Column generation for service assignment in cloud-based manufacturing

This research studies the cellular manufacturing system (CMS) controlled by kanban mechanism which defective items are produced in any production run of each product and rework is carried out to transform them into serviceable items. We consider and compare two different policies for rework where in the first policy rework is completed within the same production cycle and in the second policy rework done after N production cycles. Recently Aghajani et al. (2012) explain policy 2 and proposed a mixed-integer nonlinear programming (MINLP) model for this policy. In order to minimize total cost, MINLP model was developed for policy 1 to find simultaneously the optimal number of kanban, batch size, and number of batches. The cost function includes the cost of setup, holding, and transportation. Due to the high combinatorial structure of the problem, particle swarm optimization (PSO), and simulated annealing (SA) algorithms as meta-heuristic methods are proposed to solve the problem and numerical experiments are conducted to demonstrate the efficiency of the proposed algorithms. It is shown that both PSO and SA result are in a near optimal solution but the PSO algorithm gives a better performance than the SA method. Also, sensitivity analysis is carried out to study the effect of defective rate, holding cost, and setup cost variations on the total system cost is discussed the performance of these policies in different conditions.

A cutting plane algorithm for the site layout planning problem with travel barriers

This paper studies an integrated optimization problem of production-inventory and retail pricing decision for a single-manufacturer multi-retailer system of deteriorating items under just-in-time (JIT) delivery environment. The objective of the model is to maximize the total profit of the system which equals to the total revenue minus the total cost. The features of the model lie in three facets: first, revenue is dependent on the demand and selling price, while demand is price sensitive; second, transportation costs between manufacturer and retailers are included in assessing the total cost, and third, customer service level constraint is also considered in the model. Since the model is a complex mixed-integer nonlinear programming (MINLP) model, it is difficult to derive a closed-form solution using classic differentiation methods. In this paper, two meta-heuristic algorithms, particle swarm optimization (PSO) and quantum-behaved PSO (QBPSO) are developed to solve the model. Experiments show that QBPSO is more effective and more efficient than basic PSO. Sensitivity analysis reveals the impact of model parameters on solution, and some important managerial implications on the JIT strategy are summarized.

Cloud manufacturing is a new service-oriented manufacturing model that integrates distributed manufacturing resources to provide on-demand manufacturing services over the Internet. Service composition that builds larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers’ complex requirements is an important issue in cloud manufacturing. Meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. However, these algorithms require complex design flows and lack adaptability to dynamic environment. Deep reinforcement learning provides an alternative approach for solving cloud manufacturing service composition issues. This chapter proposes a deep Q-network (DQN) based approach for service composition in cloud manufacturing, which is able to find optimal service composition solutions through repeated training and learning. Results of experiments that take into account changes of service scales and service unavailability reveal the scalability and robustness of the DQN algorithm-based service composition approach.

The length of a rail line that will minimize total transportation (user and operator) costs, and the threshold demand necessary to ensure that the resulting length of the rail line is nonzero are investigated for an urban transportation corridor. A density of demand for travel to the central business district (CBD) represented by a general function P(x) passengers per unit length of the corridor where x is the distance from the CBD is considered. The line-cost as a function of x, the costs of the rail fleet, rail and bus operating costs and passenger time costs are also considered in the formulation. The fleet size is formulated considering the peaking of demand relative to time. When the line cost is nonuniform there could be several line lengths at which the total transportation cost is minimized or even maximized locally. When the line cost per unit length is uniform, a minimum transport cost rail line of nonzero length exists only if the net gain in travel time and operating cost of transporting the total ridership a unit distance by rail, when compared to bus, exceeds the marginal line and fleet costs per unit length. In either case, the minimum transport cost rail line length can be determined easily. The effects on the line length of shifts in demand are investigated. Closed-form solutions for the line length are obtained for the cases of sectorial and rectangular corridor-sheds with uniformly distributed demand per unit area.

Cloud manufacturing is defined as a resource sharing paradigm that provides on-demand access to a pool of manufacturing resources and capabilities aimed at utilising geographically dispersed manufacturing resources in a service-oriented manner. These services are deployed via the Industrial Internet of Things and its underlying IT infrastructure, architecture models, as well as data and information exchange protocols and standards. In this context, interoperability has been identified to be a key enabler for implementing such vertically or horizontally integrated cyber-physical systems for production engineering. Previously, the authors proposed an interoperability framework called C-MARS (Cloud Manufacturing Resource Sharing System) to enable error free and non-ambiguous information transfer between the various components and layers of a typical cloud manufacturing system. The main contribution of this paper concerns the identification of the key process parameters for deploying cloud manufacturing processes via a generic and costing-based operation and deployment model (CMARS-ABM) used to simulate interoperable cloud-based manufacturing scenarios for parts of different complexity, varying production numbers, and service composition setups typical to SMEs of varying sizes. Initial results confirm that Cloud Manufacturing is not a one-size-fits-all solution, and that there are indeed a number of driving parameters that need to be analysed to determine whether or not an investment in cloud manufacturing may be financially beneficial and advisable given a specific scenario.

This paper considers logistics decision support system which deals with transportation mode selection considering transportation and carbon emission cost. Transportation and carbon emission costs vary with the choice of transportation modes and to become competitive companies need to find proper transportation modes for their logistics services. However, due to the restricted capacity of transportation modes, it is difficult to balance transportation and carbon emission costs when designing logistics network including transportation mode choice for each service. Therefore this paper aims to analyze the trade-off relationship between transportation and carbon emission cost in mode selection of intermodal transportation and to provide optimal green logistics strategy. In this paper, the logistics decision support system is designed based on mixed integer programming model. To understand the trade-off relationship of transportation and carbon emission cost, the system is tested with various scenarios including transportation of containers between Seoul and Busan. The analysis results show that, even though sea transportation combined with trucking is competitive in carbon emission per unit distance travelled, the total cost of carbon emission and transportation for the sea transportation may not have competitive advantage over other transportation modes including rail and truck transportation modes. The sea-based intermodal logistics service may induce detours which have negative impacts on the overall carbon emission. The proposed logistics decision support system is expected to play key role in green logistics and supply chain management.

Optimization of the multi-level steam production and supply in industrial parks

Estimating transport costs and trade barriers in China: Direct evidence from Chinese agricultural traders

This study aims to identify and analyze human factors (HFs) affecting cloud manufacturing (CM) adoption across manufacturing micro, small and medium enterprises (MSMEs). Ranking of the identified HFs as per their importance was done using the AHP method. Further, the DEMATEL was used to get contextual interrelation and cause-effect relationships among the HFs. Results of the AHP method showed that factors vision of top management (HF4), collaborative human-robotsystem (HF1), real-time decision making (HF6), customer-centric servitization (HF8) and Skillset of employees (HF3) are the high priority factors. These factors also belong to the causal factor (most significant factor) group, determined using the DEMATEL approach. Thus, to enhance CM adoption, decision and policymakers of the manufacturing firm should provide more attention to improvize these factors. The study’s findings will be helpful to human resource managers, decision-makers and policymakers of the manufacturing firm to formulate effective short-term and long-term implementation plans and strategies. Further, the study’s outcomes can also be helpful for CM service providers to improve their marketing strategies. This is one of the preliminary research which analyzed human aspects of CM adoption across Indian manufacturing MSMEs using a hybrid AHP-DEMATEL approach.

This paper studies an integrated scheduling problem which consists of both production and transportation stages. Jobs are processed on a set of parallel batch-processing machines in the production stage, and then transported to a target customer in the transportation stage. The aim is to find an optimal integrated production and transportation schedule with the objective to minimize the weighted sum of total delivery time and total transportation cost with a relative preference α. A 0-1 mixed integer programming model is then constructed. To solve the proposed model, which is NP-hard, a developed GA is proposed to produce near-optimal solutions for large-size instances. Computational experiments on 720 instances with up to 150 jobs and 5 parallel batch-processing machines show the effectiveness and efficiency of the developed GA.

Nowadays automation is a trend of container terminals all over the world. Although not applied in current automated container terminals, storage allocation is indispensable in conventional container terminals, and promising for automated container terminals in future. This paper seeks into the storage allocation problem in automated container terminals and proposed a two level structure for the problem. A mixed integer programming model is built for the upper level, and a modified Particle Swarm Optimization (PSO) algorithm is applied to solve the model. The applicable conditions of the model is investigated by numerical experiments, so as the performance of the algorithm in different problem scales. It is left to future research the lower level of the problem and the potential benefit of storage allocation to automated container terminals.

Modeling Pooled Purchasing Strategy in Purchasing Consortium to Optimize Total Purchasing Cost

A thermoeconomic assessment and multi-objective optimization of a novel CCHP and multi-effect desalination (MED) hybrid system driven by an internal-reforming solid oxide fuel cell (IR-SOFC) and a micro gas turbine (MGT) are presented to comprehensively evaluate the hybrid system performance in this research. Firstly, the sensitivity analysis of system key design parameters such as steam to carbon ratio, compressor pressure ratio, pinch point temperature difference, motive steam pressure of MED and inlet pressure of ORC turbine on energy, exergy, economic, and thermoeconomic performance is investigated. It is indicated from the analysis results that the proposed hybrid system could produce power, cooling, heating and potable water which are 300.0 kW, 7.358 kW, 8.757 kW and 0.1715 kg/s, respectively. The system total cost and total cost per unit exergy are $9.838/h and $14.75/GJ under the design condition. A multi-objective optimization by using NSGA-II method is further implemented to obtain the ideal system design parameters and optimal system exergy efficiency, freshwater production, and total cost per unit exergy by setting four groups of different weights to these three objectives. The three-dimensional and two-dimensional Pareto frontier curves of the optimization results are depicted to reveal the relations between the objectives. It is validated that the system exergy efficiency and the system total cost per unit exergy are conflicted with the freshwater production.

Cloud manufacturing as an emerging service-oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. The service composition and optimal selection (SCOS) problem, in which manufacturing cloud services are optimally selected for performing subtasks, is one of the key issues for implementing a cloud manufacturing system. In this paper, we propose a new mixed-integer programming model for solving the SCOS problem with sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services are considered. Although a wide variety of metaheuristics have been tailored for solving the SCOS problem, no consistent and comprehensive conclusion has been reached so far on the superiority of a specific algorithm. Therefore, for the first time, solution space landscape of the problem was analyzed through several statistical criteria which demonstrated that the landscape is rugged and local optima are clustered in a small region of the search space. Therefore, to find good solutions, a metaheuristic algorithm needs to perform both proper exploitation and exploration of the search space. According to the landscape analysis, the basic imperialist competitive algorithm (ICA) was hybridized with a local search (LS) algorithm resulting in the hybrid ICA (HICA). To examine the performance of the proposed HICA, an example of online motorcycle production in the USA as well as four randomly generated large-scale instances, were solved through the LS, ICA, and HICA. Computational results showed that transportation consideration is indispensable for obtaining more realistic solutions in cloud manufacturing. The results also revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function, the stability of solutions and convergence speed. Hence, not only statistically but also analytically, it was proved that algorithms incorporating both exploitation and exploration are able to solve the SCOS problem more efficiently.