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.     

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.     

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.     

Single cycle policies for the one-warehouse N-retailer inventory/distribution system

We address a multi-echelon inventory system with one-warehouse and N  -retailers. The demand at each retailer is assumed to be known and satisfied by the warehouse. Shortages are not allowed and lead times are negligible. Costs at each facility consist of a fixed charge per order and a holding cost. The goal is to determine single-cycle policies which minimize the average cost per unit time, that is, the sum of the average holding and setup costs per unit time at the retailers and at the warehouse. We propose a O(NlogN)$\mathrm{O}(N\log N)$ heuristic procedure to compute efficient single-cycle policies. This heuristic is compared with other approaches proposed by Schwarz, Graves and Schwarz and Muckstadt and Roundy. We carry out a computational study to test the effectiveness of the heuristic and to compare the performance of the different procedures. From the computational results, it is shown that the new heuristic provides, on average, better single-cycle policies than those given by the Muckstadt and Roundy method.     

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.     

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.     

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.     

Pareto‐Improving Contracts for Express Package Delivery Services

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.     

Optimal responses to stockouts: Lateral transshipment versus emergency order policies

In the retail industry, stockouts have a significant effect on a firm׳s profitability. When a stockout takes place, retailers often apply one of two strategies to resolve the issue – placing an emergency order with their supplier or arranging a lateral transshipment with a nearby partner store. Choosing the optimal response to a stockout is complicated by customers׳ spontaneous reactions. Customers who find that a product is out of stock may choose to give up on the purchase, to wait for delivery (through emergency order or lateral transshipment), or go to a partner store to search for the product on their own. In this study, under a single-period setting with two retail stores, we investigate the optimal inventory decisions under each strategy, and conduct a comparison between lateral transshipment and emergency order options. We also analyze the effects of the customer requesting rate and switching rate on the optimal inventory decision. Through numerical analysis, the two strategies are compared in terms of inventory levels and profitability. The results suggest that in addition to the cost associated with each of these strategies, the customers׳ behavior in response to a stockout has a significant effect on the optimal decision. The emergency order strategy is a better option when more customers request deliveries or when more customers switch to another store. Extending this analysis, we also examine the combined strategy when an emergency order is placed after a transshipment fails to fulfill unmet demands, and explore the circumstances under which this strategy provides the highest additional profit for the stores. Finally, we also find that a higher requesting rate does not necessarily increase profits, particularly when there is a high customer switching rate, because requesting emergency order or transshipment reduces switching demand.     

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.     

Vehicle routing using fixed delivery areas

In this paper we consider the problem of vehicle routing using fixed delivery areas. This is the problem of splitting the area serviced by a depot into a number of separate subareas—a single delivery vehicle being assigned to each subarea to supply all the customers within the subarea. A heuristic algorithm is developed for the problem based upon an initial allocation of customers to subareas followed by customer interchanges in an attempt to improve the subareas. Computational results are presented for a number of test problems drawn from the literature.     

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.     

An Exploratory Study of the Impact of e‐Service Process on Online Customer Satisfaction

Although extensive academic research has examined the dynamics of interpersonal interactions between service providers and customers, much less research has investigated customer service encounters through technological interfaces such as the Web in electronic commerce transactions. Corporate websites have become an important point of contact with customers for many companies. Service has been described as one of the most important attributes for online business to influence traffic and sales. However, more research is needed to understand how Web‐based technological capabilities of services affect customer satisfaction. In this paper, we propose viewing the interface between online buyers and sellers through the lens of service management to identify possible determinants of online customer satisfaction. A company's website is considered its electronic service delivery system. We look at this electronic service delivery system from its process point of view. Our findings indicate that as the electronic service delivery system process improves, a customer's perception of the website's ease of use increases, leading to increased service value and perceived control over the process, which increases customer satisfaction. The research provides evidence that the technological capabilities embedded in the website processes are an important factor in determining service quality and ultimately online customer satisfaction.     

B2C电子商务环境下订单拣选与配送联合调度优化

如何在顾客下单后协调好拣选和配送环节,在最短的时间、以较低的成本将商品从货架上拣出、打包后配送到顾客手中,已成为B2C电子商务物流管理中亟待解决的问题。本文尝试以最小化订单履行时间为目标,构建非线性拣选与配送联合调度模型,以解决订单拣选顺序、拣选作业方式、车辆行驶线路等联合决策。为求解此NP难问题,设计了三阶段启发式算法：首先采用“聚类-路径优化”思想,依据顾客位置进行配送方案确认;然后采用基于相似度聚类的订单分批规则对每条配送线路的订单进行分批合并;最后调整拣选任务与配送线路顺序。通过数据实验对模型进行验证,并与传统拣选与配送分开优化的结果进行对比。结果表明,三阶段算法能够有效缩短订单完成时间、降低配送车辆等待时间、改善配送资源利用率。