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.     

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.     

Two new robust genetic algorithms for the flowshop scheduling problem

The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (_Cmax$C_{\max }$). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.     

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.     

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.     

Two-stage time minimizing assignment problem

This paper deals with the optimal selection of m out of n facilities to first perform m   given primary jobs in Stage-I followed by the remaining (n-m)$(n-m)$ facilities performing optimally the (n-m)$(n-m)$ secondary jobs in Stage-II. It is assumed that in both the stages facilities perform in parallel. The aim of the proposed study is to find that set of m   facilities performing the primary jobs in Stage-I for which the sum of the overall completion times of jobs in Stage-I and the corresponding optimal completion time of the secondary jobs in Stage-II by the remaining (n-m)$(n-m)$ facilities is the minimum. The developed solution methodology involves solving the standard time minimizing and cost minimizing assignment problems alternately after forbidding some facility-job pairings and suggests a polynomially bound algorithm. This proposed algorithm has been implemented and tested on a variety of test problems and its performance is found to be quite satisfactory.     

A Robust Approach for Mitigating Risks in Cyber Supply Chains

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify “good” solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst‐case risks, including models that maximize the worst‐case coverage, minimize the worst‐case regret, and maximize the average coverage in the $(1?\alpha )$ worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected‐value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.     

Effective heuristics for the blocking flowshop scheduling problem with makespan minimization

The blocking flowshop scheduling problem with makespan criterion has important applications in a variety of industrial systems. Heuristics that explore specific characteristics of the problem are essential for many practical systems to find good solutions with limited computational effort. This paper first presents two simple constructive heuristics, namely weighted profile fitting (wPF) and PW, based on the profile fitting (PF) approach of McCormick et al. [Sequencing in an assembly line with blocking to minimize cycle time. Operations Research 1989;37:925-36] and the characteristics of the problem. Then, three improved constructive heuristics, called PF-NEH, wPF-NEH, and PW-NEH, are proposed by combining the PF, wPF, and PW with the enumeration procedure of the Nawaz-Enscore-Ham (NEH) heuristic [A heuristic algorithm for the m-machine, n-job flow shop sequencing problem. OMEGA-International Journal of Management Science 1983;11:91-5] in an effective way. Thirdly, three composite heuristics i.e., PF-NEH_LS, wPF-NEH_LS, and PW-NEH_LS, are developed by using the insertion-based local search method to improve the solutions generated by the constructive heuristics. Computational simulations and comparisons are carried out based on the well-known flowshop benchmarks of Taillard [Benchmarks for basic scheduling problems. European Journal of Operation Research 1993;64:278-85] that are considered as blocking flowshop instances. The results show that the presented constructive heuristics perform significantly better than the existing ones, and the proposed composite heuristics further improve the presented constructive heuristics by a considerable margin. In addition, 17 new best-known solutions for Taillard benchmarks with large scale are found by the presented heuristics.     

Precision parameter in the variable precision rough sets model: an application

Despite their diverse applications in many domains, the variable precision rough sets (VPRS) model lacks a feasible method to determine a precision parameter (β)$(\beta )$ value to control the choice of β$\beta$-reducts. In this study we propose an effective method to find the β$\beta$-reducts. First, we calculate a precision parameter value to find the subsets of information system that are based on the least upper bound of the data misclassification error. Next, we measure the quality of classification and remove redundant attributes from each subset. We use a simple example to explain this method and even a real-world example is analyzed. Comparing the implementation results from the proposed method with the neural network approach, our proposed method demonstrates a better performance.     

Evaluation of heuristics for scheduling in a flowshop: a case study

The problem of finding the best permutation schedule for a flowshop has engaged the attention of researchers for almost four decades. In view of its NP-completeness, the problem is not amenable to the development of efficient optimizing algorithms. A number of heuristics have been proposed, most of which have been evaluated by using randomly generated problems for a single measure of performance. However, real-life problems often have more than one objective. This paper discusses a live flowshop problem that has the twin objectives of minimizing the production run-time as well as the total flowtime of jobs. Five heuristics are evaluated in this study and some interesting findings are reported.