A central problem of tool management in Versatile Multi-tool machining centres is to decide how to batch the parts to be produced and what tools to allocate to the machine in order to maximize utilization of these expensive machines. Various authors have proposed heuristics and/or mathematical models to minimize the batches of parts to be manufactured in a production period. There is no comprehensive study reported to compare the number of actual batches (stoppages) formed with and without processing time considerations. In this paper, the sequential deterministic heuristics (SDHs) are appropriately adapted to include processing time of operations in the formation of groups. The modified heuristics are more realistic in reducing machine stoppages due to tools. Some stochastic search techniques have also been adapted to compute the number of groups. The results are compared with those obtained from SDHs and standard search techniques. The results indicate that the adapted search techniques are powerful approaches for forming optimum number of batches of parts and tools.

In this study, a volume flexible inventory system for deteriorating items with stock & time dependent demand has been developed over a finite planning horizon. Shortages are permitted with partial backorder. Uncertainties are inherent in real inventory problems due to complexities of market situation. This uncertainty can be handled by the concept of randomness. As a result, backorder rate is taken as random and follows a probability distribution. All the costs are influenced by the learning effect. The optimal number of production cycles that minimize the total cost is considered. Numerical illustrations together with sensitivity analysis are given to elucidate the model. Furthermore, the numerical results of the finite planning horizon model have been plotted graphically.

Flexible job shop scheduling problem is a key factor of using efficiently in production systems. This paper attempts to simultaneously optimize three objectives including minimization of the make span, total workload and maximum workload of jobs. Since the multi objective flexible job shop scheduling problem is strongly NP-Hard, an integrated heuristic approach has been used to solve it. The proposed approach was based on a floating search procedure that has used some heuristic algorithms. Within floating search procedure utilize local heuristic algorithms; it makes the considered problem into two sections including assigning and sequencing sub problem. First of all search is done upon assignment space achieving an acceptable solution and then search would continue on sequencing space based on a heuristic algorithm. This paper has used a multi-objective approach for producing Pareto solution. Thus proposed approach was adapted on NSGA II algorithm and evaluated Pareto-archives. The elements and parameters of the proposed algorithms were adjusted upon preliminary experiments. Finally, computational results were used to analyze efficiency of the proposed algorithm and this results showed that the proposed algorithm capable to produce efficient solutions.

Today, supply chains have been widely welcomed by industry researchers and the results of applying it may increase throughput, reduce cost, increase speed to meet customers’ needs and create competitive opportunities. This paper identifies a scientific method, which operates efficiently and effectively manages supply chain operations. In this paper, a computer simulation model is analyzed for analyzing the supply chain and the results are examined, accordingly. Using Taguchi design of experiment and running the proposed model under L27 scenarios, a two-objective optimization was performed on the estimated response surfaces, leading to a 60% increase in productivity and 40% reduction in waiting time.

Demand uncertainty obliges all participants through a supply chain to make decisions under uncertainty. These decisions extend across price, investment, production, and inventory quantities. We take account of competition between two supply chains under demand uncertainty. These chains internally are involved in vertical pricing competition; however, they externally participate in horizontal pricing and service level competitions by offering a single-type product to the market. Since firms may have various attitudes against demand uncertainty and its related risks, different risk structures for competitive supply chains are considered. We assume that risk-averse firms are able to decrease demand uncertainty by information gathered from market research. For risk-averse participants in a chain, market research investment is an appropriate ground for vertical coordination, which diminishes risk through a supply chain. Optimal strategies based on game theory are obtained for different risk structures; furthermore, for each structure the effects of risk sensitivity as well as market research efficiency on these optimal strategies are investigated. Finally, we propose two scenarios for information sharing between risk-averse participants.

This paper presents an empirical investigation for line balancing in aluminum industry. Assembly line problems (ALB) are divided into three types, single-model assembly lines to handle (easy) problems to build a product that is designed to build one kind of product, multiple and mixed model to design for different kinds of products. This paper is an effort to describe comprehensively the solution to the problem for single-model assembly lines using some practical software package named FLB. The primary objective of aluminum assembly line balancing is optimal deployment activities subject to various limitations. The preliminary results of this paper indicate that the implementation of line balancing could reduce make span, significantly.

This study attempts to assess the moderating impact of recently introduced tourist relationship management (TRM) framework on service quality perception-tourist satisfaction-destination loyalty link. Tourist relationship management framework draws inspiration from customer relationship management (CRM) model with validated addition of dimensions compatible to tourism dynamics. The study, carried out in Santiniketan, India, confirmed moderating impact of dimensional performance of tourist relationship management on perceived tourism service quality-tourist satisfaction-destination loyalty link.

Research in the field of export marketing suggests that resources play an important role in providing necessary investments for development of businesses for exporting goods and services. The present study examines the factors influencing brand advantage for export ventures in small and medium firms (SMEs) located in city of Tehran, Iran. In this survey, we choose a sample of 256 managers of the SMEs randomly and distribute a questionnaire among them in Likert scale. Cronbach alpha has been calculated as 0.770, which is well above the minimum desirable level. Using principal component analysis, the study has detected six factors including organizational advantages, future studies, position of the firm in the market, product capability, the consumer & apos; s image and development tools influencing the most on export development. The implementation of structural equation modeling has also determined positive as well as negative impact of the factors on export ventures.

This paper presents an empirical investigation to identify and rank the factors of information market development systems influencing on the market share development. The population of this survey includes all managers who work for SMEs in city of Tehran, Iran. The study selects a sample of 230 people randomly and a questionnaire is distributed among them in Likert scale. Cronbach alpha has been calculated as 0.814, which is well above the minimum desirable level. Using structural equation modeling the study has determined seven factors including valid data, information, strategic information, organizational information, supportive information, customer information, development information and data analysis, which influence market share development.

This work deals with production scheduling problem in an assembly flow shop, having parts machining followed by their subsequent assembly operations. Limited heuristics available on the problem, are based on unrealistic assumption that every part is processed on all machines. In this paper, two heuristics NEH_BB and Disjunctive are proposed to solve assembly flow shop scheduling problem where every part may not be processed on each machine. Exhaustive computational experiments are conducted with 60 trials each. The methods are found to be applicable to large size problems. The objective functions used for comparison are makespan and computational time. Disjunctive method takes very less computational time as compared to NEH_BB and hence claimed to be the better among available approaches for finding solution in assembly flow shop problems.