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Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased, with many of these firms now offering a wide range of robots. A potential user is thus faced with many options in both performance and cost. Proposes a decision model for the robot selection problem using both a robustified Mahalanobis distance analysis, i.e. a multivariate distance measure, and principal‐components analysis. Unlike most other models for robot selection, this model takes into consideration the fact that a robot′s performance, as specified by the manufacturer, is often unobtainable in reality. The robots selected by the proposed model become candidates for factory testing to verify manufacturers′ specifications. Tests the proposed model on a real data set and presents an example.

Addresses an actual problem of assigning customers to employees in a mail order firm. The management of the firm groups customers based on the first letter of the last name. To make assigning responsibility for handling customer orders easier, management specifies that no letter group can be broken up. In other words, all customers with last names beginning with the same letter must be assigned to the same employee. Management also desires a fair assignment among employees. A perfectly fair assignment entails assigning the same number of customers to every employee. A lower bound on the solution to the problem is established and two approaches to the problem are developed. First, it is formulated as a zero‐one goal programming problem and thus commercially available computer codes can be used to solve it. Second, a heuristic approach which assigns customers to employees based on rules similar to assembly line balancing is developed. The zero‐one goal programming approach yields slightly better results. However, it requires longer computer running time.

Extreme temperature and humidity are major causes of errors in industrial precision and in dimensional measurement. In addition, hot and/or humid workplaces have adverse effects on humans which include reduced work time due to more frequent and longer breaks, reduced production rates, and increased error rates. Managers considering investments in air conditioning their manufacturing facilities must weigh many uncertain benefits against more certain costs. In this paper, we identify the benefits resulting from air conditioning manufacturing facilities and develop a Monte Carlo computer simulation model to evaluate investments in air conditioning. The model uses projected incremental cash flows to compute net present value and internal rate of return. Simulation is used to take into account the uncertainty associated with projecting the benefits of air conditioning, deal with possible correlation among some benefits of air conditioning, and sensitize decision makers to the range of possible outcomes. The proposed model is programmed into user‐friendly menu‐driven software which is tested on actual cases and is illustrated in this paper.

Presents a classification for production systems in manufacturing and processing industries. The proposed classification is intended to enlarge the scope of production systems that can be meaningfully classified. This is accomplished by including production system properties used in previous classifications and incorporating new characteristics that describe major changes in emerging automated production systems. The proposed classification is intended to provide better understanding of the functioning of production systems and management approaches available to improving their processes.

The purpose of this paper is twofold: to identify, evaluate and structure the research that focusses on omni-channel retailing from the perspective of logistics and supply chain management; and to reveal the intellectual foundation of omni-channel retailing research.

The paper applies a multi-method approach by conducting a content-analysis-based literature review of 70 academic papers. Based on the reference lists of these papers, the authors performed a citation and co-citation analysis based on the 34 most frequently cited papers. This analysis included multidimensional scaling, a cluster analysis and factor analysis.

The study reveals the limited consideration of logistics and supply chain management literature in the foundation of the omni-channel retailing research. Further, the authors see a dominance of empirical research as compared to conceptual and analytical research. Overall, there is a focus on the Western retail context in this research field. The intellectual foundation is embedded in the marketing discipline and can be characterised as lacking a robust theoretical foundation.

The contribution of this research is identifying, evaluating and structuring the literature of omni-channel research and providing an overview of the state of the art of this research area considering its interdisciplinary nature. This paper thus supports researchers looking to holistically comprehend, prioritise and use the underpinning literature central to the phenomena of omni-channel retailing. For practitioners and academics alike, the findings can trigger and support future research and an evolving understanding of omni-channel retailing.