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A large number of researchers have addressed social aspects in hierarchical production planning. This article responds to research gaps identified in our previous literature review. Accordingly, consideration of social aspects and the economic implications of social improvements are required in a longer term planning approach. For this, we integrate work intensity as employee utilization in a general mixed-integer programming model for master production scheduling. Following existing fatigue functions, we represent the relationship between work intensity and exhaustion through an employee-utilization-dependent exhaustion function. We account for the economic implications through exhaustion-dependent capacity load factors. We solve our model with a CPLEX standard solver and analyze a case study based on a realistic production system and numerical data. We demonstrate that the consideration of economic implications is necessary to evaluate social improvements. Otherwise, monetary disadvantages are overestimated, and social improvements are, thus, negatively affected. Moreover, from a certain level of work-intensity reduction, demand peaks are smoothed more by pre-production, which requires more core employees, while temporary employment is reduced. Further potential may arise from considering and quantifying other interdependencies, such as employee exhaustion and employee days off. In addition, the relationship between social working conditions and employee turnover can be integrated.
This paper proposes two stochastic programming models for master production scheduling with capacity-load factor scenarios. In contrast to other work on production planning with load-dependent lead times or dynamic capacity loads, we iteratively build a set of realistic capacity-load factor scenarios by simulating the realization of the master production schedules in a rolling horizon environment. Therefore, we integrate the models into a hierarchical production planning and control system that is common in industrial practice and measure the effective capacity-load factors. With these factors, we resolve the master production scheduling problem. Toa evaluate the performance of the proposed models, we compare the stochastic models with the common approach to reduce the nominally available capacity for master production scheduling. In our experiments, the stochastic models significantly reduce the tardiness of production orders caused by capacity bottlenecks.
Final assembly at Krones AG must make the best possible use of its production space and meeting the specified customer due dates is critical. Via a self developed simulation tool, the present scheduling procedure is compared with the one by priority rule shortest slack. As a consequence slack should have a higher importance in the planning
This paper introduces a novel chaotic flower pollination algorithm (CFPA) to solve a tardiness-constrained flow-shop scheduling problem with simultaneously loaded stations. This industrial manufacturing problem is modeled from a filter basket production line in Germany and has been generally solved using standard deterministic algorithms. This research develops a metaheuristic approach based on the highly efficient flower pollination algorithm coupled with different chaos maps for stochasticity. The objective function targeted is the tardiness constraint of the due dates. Fifteen different experiments with thirty scenarios are generated to mimic industrial conditions. The results are compared with the genetic algorithm and with the four standard benchmark priority rule-based deterministic algorithms of First In First Out, Raghu and Rajendran, Shortest Processing Time and Slack. From the obtained results and analysis of the relative difference, percentage relative difference and t tests, CFPA was found to be significantly better performing than the deterministic heuristics and the GA algorithm.
Sustainable production includes economic, environmental, and social aspects. However, social sustainability has received less attention, especially compared to the economic aspects. Next to technical and organizational measures, social improvements within supply chains can also be achieved through suitable production planning. Within production planning, production programs are determined, and the assignment of available resources (e.g., employees) is specified. Thus, the utilization and workload of employees are defined. This systematic literature review investigates to what extent such employee-related social aspects are reflected in production planning and discusses whether economic aspects dominate them. For this, a Scopus database search was carried out and 76 identified approaches were analyzed and categorized regarding the occurring employee-related social aspects and their implementation. Thus far, the approaches mainly consider single aspects on single planning levels. A consideration of a broad set of aspects along the entire production planning has rarely been studied. In particular, health and safety aspects are considered on the levels of assembly line balancing and job rotation. However, their impact is primarily determined by the specific settings of the decision-maker. To support decision-makers, only a few studies have investigated the effects based on real application scenarios. Further potential might be an extended modeling of social and economic interdependencies and a consideration of employee-related social aspects in medium- to long-term production planning.
Emission reduction through production scheduling by priority rules and energy onsite generation
(2022)
This article describes primary findings of various simulation runs on job shop scheduling dealing with energy consumption and emission pollution. By two combinations of priority rules, production is linked to the generation output of a renewable energy source installed onsite. The resulting schedules show a reduction of energy-related emissions and makespan compared to several conventional priority rules often used in industrial practice.
Storage and retrieval times of goods in inventory processes determine the efficiency of highbay warehouses in companies. These times are highly dependent on the performance of rack feeders. Storage strategies have the greatest influence. The paper shows the impact of an efficient control of rack feeders through storage strategies. Storage strategies which are typically used in industrial inventory processes are analysed. For the analysis a simulation system is implemented, so that the real process times are similar to the simulated ones. This results in recommendations for the use of storage strategies in companies.