Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers

© 2014 IEEE. In this paper, a supply disruption management model is introduced in a three-tier supply chain with multiple suppliers and retailers, where the system may face sudden disruption in its raw material supply. At first, we formulated a mathematical model for ideal conditions and then reformulated it to revise the supply, production and delivery plan after the occurrence of a disruption, for a future period, to recover from the disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We have also proposed an efficient heuristic to solve the model and the results have been compared, with another established solution approach, for a good number of randomly generated test problems. The comparison showed the consistent performance of our developed heuristic. This paper also presents some numerical examples to explain the usefulness of the proposed approach

Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy

Background The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89.6 per cent) compared with that in countries with a middle (753 of 1242, 60.6 per cent; odds ratio (OR) 0.17, 95 per cent c.i. 0.14 to 0.21, P <0001) or low (363 of 860, 422 per cent; OR 008, 007 to 010, P <0.001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference -94 (95 per cent c.i. -11.9 to -6.9) per cent; P <0001), but the relationship was reversed in low-HDI countries (+121 (+7.0 to +173) per cent; P <0001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0.60, 0.50 to 073; P <0.001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.Peer reviewe

Global variation in anastomosis and end colostomy formation following left-sided colorectal resection

Background End colostomy rates following colorectal resection vary across institutions in high-income settings, being influenced by patient, disease, surgeon and system factors. This study aimed to assess global variation in end colostomy rates after left-sided colorectal resection. Methods This study comprised an analysis of GlobalSurg-1 and -2 international, prospective, observational cohort studies (2014, 2016), including consecutive adult patients undergoing elective or emergency left-sided colorectal resection within discrete 2-week windows. Countries were grouped into high-, middle- and low-income tertiles according to the United Nations Human Development Index (HDI). Factors associated with colostomy formation versus primary anastomosis were explored using a multilevel, multivariable logistic regression model. Results In total, 1635 patients from 242 hospitals in 57 countries undergoing left-sided colorectal resection were included: 113 (6·9 per cent) from low-HDI, 254 (15·5 per cent) from middle-HDI and 1268 (77·6 per cent) from high-HDI countries. There was a higher proportion of patients with perforated disease (57·5, 40·9 and 35·4 per cent; P < 0·001) and subsequent use of end colostomy (52·2, 24·8 and 18·9 per cent; P < 0·001) in low- compared with middle- and high-HDI settings. The association with colostomy use in low-HDI settings persisted (odds ratio (OR) 3·20, 95 per cent c.i. 1·35 to 7·57; P = 0·008) after risk adjustment for malignant disease (OR 2·34, 1·65 to 3·32; P < 0·001), emergency surgery (OR 4·08, 2·73 to 6·10; P < 0·001), time to operation at least 48 h (OR 1·99, 1·28 to 3·09; P = 0·002) and disease perforation (OR 4·00, 2·81 to 5·69; P < 0·001). Conclusion Global differences existed in the proportion of patients receiving end stomas after left-sided colorectal resection based on income, which went beyond case mix alone

Resource constrained project scheduling with uncertain activity durations

© 2017 Elsevier Ltd In this paper, we consider Resource Constrained Project Scheduling Problems (RCPSPs) with known deterministic renewable resource requirements but uncertain activity durations. In this case, the activity durations are represented by random variables with different probability distribution functions. To deal with this problem, we propose an approach based on the robust optimization concept, which produces reasonably good solutions under any likely input data scenario. Depending on different uncertainty characteristics, we have developed six different heuristics to incorporate the uncertain duration as a deterministic constraint in a robust optimization model. The resulting optimization model is then solved by using a Coin-Branch & Cut (CBC) solver. To judge the performance of the algorithm, we solved 30, 60, 90 and 120-activity benchmark problems from the project scheduling problem library (PSPLIB). Our proposed approach guarantees the feasibility of solutions and produces high-quality solutions, particularly for larger activity instances, compared to other existing approaches

2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017

© 2017 IEEE. Simulation Optimization is computationally expensive, especially in large-scale stochastic problem solving, where the computational budget is considered as an important factor. A higher computational budget attempts to generate highly accurate solutions while a lower budget might result in biased or unrealistic solutions. In this paper, the effect of computational budget on the quality of the solution, in the context of Simulation Optimization, has been studied. The study is conducted by combining a Multi-operator Differential Evolutionary Algorithm with Monte-Carlo simulation. For experimentation, the stochastic test problems were generated based on the IEEE-CEC'2006 constrained optimization competition test problems. The experimental results provide interesting insights about the behavior of simulation optimization that would allow to reduce the computational time not compromising the quality of solutions

Single mode resource constrained project scheduling with unreliable resources

© 2018 Springer-Verlag GmbH Germany, part of Springer Nature Over the last few decades, resource constrained project scheduling has been widely studied. In real life, resources may fail or be interrupted due to various reasons. They then require reactive rescheduling to minimize the effect of such disruptions. During the course of a project, a single or a series of independent disruptions may take place where the disruption information is not known a priori. In this paper, we have formulated two discrete time based models to deal with two types of disruption scenarios. We have also proposed a solution approach that can deal with a single, as well as a series of independent disruptions, in a reactive rescheduling manner. To judge the performance of the proposed approach, a number of test instances from the Project Scheduling Library were combined with randomly generated disruption events. The computational experiments were also conducted to analyse the effects of different factors related to the disruption recovery process

Adaptive simulation budget allocation in Simulation assisted Differential Evolution algorithm

Recently, the literature on simulation assisted optimization for solving stochastic optimization problems has been considerably growing. In the optimization context, the population based meta-heuristics algorithms, such as, Differential Evolutionary (DE), has shown tremendous success in solving continuous optimization problems. While in the simulation context, Monte-Carlo Simulation for sample average approximation is one of the successful approaches in handling the stochastic parameters of such problems. However, the intertwined computational burden, when combining these two approaches is amplified, and that encourages new research in this topic. In this problem, the challenge is to maintain high quality stochastic solutions by minimizing the computational cost to a reasonable level. To deal with this challenge, we propose a novel Adaptive Segment Based Scheme (ASBS) algorithm, for allocating the MCS budget in a Simulation assisted Differential Evolution (Sim-DE) Algorithm. This allows the algorithm to adaptively control the simulation budget based on a performance measure. The performance of the proposed ASBS algorithm is compared with other simulation budget allocation techniques while using the same base algorithm. The experimental study has been conducted by solving a modified set of IEEE-CEC’2006 test problems and a wind-thermal power systems application. The experimental results reveal that the ASBS algorithm is able to substantially reduce the simulation budget, with an insignificant effect in solution quality

Single mode resource constrained project scheduling with unreliable resources

© 2018 Springer-Verlag GmbH Germany, part of Springer Nature Over the last few decades, resource constrained project scheduling has been widely studied. In real life, resources may fail or be interrupted due to various reasons. They then require reactive rescheduling to minimize the effect of such disruptions. During the course of a project, a single or a series of independent disruptions may take place where the disruption information is not known a priori. In this paper, we have formulated two discrete time based models to deal with two types of disruption scenarios. We have also proposed a solution approach that can deal with a single, as well as a series of independent disruptions, in a reactive rescheduling manner. To judge the performance of the proposed approach, a number of test instances from the Project Scheduling Library were combined with randomly generated disruption events. The computational experiments were also conducted to analyse the effects of different factors related to the disruption recovery process