood risk zone under which a municipality is expected to lie would alter every day. The appropriate authorities can then plan ahead in terms of preparedness to combat the impending flood situation in the most critical and vulnerable areas.     

Single-machine batch delivery scheduling with an assignable common due window

This paper addresses a batch delivery single-machine scheduling problem in which jobs have an assignable common due window. Each job will incur an early (tardy) penalty if it is early (tardy) with respect to the common due window under a given schedule. There is no capacity limit on each delivery batch, and the cost per batch delivery is fixed and independent of the number of jobs in the batch. The objective is to find the optimal size and location of the window, the optimal dispatch date for each job, as well as an optimal job sequence to minimize a cost function based on earliness, tardiness, holding time, window location, window size, and batch delivery. We show that the problem can be optimally solved in O(n⁸)$O(n^8)$ time by a dynamic programming algorithm under a reasonable assumption on the relationships among the cost parameters. A computational experiment is also conducted to evaluate the performance of the proposed algorithm. We also show that some special cases of the problem can be optimally solved by lower order algorithms.     

Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem

In this work three genetic algorithms are presented for the permutation flowshop scheduling problem with total tardiness minimisation criterion. The algorithms include advanced techniques like path relinking, local search and a procedure to control the diversity of the population. We also include a speed up procedure in order to reduce the computational effort needed for the local search technique, which results in large CPU time savings. A complete calibration of the different parameters and operators of the proposed algorithms by means of a design of experiments approach is also given. We carry out a comparative evaluation with the best methods that can be found in the literature for the total tardiness objective, and with adaptations of other state-of-the-art methods originally proposed for other objectives, mainly makespan. All the methods have been implemented with and without the speed up procedure in order to test its effect. The results show that the proposed algorithms are very effective, outperforming the remaining methods of the comparison by a considerable margin.     

The relative value of a management information system,a simulation study

A relative value of a management information system (MIS) is defined in this paper by a ratio $\text{u}{}_1\text{u}{}_0$, where u₀ is a value of a utility function of an enterprise whose management information system is perfect, and u₁ is its value when it is not perfect and may produce inaccurate or out-of-date data among correct information. Our simulation model contains beuristics which describe the operational and strategic information system of an enterprise. The environment of the enterprise may be stable or dynamic. A mathematical formula, based on simulations, is developed. This formula describes how the relative value of an MIS depends on such factors as the accuracy of an operational information system, delays in information flow, the quality of a strategic information system, a reinvestment ratio used in the enterprise, and a number of investment periods. This formula has been found suitable in an enterprise with a strategically stable environment, but not with a turbulent environment.     

An ACE in the hole: Twin family models for applied behavioral genetics research

Many studies investigate the genetic and environmental influences on traits using twin data and ACE models (A = additive genetic effects, C = shared environment effects, E = unshared environment effects). Unfortunately, relying on twins leads to biased results and limits what researchers can study. We introduce twin models and describe their problems. We show how to solve these problems with data from twins and their families, which allow modeling new effects such as the parent-to-child transmission of traits. We illustrate twin family models using extraversion (extroversion) data from the Virginia 30,000 twin family dataset, giving model specifications and code for the program Mplus. We conclude that if researchers are interested in understanding a broad sense of genetic influences on observed variables, traditional twin models are adequate. However, when data from twins and families are available, twin family models offer better and more interesting estimates of genetic and environmental effects.     

An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems

Dynamic multi-objective optimization algorithms are used as powerful methods for solving many problems worldwide. Diversity, convergence, and adaptation to environment changes are three of the most important factors that dynamic multi-objective optimization algorithms try to improve. These factors are functions of exploration, exploitation, selection and adaptation operators. Thus, effective operators should be employed to achieve a robust dynamic optimization algorithm. The algorithm presented in this study is known as spread-based dynamic multi-objective algorithm (SBDMOA) that uses bi-directional mutation and convex crossover operators to exploit and explore the search space. The selection operator of the proposed algorithm is inspired by the spread metric to maximize diversity. When the environment changed, the proposed algorithm removes the dominated solutions and mutated all the non-dominated solutions for adaptation to the new environment. Then the selection operator is used to select desirable solutions from the population of non-dominated and mutated solutions. Generational distance, spread, and hypervolume metrics are employed to evaluate the convergence and diversity of solutions. The overall performance of the proposed algorithm is evaluated and investigated on FDA, DMOP, JY, and the heating optimization problem, by comparing it with the DNSGAII, MOEA/D-SV, DBOEA, KPEA, D-MOPSO, KT-DMOEA, Tr-DMOEA and PBDMO algorithms. Empirical results demonstrate the superiority of the proposed algorithm in comparison to other state-of-the-art algorithms.

     

The complexity of VLSI power-delay optimization by?interconnect resizing

The lithography used for 32 nanometers and smaller VLSI process technologies restricts the interconnect widths and spaces to a very small set of admissible values. Until recently the sizes of interconnects were allowed to change continuously and the implied power-delay optimal tradeoff could be formulated as a convex programming problem, for which classical search algorithms are applicable. Once the admissible geometries become discrete, continuous search techniques are inappropriate and new combinatorial optimization solutions are in order. A first step towards such solutions is to study the complexity of the problem, which this paper is aiming at. Though dynamic programming has been shown lately to solve the problem, we show that it is NP-complete. Two typical VLSI design scenarios are considered. The first trades off power and sum of delays (L ₁), and is shown to be NP-complete by reduction of PARTITION. The second considers power and max delays (L _∞), and is shown to be NP-complete by reduction of SUBSET_SUM.     

An integrated multiobjective decision making process for supplier selection and order allocation

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) is proposed to consider both tangible and intangible factors in choosing the best suppliers and define the optimum quantities among selected suppliers to maximize the total value of purchasing and minimize the budget and defect rate. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). Also the priorities of suppliers will be used as the parameters of the first objective function. This multi-objective real-life problem was solved by using ε$\epsilon$-constraint method and a reservation level driven Tchebycheff procedure. Finally, the most preferred nondominated solutions were determined by considering decision maker's (DM) preferences and the results obtained by these techniques are compared.