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Increasing global energy consumption, large variations in its cost and the environmental degradation effects are good reasons for the manufacturing industries to become greener. Green shop floor scheduling is increasingly becoming a vital factor in the sustainable manufacturing. In this paper, a green permutation flowshop scheduling problem with sequence-dependent setup times is studied. Two objectives are considered including minimisation of makespan as a measure of service level and minimisation of total energy consumption as a measure of environmental sustainability. We extend a bi-objective mixed-integer linear programming model to formulate the stated problem. We develop a constructive heuristic algorithm to solve the model. The constructive heuristic algorithm includes iterated greedy (CHIG) and local search (CHLS) algorithms. We develop an efficient energy-saving method which decreases energy consumption, on average, by about 15%. To evaluate the effectiveness of the constructive heuristic algorithm, we compare it with the famous augmented ϵ-constraint method using various small-sized and large-sized problems. The results confirm that the heuristic algorithm obtains high-quality non-dominated solutions in comparison with the augmented ϵ-constraint method. Also, they show that the CHIG outperforms the CHLS. Finally, this paper follows a case-study, with in-depth analysis of the model and the constructive heuristic algorithm. [ABSTRACT FROM AUTHOR]

The transportation network resiliency plays a significant role in reducing the devastating impacts of a disaster. This paper develops a mathematical model to improve the resilience of road–bridge transportation networks in the recovery phase. The model consists of two objective functions. The first objective function minimises total recovery time, and the second one prioritises damaged bridges for restoration to increase network performance level in an early stage by maximising the skewness of the recovery trajectory. This paper considers disrupted bridges as repair projects and schedules a network of recovery tasks with precedence relationships to restore each bridge while considering resource constraints. Thus, it is similar to the resource–constraint multi-project scheduling problem. A genetic algorithm is applied to solve the model for a large-scale hypothetical road–bridge transport network, and the highway network of Shelby County, TN, the USA, following a seismic hazard. Different recovery task networks are considered to restore disrupted bridges. Three communities with rich, average and poor resources are compared to evaluate the impact of different community investment structures on the recovery process and network recovery time. The results show that the proposed restoration plan leads to achieving the best recovery trajectory and shortest total recovery time. [ABSTRACT FROM AUTHOR]

The health service network has problems such as a shortage of medical equipment and human resources. Due to the need for high expertise in supplying these facilities, this problem is much harder to be solved than other industrial ones. In the COVID-19 pandemic, maintaining tranquility in society is the most important factor. The tranquility is obtained by providing medical facilities in the health care network. Also, the COVID-19 pandemic imposes new restrictions on the network because of preventive guidelines. In this situation, the problem of resource allocation will become more sophisticated and will reduce system efficiency. In this paper, the problem of transferring hospital beds to patients infected by COVID-19 considering a predetermined capacity level is considered. To cope with these problems, a mixed-integer mathematical programming model is suggested. In addition, to consider the uncertainty in the demand of patients that occurs in the pandemic, the fuzzy programming approach is used. The suggested model is solved with the Benders decomposition algorithm (BDA) and applied for assigning beds in two samples. The results show that proper management of resources in crisis situations such as the COVID-19 outbreak is very effective. As a result, this issue causes to overcome pressure on medical staff and lack of hospital facilities, during pandemic conditions. [ABSTRACT FROM AUTHOR]

Job scheduling and maintenance activities scheduling are two important issues in production management, which are often addressed separately. In this paper, the non-permutation flow shop scheduling problem with learning effects and flexible maintenance activities is studied. Each machine has a number of preventive maintenance activities that should be finished within specific time intervals. The aim is to simultaneously determine the sequence of jobs and the finish time of maintenance activities for minimising the sum of tardiness costs and maintenance costs. A mixed integer linear programming model is proposed to formulate the problem. Owing to the high complexity of the problem, an improvement heuristic method and a hybrid meta-heuristic algorithm based on the simulated annealing algorithm and firefly algorithm is presented to find nearly optimal solutions for medium and large problems. To obtain better and more robust solutions, the Taguchi method is used in order to calibrate the hybrid algorithm parameters. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution approaches. [ABSTRACT FROM PUBLISHER]

Sustainability consciousness in manufacturing has become an interesting topic for many researchers in recent years. There is also more concern in many companies about reducing energy consumption in manufacturing. Improving environmental health and safety, production cost saving, access to governmental incentives such as grants and tax credits and also improving the brand image are the most important reasons which is leading many companies to an environmental-friendly production planning. For example, one of the most applicable scheduling problems deals with planning jobs on numbers of parallel machines. In such an application, different machines have different technologies and different speed and power of energy consumption in manufacturing similar jobs. This article introduced a mathematical formulation which models the green parallel machines scheduling problem with total energy consumption and total completion time as objectives. Due to high computational complexity of the proposed model, a heuristic algorithm is developed to obtain the exact Pareto frontier of these two objectives with a polynomial complexity. Numerical experiments are presented to show the efficiency and speed of the proposed algorithm compared to solving the model using the optimisation software directly. [ABSTRACT FROM AUTHOR]