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Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

Current industry practices illustrate there is no standard method to estimate the number of hours worked on maintenance activities; instead, industry experts use experience to guess maintenance work hours. There is also a gap in the research literature on maintenance work hour estimation. This paper investigates the use of machine-learning algorithms to predict maintenance work hours and proposes a method that utilizes historical preventive maintenance order data to predict maintenance work hours.

The paper uses the design research methodology utilizing a case study to validate the proposed method.

The case study analysis confirms that the proposed method is applicable and has the potential to significantly improve work hour prediction accuracy, especially for medium- and long-term work orders. Moreover, the study finds that this method is more accurate and more efficient than conducting estimations based on experience.

The study has major implications for industrial applications. Maintenance-intensive industries such as oil and gas and chemical industries spend a huge portion of their operational expenditures (OPEX) on maintenance. This research will enable them to accurately predict work hour requirements that will help them to avoid unwanted downtime and costs and improve production planning and scheduling.

The proposed method provides new insights into maintenance theory and possesses a huge potential to improve the current maintenance planning practices in the industry.

Maintenance management consists of an aggregate effort to perform maintenance by effectively utilizing manpower and material through the application of standard procedures. It is a complex and multifaceted task. An ever‐growing number of computerized maintenance management information systems are available on the market to facilitate this task. In order to install a computerized maintenance management system, a company has two options: either to buy or to develop such a system. Briefly describes the major functions of maintenance management and suggests an instrument to evaluate comparatively the available computerized maintenance management systems.

Human reliability in computer programming can be improved by reducing human errors. The traditional approaches for error reduction in industry are not applicable to minimizing errors in computer programming. Proposes a model for error reduction in software prior to its final release. The model consists of two modules, an error detection module, and an error correction module. A computational procedure is outlined for determining the optimal number of detection and correction stages prior to the final release of the software.

The purpose of this paper is to provide an overview of research and development in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in and references for the measurement of maintenance performance. The main focus is to determine how value can be created for organizations by measuring maintenance performance, examining such maintenance strategies as condition‐based maintenance, reliability‐centred maintenance, e‐maintenance, etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.

A state‐of‐the‐art literature review has been carried out to answer the following two research questions. First, what approaches and techniques are used for maintenance performance measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies? Second, in general, how can MPM create value for organizations and, more specifically, which system of measurement is best for which maintenance strategy?

The body of knowledge on maintenance performance is both quantitatively and qualitatively based. Quantitative approaches include economic and technical ratios, value‐based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst other aspects. Qualitatively based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.

A comprehensive review of maintenance performance metrics is offered, aiming to give, in a condensed form, an extensive introduction to MPM and a presentation of the state of the art in this field.

Models inspection co‐ordination of a group of machines using a non‐linear integer program. The objective of the model is to minimize the expected total cost per unit time, which is a function of a major inspection cost, minor inspection cost and the cost of failure, subject to the restriction that the inspection interval of each machine is an integer multiple of a basic time interval. Proposes a solution procedure to solve the inspection co‐ordination problem. Uses a numerical example to illustrate the model and its proposed solution method.

This paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.

The critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.

For a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.

Only three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.

The proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.

This research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

The purpose of this paper is to integrate the decisions regarding optimal process mean and the parameters of a sampling plan.

A model is developed to determine these parameters. The model maximizes producer expected profit, while protecting the consumer through a constraint on the probability of accepting lots with low incoming quality. The model is presented for two cases. The first one is for non‐destructive testing and the other for destructive testing. An example is presented to demonstrate that the utility of the model and sensitivity analysis on key parameters of the model has been conducted.

The findings indicated that the optimal parameters for the process and the sampling plan are significantly different from when determined separately. The sensitivity analysis showed that the process parameters are very sensitive to changes in the process variance, moderately sensitive to the limit on incoming quality, and insensitive to the consumer risk and inspection cost.

The models developed offer an alternative approach for quality managers to address setting process targets, taking into consideration a sampling plan.

The originality of the paper is in the integration of two elements of quality that are usually treated separately in the literature.

Maintenance is a complex process that is triggered by equipment failure or planned repair. This process requires planning, scheduling, control and the deployment of maintenance resources to perform necessary maintenance activities. In this paper a generic conceptual model for maintenance systems has been developed. The conceptual model consists of seven modules. The first one is the input module in which the characteristic of the maintenance system is specified. The second module is concerned with modeling the maintenance load. The third module is the planning and scheduling. This module is the most critical, since it controls the maintenance process. The fourth module is the material and spare parts supply, followed by the equipment availability module. The sixth module is the quality control module and the performance measures are the seventh module. The specification of such a conceptual model lays the ground for developing a realistic simulation model.

In this paper we incorporate Taguchi's loss function in the economic design of the control chart. This is done by redefining the in‐control and out‐of‐control costs using Taguchi's loss function. The new model brings Taguchi's off‐line concepts to the classical SPC approach and continues to have the advantages of the economic design model by taking into consideration the cost consequences of the design. Sensitivity analysis with respect to important model parameters is discussed.