An integrated supplier selection and inventory problem with multi-sourcing and lateral transshipments

In this research, we consider the supplier selection problem of a firm offering a single product via multiple warehouses. The warehouses face stationary, stochastic demand and replenish their inventory via multiple suppliers, to be determined from a set of candidates, with varying price, capacity, quality, and disruption characteristics. Additionally, the warehouses may simultaneously replenish their inventory from other warehouses proactively. With these characteristics, the problem is a multi-sourcing, supplier selection, and inventory problem with lateral transshipments. Even though the benefits of multi-sourcing and lateral transshipments have been presented in the literature individually to mitigate risks associated with uncertain demand and disrupted supply, the intertwined sourcing and inventory decisions under these settings have not been investigated from a quantitative perspective. We develop a decomposition based heuristic algorithm, powered with simulation. While the decomposition based heuristic determines a solution with supplier selection and inventory decisions, the simulation model evaluates the objective function value corresponding to each generated solution. Experimental results show, contrary to the existing literature, inferior decisions may result when considering the selection of suppliers solely on unit and/or contractual costs. We also evaluate the impact of multi-sourcing with rare but long disruptions compared to frequent but short ones.     

An interactive three-stage model for mutual funds portfolio selection

The aim of this work is to be a useful instrument for helping finance practitioners on the selection of suitable mutual fund portfolios. The portfolio selection problem is characterized by imprecision and/or vagueness inherent in the required data and more generally, in the context where investors have to make decisions. In order to mitigate these problems, a three stage model has been proposed based on a multi-index model and considering several market scenarios described in an imprecise way by an expert. The proposed fuzzy model allows the Decision Maker to select, by means of an outranking method, a suitable portfolio taking into account the uncertainty related to the market scenarios and the imprecision and/or vagueness associated with the model data.     

An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) is proposed. This integrated approach considers both tangible and intangible factors in choosing the best suppliers and defines the optimum quantities among selected suppliers to maximize the total value of purchasing (TVP), and to minimize the total cost and total defect rate and to balance the total cost among periods. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). The priorities of suppliers will also be used as the parameters of the first objective function. This multi-objective and multi-period real-life problem is solved by using previous techniques and a reservation level driven Tchebycheff procedure (RLTP). Finally the most preferred nondominated solutions are determined by considering the decision maker's (DM's) preferences and the results obtained by these techniques are compared.     

The supplier selection problem with quantity discounts and truckload shipping

To minimize procurement expenditures both purchasing and transportation costs need to be considered. We study a procurement setting in which a company needs to purchase a number of products from a set of suppliers to satisfy customer demand. The suppliers offer total quantity discounts and transportation costs are based on truckload shipping rates. The goal is to select a set of suppliers so as to satisfy product demand at minimal total costs. The resulting optimization problem is strongly NP-hard. We develop integer programming based heuristics to solve the problem. Extensive computational experiments demonstrate the efficacy of the proposed heuristics and provide insight into the impact of instance characteristics on effective procurement strategies.     

An integrated multiobjective decision making process for supplier selection and order allocation

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) is proposed to consider both tangible and intangible factors in choosing the best suppliers and define the optimum quantities among selected suppliers to maximize the total value of purchasing and minimize the budget and defect rate. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). Also the priorities of suppliers will be used as the parameters of the first objective function. This multi-objective real-life problem was solved by using ε$\epsilon$-constraint method and a reservation level driven Tchebycheff procedure. Finally, the most preferred nondominated solutions were determined by considering decision maker's (DM) preferences and the results obtained by these techniques are compared.     

An economic model for the quality selection problem

The object of an economic model for the quality selection problem is to select the best parameter value for an input quality characteristic (X) so that the quality loss incurred on an output quality characteristic (Y) is minimized. The relation function between Y and X is assumed to be known throughout the article. In the work of Taguchi's experimentations, the selection of best parameter values is solved by two-step optimization when Y is adjustable. In the article, it is further extended to the case where Y is non-adjustable. An economic quality selection model with a general relation function is proposed based on a Taylor-series method for both adjustable and non-adjustable cases. A special case of a quality selection model with a quadratic relation function is also studied. A circuit example provided by Taguchi is presented to illustrate the use of this model.     

双积分政策下传统能源车企的策略选择研究

双积分政策的实施迫使传统能源车企必须考虑采用何种策略调整运营决策,然而现有研究无法为其提供策略选择指导。本文在双积分政策背景下分别构建传统能源车企采用购买、关联和投产策略下的最优决策模型,通过不同模型的解析对比和数值案例分析,研究该政策对传统能源车企的影响并寻求为其提供策略选择指导。研究结果表明双积分政策的实施会减少传统能源汽车的产量并降低传统能源车企的利润;传统能源车企应关注积分价格和新能源汽车积分比例要求;若与新能源车企达成较低的积分内部协议价,则传统能源车企采用关联策略最优;若协议价较高,当积分市场价较低时采用购买策略最优,否则在关联企业积分盈余情况下采用关联策略最优;若考虑投产策略,当积分市场价格较高时可以获利更多,而当积分市场价格较低时采用购买策略更好。     

An integrated hybrid approach to the examination timetabling problem

This paper is derived from an interest in the development of automated approaches to tackle examination timetabling problems effectively. We propose a hybrid approach that incorporates effective heuristic operators within the great deluge algorithm. The latter was chosen because of consistently good performances being reported within the examination timetabling research domain. The additional heuristic procedures further enhance the overall effectiveness of this integrated hybrid approach. These procedures are drawn from methodologies that have appeared in the literature under term the “electromagnetic-like mechanism”. The aim is to move sample points towards a high quality solution while avoiding local optima by utilising a calculated force value. This value, which is calculated dynamically, is treated as a decay rate in determining the level within the great deluge algorithm. To evaluate the proposed algorithm, we carry out experimental work on two types of examination timetabling datasets. All the related results and analysis obtained illustrate that this hybrid approach is effective when compared with existing approaches in the literature.     

Supply chain configuration for diffusion of new products: An integrated optimization approach

We develop an integrated/hybrid optimization model for configuring new products’ supply chains while explicitly considering the impact of demand dynamics during new products’ diffusion. The hybrid model simultaneously determines optimal production/sales plan and supply chain configuration. The production and sales plan provides decisions on the optimal timing to launch a new product, as well as the production and sales quantity in each planning period. The supply chain configuration provides optimal selection of options and safety stock level kept at each supply chain function. Extensive computational experiments on randomly generated testbed problems indicate that the hybrid modeling and solution approach significantly outperforms non-hybrid alternative modeling and solution approaches under various diffusion and supply chain topologies. We provide insights on optimal production/sales plan and supply chain configuration for new products during their diffusion process. Also, managerial implications relevant to effectiveness of the hybrid approach are discussed.     

An integrated strategy for a production planning and warehouse layout problem: Modeling and solution approaches

We study a real-world production warehousing case, where the company always faces the challenge to find available space for its products and to manage the items in the warehouse. To resolve the problem, an integrated strategy that combines warehouse layout with the capacitated lot-sizing problem is presented, which have been traditionally treated separately in the existing literature. We develop a mixed integer linear programming model to formulate the integrated optimization problem with the objective of minimizing the total cost of production and warehouse operations. The problem with real data is a large-scale instance that is beyond the capability of optimization solvers. A novel Lagrangian relax-and-fix heuristic approach and its variants are proposed to solve the large-scale problem. The preliminary numerical results from the heuristic approaches are reported.     

An integrated approach for water resources decision making under interactive and compound uncertainties

In recent years, the issue of water allocation among competing users has been of great concern for many countries due to increasing water demand from population growth and economic development. In water management systems, the inherent uncertainties and their potential interactions pose a significant challenge for water managers to identify optimal water-allocation schemes in a complex and uncertain environment. This paper thus proposes a methodology that incorporates optimization techniques and statistical experimental designs within a general framework to address the issues of uncertainty and risk as well as their correlations in a systematic manner. A water resources management problem is used to demonstrate the applicability of the proposed methodology. The results indicate that interval solutions can be generated for the objective function and decision variables, and a number of decision alternatives can be obtained under different policy scenarios. The solutions with different risk levels of constraint violation can help quantify the relationship between the economic objective and the system risk, which is meaningful for supporting risk management. The experimental data obtained from the Taguchi's orthogonal array design are useful for identifying the significant factors affecting the means of total net benefits. Then the findings from the mixed-level factorial experiment can help reveal the latent interactions between those significant factors at different levels and their effects on the modeling response.     

Column generation approach for the point-feature cartographic label placement problem

This paper proposes a column generation approach for the Point-Feature Cartographic Label Placement problem (PFCLP). The column generation is based on a Lagrangean relaxation with clusters proposed for problems modeled by conflict graphs. The PFCLP can be represented by a conflict graph where vertices are positions for each label and edges are potential overlaps between labels (vertices). The conflict graph is decomposed into clusters forming a block diagonal matrix with coupling constraints that is known as a restricted master problem (RMP) in a Dantzig-Wolfe decomposition context. The clusters’ sub-problems are similar to the PFCLP and are used to generate new improved columns to RMP. This approach was tested on PFCLP instances presented in the literature providing in reasonable times better solutions than all those known and determining optimal solutions for some difficult large-scale instances.     

A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem

In this paper, multiple criteria sorting methods based on data envelopment analysis (DEA) are developed to evaluate research and development (R&D) projects. The weight intervals of the criteria are obtained from Interval Analytic Hierarchy Process and employed as the assurance region constraints of models. Based on data envelopment analysis, two threshold estimation models, and five assignment models are developed for sorting. In addition to sorting, these models also provide ranking of the projects. The developed approach and the well-known sorting method UTADIS are applied to a real case study to analyze the R&D projects proposed to a grant program executed by a government funding agency in 2009. A five level R&D project selection criteria hierarchy and an assisting point allocation guide are defined to measure and quantify the performance of the projects. In the case study, the developed methods are observed to be more stable than UTADIS.     

确定多属性群决策协调权的模型和方法

在权重信息不完全的多属性群决策过程中,当决策者给出了各自的关于方案的偏好序关系之后,需要检验是否存在一组能支持所有决策者意见的权重(即协调权).在定义偏好关系"重要度"的基础上,构造了一个{0,1}混合整数线性规划模型,该模型不仅能够判断协调权是否存在,而且可以识别出导致协调权不存在的"最不重要"的偏好序关系.此外还证明当决策者修改这些序关系后,群决策问题一定存在协调权.最后用一个例子说明了该模型的有效性和实用性.     

A robust branch-and-cut approach for the minimum-energy symmetric network connectivity problem

This paper considers the minimum-energy symmetric network connectivity problem (MESNC) in wireless sensor networks. The aim of the MESNC is to assign transmission power to each sensor node such that the resulting network, using only bidirectional links, is connected and the total energy consumption is minimized. We first present two new models of this problem and then propose new branch-and-cut algorithms. Based on an existing formulation, we present the first model by introducing additional constraints. These additional constraints allow us to relax certain binary variables to continuous ones and thus to reduce significantly the number of binary variables. Our second model strengthens the first one by adding an exponential number of lifted directed-connectivity constraints. We present two branch-and-cut procedures based on these proposed improvements. The computational results are reported and show that our approaches, using the proposed formulations, can efficiently solve instances with up to 120 nodes, which significantly improve our ability to solve much larger instances in comparison with other exact algorithms in the literature.     

An improved MIP heuristic for the intermodal hub location problem

Optimization methods have been commonly developed for the intermodal hub location problem because it has a broad range of practical applications. These methods include exact methods (limited on solving large-size problems) and heuristics (no guarantee on solution quality). In order to avoid their weakness but to leverage their strength, we develop an improved MIP heuristic combining branch-and-bound, Lagrangian relaxation, and linear programming relaxation. In the heuristic, we generate a population of initial feasible solutions using the branch-and-bound and Lagrangian relaxation methods and create a linear-relaxed solution using the linear programming relaxation method. We combine these feasible and linear-relaxed solutions to fix a portion of hub location variables so as to create a number of restricted hub location subproblems. We then combine the branch-and-bound method to solve these restricted subproblems for iteratively improving solution quality. We discuss in detail the application of the method to the intermodal hub location problem. The discussion is followed by extensive statistical analysis and computational tests, where the analysis shows statistical significance of solutions for guiding the heuristic search and comparisons with other methods indicate that the proposed approach is computationally tractable and is able to obtain competitive results.     

An application of the integrated AHP-PGP model for performance measurement of supply chain management

The performance measurement problem has gained great attention in business and operations management literature. The first objective of this study is to determine the required performance measures and to develop a model for performance evaluation, based on these selected measures using analytical hierarchy process (AHP) methodology. The second objective is to explain how an integrated AHP-PGP (pre-emptive goal programming) model can be used in performance measurement while optimising the overall performance. An integration of AHP and multi-objective PGP is used to consider both quantitative and qualitative performance measures in optimising the overall performance of the system. The integrated model is presented with a real-world application using source data provided by a survey conducted in India. Findings demonstrate that the integrated AHP-PGP model can be useful to all supply chain industries in their day-to-day performance measurement decisions.     

Linear solution schemes for Mean-SemiVariance Project portfolio selection problems: An application in the oil and gas industry

We study the Mean-SemiVariance Project (MSVP) portfolio selection problem, where the objective is to obtain the optimal risk-reward portfolio of non-divisible projects when the risk is measured by the semivariance of the portfolio׳s Net-Present Value (NPV) and the reward is measured by the portfolio׳s expected NPV. Similar to the well-known Mean-Variance portfolio selection problem, when integer variables are present (e.g., due to transaction costs, cardinality constraints, or asset illiquidity), the MSVP problem can be solved using Mixed-Integer Quadratic Programming (MIQP) techniques. However, conventional MIQP solvers may be unable to solve large-scale MSVP problem instances in a reasonable amount of time. In this paper, we propose two linear solution schemes to solve the MSVP problem; that is, the proposed schemes avoid the use of MIQP solvers and only require the use of Mixed-Integer Linear Programming (MILP) techniques. In particular, we show that the solution of a class of real-world MSVP problems, in which project returns are positively correlated, can be accurately approximated by solving a single MILP problem. In general, we show that the MSVP problem can be effectively solved by a sequence of MILP problems, which allow us to solve large-scale MSVP problem instances faster than using MIQP solvers. We illustrate our solution schemes by solving a real MSVP problem arising in a Latin American oil and gas company. Also, we solve instances of the MSVP problem that are constructed using data from the PSPLIB library of project scheduling problems.     

An integrated lean and green approach for improving sustainability performance: a case study of a packaging manufacturing SME in the U.K.

Abstract

Competitive landscape, informed consumers and stringent regulations have forced many manufacturing small- and medium-sized enterprises (SMEs) to focus on operational efficiency along with sustainability issues in recent years. While many manufacturing organisations have been taking lean initiatives for the past few years for operational excellence, an impulsive rush to adopt lean without a strategic deployment vision has led to scattered implementation of lean tools and projects without desired success. Many researchers and practitioners prescribe value stream mapping as a foundation for lean transformation initiatives; however, little empirical work is available on the symbiosis of lean and green paradigms to reap maximum benefits. This research, through a systematic methodology and a novel tool called Green Integrated Value Stream Mapping (GIVSM), integrates both paradigms in a case study on a U.K. packaging-manufacturing SME. Applying the GIVSM demonstrates that simultaneous deployment of lean and green paradigms have synergistic effect for improving both operational efficiency and environmental performance. In addition, continuous improvement framework with sustainable procurement is proposed to overcome the lean-green misalignments. This study also provides a guiding reference for practitioners to undertake similar improvement projects and identifies opportunities to expand this academic research on integrated lean-green approach into other industry sectors.