AN ALGORITHM TO SOLVE THE ASSOCIATIVE PARALLEL MACHINE SCHEDULING PROBLEM

Effective production scheduling is essential for improved performance. Scheduling strategies for various shop configurations and performance criteria have been widely studied. Scheduling in parallel machines (PM) is one among the many scheduling problems that has received considerable attention in the literature. An even more complex scheduling problem arises when there are several PM families and jobs are capable of being processed in more than one such family. This research addresses such a situation, which is defined as an Associative Parallel Machine scheduling (APMS) problem. This research presents the SAPT-II algorithm that solves a highly constrained APMS problem with the objective to minimize average flow time. A case example from a make-to-order industrial product manufacturer is used to illustrate the complexity of the problem and evaluate the effectiveness of the scheduling algorithm

Target-driven sustainable product development

Figuring in sustainability in product development requires a profound understanding of the cause and effect of engineering decisions along the full spectrum of the product lifecycle and the triple bottomline of sustainability. Sustainability design targets can contribute to mitigating the complexity involved, by means of a formalised problem description. This article discusses how sustainability design targets can be defined and presents methods for systematically implementing these targets into the design process. To that end, different means of decision support mechanisms are presented. They comprise (a) use cases of target breakdowns in subsystems, (b) systematic reduction of solution space and (c) assistance in design activities to ensure achievement of sustainability design targets. This paper explains how interfaces to engineering tools such as Computer Aided Design/Engineering (CAD/CAE) or Product Data/Lifecycle Management (PDM/PLM) can be put in place to make the process of retrieving information and providing decision support more seamless

Quantitative modeling and analysis of supply chain risks using Bayesian theory

Purpose Globally expanding supply chains (SCs) have grown in complexity increasing the nature and magnitude of risks companies are exposed to. Effective methods to identify, model and analyze these risks are needed. Risk events often influence each other and rarely act independently. The SC risk management practices currently used are mostly qualitative in nature and are unable to fully capture this interdependent influence of risks. The purpose of this paper is to present a methodology and tool developed for multi-tier SC risk modeling and analysis. Design/methodology/approach SC risk taxonomy is developed to identify and document all potential risks in SCs and a risk network map that captures the interdependencies between risks is presented. A Bayesian Theory-based approach, that is capable of analyzing the conditional relationships between events, is used to develop the methodology to assess the influence of risks on SC performance Findings Application of the methodology to an industry case study for validation reveals the usefulness of the Bayesian Theory-based approach and the tool developed. Back propagation to identify root causes and sensitivity of risk events in multi-tier SCs is discussed. Practical implications SC risk management has grown in significance over the past decade. However, the methods used to model and analyze these risks by practitioners is still limited to basic qualitative approaches that cannot account for the interdependent effect of risk events. The method presented in this paper and the tool developed demonstrates the potential of using Bayesian Belief Networks to comprehensively model and study the effects or SC risks. The taxonomy presented will also be very useful for managers as a reference guide to begin risk identification. Originality/value The taxonomy developed presents a comprehensive compilation of SC risks at organizational, industry, and external levels. A generic, customizable software tool developed to apply the Bayesian approach permits capturing risks and the influence of their interdependence to quantitatively model and analyze SC risks, which is lacking

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population