JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH IN PRODUCTION SYSTEMS (IERPS)
Year:2016 | Volume:4 | Issue:9 (SPECIAL ISSUE) |Papers Count:8

In this paper, a new mathematical model for vehicle routing problem is presented. The objectives are to minimize the energy consumption and the travel times in which speeds varied in different hours of the day. Since the vehicle routing problem belongs to the category of NP-hard problems, to solve the problem, a method based on the imperialist competitive algorithm (ICA) is proposed. Finally, the associated results are compared with the results obtained by particle swarm optimization (PSO) on the well-known benchmark problems.

This paper develops a 0-1 integer-programming model for multi-model assembly line balancing and equipment selection problem. In the investigated model, parallel stations are allowed under zoning constraints. Under zoning constraints, assignment of different tasks to the same workstation may be forced (positive zoning constraint) or may be forbidden (negative zoning constraint). There are two objectives for the investigated problem: (1) Optimizing the number of workstations over the assembly line and (2) Minimizing the total equipment costs. An augmented ε-constraint method is proposed to solve the investigated problem. To illustrate effectiveness of the proposed model, a numerical example is conducted. Results show the performance of the proposed model.

Reducing production costs has become one of the most important concerns, due to the economic development and increasing competitiveness in industries. To achieve this goal, considering real conditions is important. In this presentation, we investigate the production scheduling of a Pickling Line in Esfahan's Mobarakeh Steel Company called Pickling Line Scheduling (PLS). The problem is to generate multiple production turns for the Pickling Line coils and at the same time determine the sequence of these turns and select coils then the sequence coils of these turn so that the productivity and product quality both maximized while the production cost minimized. We formulate this problem as a mixed integer nonlinear program and propose a heuristic algorithm to obtain satisfactory solutions. Results on real production instances show heuristic algorithm is more effective and efficient with comparison to manual scheduling in Esfahan's Mobarakeh Steel Company.

This paper studies multi-stage, multi-product, multi-period production planning problem with sequence dependent setups in closed-loop supply chain. Manufacturing and remanufacturing processes of each product are regarded consequently, and both of them could be performed if machine is ready for processing corresponding product.To formulate the problem, a mixed-integer programming (MIP) model is presented and four heuristic algorithms using rolling horizon and a genetic algorithm are developed to solve the model. First two heuristic algorithms are developed based on the original model, but to solve the large instances the other two heuristics and the genetic algorithm are based on the simplified model, which is obtained by elimination of non-permutation sequences of original model solution space. To calibrate the parameters of the proposed genetic algorithm, Taguchi method is applied. The numerical results indicate the efficiency of the proposed meta-heuristic algorithm against MIP-based heuristic algorithms.

Expansive economic, production and commercial development today has made businesses and enterprises to have active presence and contribution in network-based and chain economy. Although focusing on this approach has brought some privileges and productivities for the firms which are members of networks and supply chains, it has been accompanied by various problems and complexities for financial crisis management, cash flow and flow capital amount of the foundations and their commercial partners. To overcome such problem, besides developing the concepts of supply chain management and logistics, Financial Supply Chain Management (FSCM) was also promoted to minimize inefficiencies of financial flow in supply chain using financial, engineering and management approaches, and to provide an effective management ground for cash inventory and flow capital all throughout the supply chain by making use of various tools and techniques of supply chain finance and/or SCF.Thus, regarding the significance of financial flow management in the chain and benefitting the principles and fundamentals of research by fact finding and case study, it was tried to present a framework to make use of financial flow management and its principles and fundamentals effectively by reviewing evidences as well as applicable experiences in this area. The present paper emphasized that as logistic services providers are responsible in supply chains to improve inventory level; financial services providers also have the same role concerning liquidity level in the chains.

In this study we consider the production environment of re-entrant flow-shop (RFS) with the objective of minimizing make span of the jobs. In a RFS, at least one job should visit at least one of the machines more than once. In a nowait flow shop-scheduling problem, when the process of a specific job begins on the first machine, it should constantly be processed without waiting in the line of any machine until its processing is completed on the last one.Integration of the properties of both of these environments, which is applied in many industries such as robotic industries, is not investigated separately. In the paper, we present a simulated annealing (SA) and a genetic algorithm (GA) based on heuristics for the problem. First, we develop the mathematical model for the problem, and then we present the suggested algorithms. For small scale, results of GA and SA are compared to GAMS. For large-scale problems, results of GA and SA are compared to each other. Computational results show that both SA ad GA algorithms perform properly but totally, SA is likely to turn out well in finding better solutions especially in large-scale problems.

Parameter uncertainty is one of the most concerning issues in manufacturing systems. Information insufficiency and also flexibility in the customer needs are main reasons of the uncertainty. In this study a robust optimization approach has been implemented in order to cope with uncertainty in a cellular manufacturing system. The solution obtained using this robust model remains feasible even optimal in every uncertainty level. Moreover multi0functional machines’ reliability is considered in proposed mathematical model. Machine tool selection is done based on the machine reliability. Other features of proposed model are consideration of inter-intra cell formation, cells’reconfiguration and tools’ install-uninstallation costs. The proposed model is linearized and solved using the Gams optimization package. Based on the obtained results, machine loading volume impacts on the part process routing and also the machine intra-cell layout. Moreover, tool consumption cost is the most sensitive term to the model uncertainty.

Scheduling problems with tool changes considerations have been investigated extensively in the last two decades. Tool change activities applied in flexible maintenance activities where the tools may be changed at any time of their lifespan. Also, to determining the quality of the final product, considering the quality of the tools as a production condition items is inevitable. In this paper, the classical single machine scheduling problem with tool change is examined. In this problem, two sets of jobs, namely special jobs and normal jobs are considered. Special jobs must be processed during a certain time after the tool change. This problem has been studied in the literature. Mathematical programming models used to solve scheduling problems with small size and medium size. For scheduling problems with large size, six algorithms based on Bin Packing problem presented and focused on their performance. In this paper, we present a new algorithm for solving large size scheduling problems.The computational results show that the proposed algorithm performance at the half of instance problems is better than four algorithms of literature. In addition, in the other instance it is better than all developed literature algorithms.