Job selection and scheduling are among the most important decisions for production planning in today’s manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of jobs and schedule them in such a way that the total net profit is maximized. The cost components considered include jobs & apos; processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS) and simulated annealing (SA), were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational time.
Supplier selection management has been considered as an important subject for industrial organizations. In order to remain on the market, to gain profitability and to retain competitive advantage, business units need to establish an integrated and structured supplier selection system. In addition, environmental protection problems have been big solicitudes for organizations to consider green approach in supplier selection problem. However, finding proper suppliers involves several variables and it is critically a complex process. In this paper, the main attention is focused on finding the right supplier based on fuzzy multi criteria decision making (MCDM) process. The weights of criteria are calculated by analytical hierarchical process (AHP) and the final ranking is achieved by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS). TOPSIS advantage among the other similar methods is to obtain the best solution close to ideal solution. The paper attempts to express better understanding by an example of an automobile manufacturing supply chain.
This paper analyzes the integration of two combinatorial problems that frequently arise in production and distribution systems. One is the Bin Packing Problem (BPP) problem, which involves finding an ordering of some objects of different volumes to be packed into the minimal number of containers of the same or different size. An optimal solution to this NP-Hard problem can be approximated by means of meta-heuristic methods. On the other hand, we consider the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), which is a variant of the Travelling Salesman Problem (again a NP-Hard problem) with extra constraints. Here we model those two problems in a single framework and use an evolutionary meta-heuristics to solve them jointly. Furthermore, we use data from a real world company as a test-bed for the method introduced here.
Two procedures to evaluate fracture resistance of notched components are proposed in this contribution: the Strain Energy Density (SED) over a control volume and the Cohesive Zone Model (CZM). With the aim to simplify the application of the two fracture criteria, the concept of the ‘equivalent local mode I’ is presented. The control volume of the SED criterion and the cohesive crack of the CZM, have been rotated along the notch edge and centered with respect to the point where the elastic principal stress is maximum. Numerical predictions are compared with experimental results from U and V shaped notches under three point bending with notch root radius ranging from 0.2 to 4.0 mm. In parallel the loading conditions vary, from pure mode I to a prevailing mode II. All specimens were made of PMMA and tested at -60°C. The good agreement between theory and experimental results adds further confidence to the proposed fracture criteria.
A multiproduct pipeline provides an economic way to transport large volumes of refined petroleum products over long distances. In such a pipeline, different products are pumped back−to−back without any separation device between them. The sequence and lengths of such pumping runs must be carefully selected in order to meet market demands while minimizing pipeline operational costs and satisfying several constraints. The production planning and scheduling of the products at the refinery must also be synchronized with the transportation in order to avoid the usage of the system at some peak−hour time intervals. In this paper, we propose a multi−period mixed integer nonlinear programming (MINLP) model for an optimal planning and scheduling of the production and transportation of multiple petroleum products from a refinery plant connected to several depots through a single pipeline system. The objective of this work is to generalize the mixed integer linear programming (MILP) formulation proposed by Cafaro and Cerdá (2004, Computers and Chemical Engineering) where only a single planning period was considered and the production planning and scheduling was not part of the decision process. Numerical examples show how the use of a single period model for a given time period may lead to infeasible solutions when it is used for the upcoming periods. These examples also show how integrating production planning with the transportation and the use of a multi−period model may result in a cost saving compared to using a single−period model for each period, independently.