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Thus far in the available literature, capability-based distributed layout (CBDL) design approaches were only developed under certain environments. Indeed, uncertainties embedded in the machine unavailability (or random machine breakdowns), product demands, and process flow data were not considered by the previous studies to achieve a robust CBDL design. However, many real-life facility layout design applications may involve different types of uncertainties simultaneously, like fuzziness and stochasticity. Based on this motivation, for the first time in the literature, this paper introduces a novel robust capability-based distributed layout (R-CBDL) design problem under a mixed fuzzy-stochastic environment. First, a new fuzzy-stochastic optimization model of the R-CBDL design problem is developed by considering the random machine breakdowns and fuzzy demand/process flow data. Then, a hybrid solution approach based on a chance-constrained stochastic programming technique with a well-known interactive fuzzy resolution method is proposed. Thus, the random machine breakdowns and fuzzy part flow rates among different machining capabilities could be easily handled via the proposed approach. Fortunately, the proposed approach can also generate various risky and risk-free robust layout design alternatives under different probabilistic scenarios and uncertainty levels (α-cuts) according to the facility designer's risk attitude. To show the validity and applicability of the proposed R-CBDL problem and hybrid solution approach, an extensive computational study with comparative analysis is first presented based on an illustrative numerical example under different machine capability overlap cases and probability distributions. Then, the performance of the proposed approach is also tested on a real-life cellular manufacturing system of a company. The computational experiments have shown that the proposed approach can accomplish more efficient robust layout design alternatives with on average 24.5% better total expected layout score when compared to the existing cellular layout of the manufacturing company. [ABSTRACT FROM AUTHOR]

Fleet planning problems in intermodal transportation represent one of the most important problems in logistics. In addition to the integration of various sub-problems interrelated at different planning levels, i.e., strategic, tactical, and operational, the inclusion of multiple modes, resources, and multiple actors in intermodal transportation systems makes fleet planning more complex in comparison with unimodal systems. The present paper proposes a holistic fleet planning approach, which integrates various decisions such as freight planning (transport mode, service type and route selection), fleet composition and allocation, empty vehicle repositions, fleet expansion/reduction and outsourcing. A comprehensive mixed-integer linear programming (MILP) model is developed for an international logistics company, which operates a large intermodal network in Europe. The computational results of the case study have shown that application of a reallocation strategy may provide cost reduction up to 10% for the logistics company. Considering the existing situation of the company, outsourcing can be reduced up to 30%, and fleet utilization can be increased by 5% by applying the proposed model. The extensive case study also shows that effective and efficient fleet plans can be generated through the proposed MILP model. [ABSTRACT FROM AUTHOR]

In fuzzy mathematical programming literature, most of the transformation approaches were mainly focused on integer linear programs (ILPs) with fuzzy parameters/variables. However, ILP-based solution approaches may be inadequate for solving large-scaled combinatorial fuzzy optimization problems, like project scheduling under mixed fuzzy-stochastic environments. Moreover, many real-life project scheduling applications may contain different types of uncertainties such as fuzziness, stochasticity, and dynamism simultaneously. Based on these motivations, this paper presents a novel constraint programming (CP)-based transformation approach for solving a multi-objective and multi-mode, fuzzy-stochastic resource investment project scheduling problem (FS-MRIPSP) which is a well-known NP-complete problem. In fact, the proposed approach mainly depends on a bound and decomposition principle which divides fuzzy components of the problem into the crisp middle, lower, and upper level problems. Thus, it reduces the problem dimension and does not need to use any standard fuzzy arithmetic and ranking operations directly. Furthermore, the stochastic nature of the problem is also taken into account by using a multi-scenario-based stochastic programming technique. Finally, a weighted additive fuzzy goal program is embedded into the proposed CP-based transformation approach to produce compromise fuzzy project schedules that trade-off between expected values of project makespan and total resource usage costs. To show the validity and practicality of the proposed approach, a real-life application is presented for the production-and-operations management module implementation process of an international Enterprise Resource Planning software company. The fuzzy-stochastic project schedules generated by the proposed CP-based approach are also compared to the results of a similar ILP-based method. Computational results have shown that the CP-based approach outperforms the ILP-based method in terms of both solution quality and computational time. [ABSTRACT FROM AUTHOR]

This paper presents a novel direct solution approach for fully fuzzy transportation problems in which all of the model parameters as well as decision variables are considered as fuzzy numbers. In detail, a fuzzy decoding procedure based on constrained fuzzy arithmetic operations and a fuzzy ranking technique is first introduced for solution of the problem directly without any fuzzy to crisp transformation process. Then, this decoding procedure is embedded into a metaheuristic, namely priority-based PSO algorithm for generating new solution vectors and seeking for better fuzzy acceptable solutions. By making use of the constrained fuzzy arithmetic concept, the proposed approach is also able to handle the decision maker's attitude toward risk. In order to show validity and applicability of the proposed approach, numerical examples on both balanced and unbalanced fully fuzzy transportation cases are generated and solved. The computational results have shown that relatively more precise and information efficient solutions can be obtained from the proposed approach for "risk-averse" and "partially risk-averse" decision makers. Furthermore, the proposed approach is also able to produce fuzzy solutions for "risk seekers" with high degree of uncertainty similar to the other methods available in the literature. [ABSTRACT FROM AUTHOR]

Recent years, there has been a growing interest in reverse logistics, recycling, remanufacturing, and reusing due to the environmental concerns, economical issues, and legal obligations. Companies should take into account the recovery options such as recycling, remanufacturing, etc. while preparing their tactical plans. In this paper, a mixed integer linear programming model is proposed for tactical planning in a conceptual closed-loop supply chain with remanufacturing option. In the model, both forward and reverse flows are involved and two production alternatives are considered: either production of new products directly in manufacturing plants or bringing the returned products back to 'as new condition' in the remanufacturing facilities. The proposed model attempts to optimize all of the consecutive stages in the closed-loop supply chain. Hence, the purpose of this research is to formulate a mathematical programming model to focus primarily on integrating remanufacturing as a recovery option into tactical planning process. The proposed model is applied to an illustrative case and solved by LINGO 9.0 optimization solver. In order to obtain the best objective function value that is targeted, the effects of the major factors regarding reverse flows and remanufacturing system are examined with the help of Taguchi experimental design technique at the end of the study. Analysis of variance, Taguchi's signal/noise ratios, analysis of means graphs and interaction graphs are provided by MINITAB 14 software and interpreted for the evaluation of experimental results and effects of related factors. [ABSTRACT FROM AUTHOR]

Nowadays, so as to adapt to the global market, where competition is getting tougher, firms producing through the modern production approach need to bring not the only performance of the system designed both during the research and development phase and the production phase but also the performance of the product to be developed as well as the process to be improved to the highest level. The Taguchi method is an experimental design technique seeking to minimize the effect of uncontrollable factors, using orthogonal arrays. It can also be designed as a set of plans showing the way data are collected through experiments. Experiments are carried out using factors defined at different levels and a solution model generated in ARENA 3.0 program using SIMAN, which is a simulation language. Many experimental investigations reveal that the speed and capacity of automated-guided vehicle, the capacities of local depots, and the mean time between shipping from the main depot are the major influential parameters that affect the performance criteria of the storage system. For the evaluation of experiment results and effects of related factors, variance analysis and signal/noise ratio are used and the experiments are carried out in MINITAB15 according to Taguchi L16 scheme. The purpose of this study is to prove that experimental design is an utilizable method not only for product development and process improvement but it can also be used effectively in the design of material handling-transfer systems and performance optimization of automation technologies, which are to be integrated to the firms. [ABSTRACT FROM AUTHOR]