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In this paper, we propose a new mathematical programming model for integrating production and procurement transport planning decisions in manufacturing systems in a unique optimization model. This problem was introduced conceptually and dubbed as MRP IV by Díaz-Madroñero et al. (2012) to extend the current MRP (material requirement planning) systems. This proposal simultaneously considers material, production resources capacities and procurement transport planning decisions with different shipping modes (such as full-truckload, less-than-truckload and milk-run) in the supply chain to avoid suboptimal results, which are usually generated due to sequential and independent plans. We considered an industrial automobile company to validate the proposed model using real world data. The results obtained by the MRP IV proposed model, in terms of total planning costs and transport efficiency indicators, are better than those obtained in the current heuristic procedures followed in the company under study. [ABSTRACT FROM AUTHOR]

Abstract: Since the observed values of security returns in real-world problems are sometimes imprecise or vague, an increasing effort in research is devoted to study the properties of risk measures in fuzzy portfolio optimization problems. In this paper, a new risk measure is suggested to gauge the risk resulted from fuzzy uncertainty. For this purpose, the absolute deviation and absolute semi-deviation are first defined for fuzzy variable by nonlinear fuzzy integrals. To compute effectively the absolute semi-deviations of single fuzzy variable as well as its functions, this paper discusses the methods of computing the absolute semi-deviation by classical Lebesgue–Stieltjes (L–S) integral. After that, several useful absolute deviation and absolute semi-deviation formulas are established for common triangular, trapezoidal and normal fuzzy variables. Applying the absolute semi-deviation as a new risk measure in portfolio optimization, three classes of fuzzy portfolio optimization models are developed by combining the absolute semi-deviation with expected value operator and credibility measure. Based on the analytical representation of absolute semi-deviations, the established fuzzy portfolio selection models can be turned into their equivalent piecewise linear or fractional programming problems. Since the absolute semi-deviation is a piecewise fractional function and pseudo-convex on the feasible subregions of deterministic programming models, we take advantage of the structural characteristics to design a domain decomposition method to separate a deterministic programming problem into three convex subproblems, which can be solved by conventional solution methods or general-purpose software. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the effectiveness of the solution method. [Copyright &y& Elsevier]

This paper presents a mathematical model for the design of a non-cooperative supply chain where transportation activities are provided by logistics companies. We consider a four-layer network comprised of manufacturers, retailers, customers, and logistics service providers (LSPs). In this problem, manufacturers, retailers, and LSPs do not collaborate or engage in any type of bargaining strategy among one other. Instead, they compete to supply products to customers at demand markets, while each agent seeks to maximize his own profit. LSPs compete among themselves to provide logistics (transportation/warehousing) services to manufacturers. It is considered that manufacturers, LSPs, and retailers collaborate to maximize services. Normally this problem cannot be modeled as an optimization problem, so we use a variational inequality approach to formulate it. To the best of our knowledge, this important problem, which helps evaluate options and make decisions when logistics activities are provided by LSPs, has not been modeled in the literature and this constitutes our main contribution. The model determines the optimal level of production for each manufacturer, the flow of products between manufacturers and retailers, the flow of products to be handled by each logistics service provider and the flow of products between retailers and demand markets in a non-cooperative environment. Numerous experiments are presented and adapted from test examples that are available in the literature, while results and important findings are discussed. [ABSTRACT FROM AUTHOR]

Robust optimization has attracted great attention in recent decade owing to its ability to cope with risk and uncertainty in real-life optimization problems. Recently, the scope of robust optimization is extended into fuzzy mathematical programming by proposing robust possibilistic programming approach. As a complementary for robust possibilistic programming approach, this paper proposes different novel robust flexible programming and robust mixed possibilistic-flexible programming models. The advantages and strengths of the proposed models are described and a real-life problem is employed to show the efficiency and practicability of the propounded models against the traditional fuzzy programming approach. [ABSTRACT FROM AUTHOR]

This study describes a single phase algorithm for the fixed destination multi-depot multiple traveling salesman problem with multiple tours (mdmTSP). This problem widely appears in the field of logistics mostly in connection with maintenance networks. The general model of the technical inspection and maintenance systems is shown in the first part, where the solution of this problem is an important question. A mathematical model of the system’s object expert assignment is proposed with the constraints typical of the system, like experts’ capacity minimum and maximum and constraints on maximum and daily tours of the experts. In the second part, the developed evolutionary programming algorithm is described which solves the assignment, regarding the constraints introducing penalty functions in the algorithm. In the last part of the paper, the convergence of the algorithm and the run times and some examination of the parallelization are presented. [ABSTRACT FROM AUTHOR]

Abstract: This paper employs uncertain programming to deal with uncertain optimal assignment problem in which profit is uncertain. Within the framework of uncertain programming, it gives the uncertainty distribution of the optimal assignment profit, and the concept of α-optimal assignment for uncertain optimal assignment problem is proposed. Then α-optimal model is also constructed. Taking advantage of properties of uncertainty theory, α-optimal model can be transformed into a corresponding deterministic form, which can be solved by Kuhn–Munkres algorithm. [Copyright &y& Elsevier]

Abstract: This paper deals with a minimum spanning tree problem where each edge cost includes uncertainty and importance measure. In risk management to avoid adverse impacts derived from uncertainty, a d-confidence interval for the total cost derived from robustness is introduced. Then, by maximizing the considerable region as well as minimizing the cost-importance ratio, a biobjective minimum spanning tree problem is proposed. Furthermore, in order to satisfy the objects of the decision maker and to solve the proposed model in mathematical programming, fuzzy goals for the objects are introduced as satisfaction functions, and an exact solution algorithm is developed using interactive decision making and deterministic equivalent transformations. Numerical examples are provided to compare our proposed model with some previous models. [Copyright &y& Elsevier]

Abstract: This paper proposes a bi-objective model for designing a reliable network of bi-directional facilities in logistics network under uncertainties. For this purpose, the model utilizes an effective reliability approach to find a robust logistics network design. The objectives of the model are to minimize the total costs and the expected transportation costs after failures of bi-directional facilities of the logistics network. To solve the model, a new solution approach is proposed by combining queuing theory, fuzzy possibilistic programming and fuzzy multi-objective programming. Finally, the computational experiments are provided to illustrate the effectiveness of the proposed model and solution approach. [Copyright &y& Elsevier]

Abstract: With the rapid development in computer technologies, mathematical programming-based technique to solve scheduling problems is significantly receiving attention from researchers. Although, it is not efficient solution method due to the NP-hard structure of these problems, mathematical programming formulation is the first step to develop an effective heuristic. Numerous comparative studies for variety scheduling problems have appeared over the years. But in our search in literature there is not an entirely review for mathematical formulations of flexible job shop scheduling problems (FJSP). In this paper, four the most widely used formulations of the FJSP are compiled from literature and a time-indexed model for FJSP is proposed. These formulations are evaluated under three categories that are distinguished by the type of binary variable that they rely on for using of sequencing operations on machines. All five formulations compared and results are presented. [Copyright &y& Elsevier]

Abstract: Goal programming as a well known technique has been widely used for solving multi objective decision making problems. However, in some practical cases, there may exist situations where the decision maker is interested in setting multi aspiration levels for objectives that may not be expressed in a precise manner. In this paper, a novel formulation of fuzzy multi-choice goal programming (FMCGP) is presented. The proposed approach not only improves the applicability of goal programming in real world situations but also provides useful insight about the solution of a new class of problems. To illustrate and clarify the proposed approach, a numerical example is presented. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to point out that the programming model proposed by Eren [A bicriteria m-machine flowshop scheduling with sequence-dependent setup times, Appl. Math. Model. 34 (2010) 284–293] is incorrect. We propose a mixed 0–1 programming model for the same scheduling problem based on their model. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to point out that the integer programming model proposed by Eren and Güner [T. Eren, E. Güner, A bicriteria flowshop scheduling with a learning effect, Applied Mathematical Modelling 32 (2008) 1719–1733] is incorrect. We propose a mixed 0–1 programming model for the same scheduling problem based on their model. [Copyright &y& Elsevier]

Abstract: Data envelopment analysis (DEA) and multiple objective linear programming (MOLP) can be used as tools in management control and planning. The existing models have been established during the investigation of the relations between the output-oriented dual DEA model and the minimax reference point formulations, namely the super-ideal point model, the ideal point model and the shortest distance model. Through these models, the decision makers’ preferences are considered by interactive trade-off analysis procedures in multiple objective linear programming. These models only consider the output-oriented dual DEA model, which is a radial model that focuses more on output increase. In this paper, we improve those models to obtain models that address both inputs and outputs. Our main aim is to decrease total input consumption and increase total output production which results in solving one mathematical programming model instead of n models. Numerical illustration is provided to show some advantages of our method over the previous methods. [ABSTRACT FROM AUTHOR]

Abstract: Environment issues are more than ever important in a modern society. Complying with stricter legal thresholds on pollution emissions raises an important economic issue. This paper presents some ideas in the use of optimization tools to help in the planning and control of stationary pollution sources. Three main semi-infinite programming approaches are described. The first consists in optimizing an objective function while the pollution level in a given region is kept bellow a given threshold. In the second approach the maximum pollution level in a given region is computed and in the third an air pollution abatement problem is considered. These formulations allow to obtain the best control parameters and the maxima pollution positions, where the sampling stations should be placed. A specific modeling language was used to code four academic problems. Numerical results computed with a semi-infinite programming solver are shown. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to point out some errors in Eren [Applied Mathematical Modelling, 33 (2009) 1141–1150]. We also suggest some methods to fix them. [Copyright &y& Elsevier]