Optimal apparel supplier selection with forecast updates under carbon emission taxation scheme

Carbon emission tax is an important measure for sustainable supply chain management. This paper studies an optimal supplier selection problem in the fashion apparel supply chain in the presence of carbon emission tax. We consider the scenario in which there are multiple suppliers in the market. In the basic model, each supplier offers a supply lead time and a wholesale pricing contract to the fashion retail buyer. For the fashion retail buyer, the supplier which offers a shorter lead time allows it to postpone the ordering decision with updated and better forecast, and also a smaller carbon tax. However, the wholesale price is usually larger. We propose a two-phase optimal supplier selection scheme in which phase one filters the inferior suppliers and phase two helps to select the best supplier among the set of non-inferior suppliers by multi-stage stochastic dynamic programming. The impacts brought by different formats of carbon emission tax are explored. Finally, we examine an extended model in which there is a local supplier who offers a buyback contract and accepts product returns. Insights from the analysis are discussed.     

A robust optimisation approach to the problem of supplier selection and allocation in outsourcing

We formulate the supplier selection and allocation problem in outsourcing under an uncertain environment as a stochastic programming problem. Both the decision-maker's attitude towards risk and the penalty parameters for demand deviation are considered in the objective function. A service level agreement, upper bound for each selected supplier's allocation and the number of selected suppliers are considered as constraints. A novel robust optimisation approach is employed to solve this problem under different economic situations. Illustrative examples are presented with managerial implications highlighted to support decision-making.     

动态规划法中返求最优可靠度分配的实用算法

本文简单介绍动态规划法应用,并着重对动态规划法中返求最优可靠度分配的算法进行研究,提出了一种比较简单实用的实现算法,该方法的优点在于：简单实用,易于编程和实现,解决参数化编程求解问题。     

Development and evaluation of the many-to-many supplier negotiation strategy

Supply chain is a complex logistics network, consists of suppliers, manufacturers, warehouses, distribution centers, retailers and customers. In general, the number of firms in each supply chain tier is more than one. Thus, the supply–demand relationship of two adjacent tiers in supply chain is the many-to-many type. Most of supplier selection processes are based on bidding and negotiation mechanism. Due to the diversity of intellectual backgrounds of the negotiating parties, competitive nature, many variables involved in supply–demand relationship, complex interactions and inadequate negotiation knowledge of project participants, the supplier negotiation process is sophisticated. This paper intends to develop the many-to-many supplier negotiation process, design the interactive and competitive bidding strategies of both-side parties, and conduct experimental simulations to investigate how the negotiation efficiency influenced by the varying bidding strategies of a considered demander. The purpose of this paper is to explore the relationships between a demander’s bidding strategies and negotiation efficiency under different order and competitive conditions. The explorative results will be the decision support for assisting a demander to determine the proper bidding strategy in many-to-many supplier negotiation process.     

A hybrid solution approach for fuzzy multiobjective dual supplier and material selection problem of carton box production systems

A novel optimization problem of carton box manufacturing industries is introduced in this paper. A mixed integer linear formulation with multiple objective functions is developed in order to determine the value of some criteria of carton raw sheets such as size, amount, and supplier under simultaneous minimization of multiple goals such as purchasing cost of raw sheets under discount policy, wastage remained from raw sheets, and quantity of surplus of carton boxes. In order to cope with the unstable market of this sector, some parameters of the proposed formulation such as demand value of the products and price given for raw sheets are assumed to be fuzzy numbers. To tackle such fuzzy multiobjective problem, first, the fuzzy problem is converted to a crisp form using the concepts of necessity‐based chance‐constrained modelling approach. Then a new hybrid form of the fuzzy programming approach is proposed to solve the obtained crisp multiobjective problem effectively. Computational experiments on a real case given by a carton box factory show the superior result of the proposed solution approach compared with the well‐known multiobjective solution methods taken from the literature.     

Constrained two-dimensional cutting stock problems a best-first branch-and-bound algorithm

In this paper, we develop a new version of the algorithm proposed in Hifi (Computers and Operations Research 24/8 (1997) 727–736) for solving exactly some variants of (un)weighted constrained two-dimensional cutting stock problems. Performance of branch-and-bound procedure depends highly on particular implementation of that algorithm. Programs of this kind are often accelerated drastically by employing sophisticated techniques. In the new version of the algorithm, we start by enhancing the initial lower bound to limit initially the space search. This initial lower bound has already been used in Fayard et al. 1998 (Journal of the Operational Research Society, 49, 1270–1277), as a heuristic for solving the constrained and unconstrained cutting stock problems. Also, we try to improve the upper bound at each internal node of the developed tree, by applying some simple and effcient combinations. Finally, we introduce some new symmetric-strategies used for neglecting some unnecessary duplicate patterns . The performance of our algorithm is evaluated on some problem instances of the literature and other hard-randomly generated problem instances.     

Stochastic iterative dynamic programming: a Monte Carlo approach to dual control

Practical exploitation of optimal dual control (ODC) theory continues to be hindered by the difficulties involved in numerically solving the associated stochastic dynamic programming (SDPs) problems. In particular, high-dimensional hyper-states coupled with the nesting of optimizations and integrations within these SDP problems render their exact numerical solution computationally prohibitive. This paper presents a new stochastic dynamic programming algorithm that uses a Monte Carlo approach to circumvent the need for numerical integration, thereby dramatically reducing computational requirements. Also, being a generalization of iterative dynamic programming (IDP) to the stochastic domain, the new algorithm exhibits reduced sensitivity to the hyper-state dimension and, consequently, is particularly well suited to solution of ODC problems. A convergence analysis of the new algorithm is provided, and its benefits are illustrated on the problem of ODC of an integrator with unknown gain, originally presented by Åström and Helmersson (Computers and Mathematics with Applications 12A (1986) 653-662).     

大数据理念下的供应商分析及应用研究

为了解决供应商关系管理中数据利用率不足的问题,充分提升数据价值的挖掘能力和应用效果,提升供应商管控水平,推进现代供应链的建设,本文基于HHM理论构建了供应商大数据分析模型,科学设计模型关键要素,采用“通用+专用”的方式选择评价指标,采用层次分析法确定指标权重,采用四分位法等多种科学评分方式获取供应商评价结果。供应商评价结果具有多种场景应用性,包括供应商分级管理、供应商分类管理、相关性分析、预警分析、产品行业分析等.相关场景的应用有利于释放大数据内在价值,解释供应商生产、技术等方面的能力和状态,从而更加全面、深入地掌握供应商实际情况,实现电网公司供应商关系管理水平的大幅提升,助力采购优质电力设备,为泛在电力物联网的建设提供重要支撑。     

Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study

Microgrids are subsystems of the distribution grid which comprises generation capacities, storage devices and flexible loads, operating as a single controllable system either connected or isolated from the utility grid. In this work, microgrid management system is developed in a stochastic framework. It is seen as a constraint-based system that employs forecasts and stochastic techniques to manage microgrid operations. Uncertainties due to fluctuating demand and generation from renewable energy sources are taken into account and a two-stage stochastic programming approach is applied to efficiently optimize microgrid operations while satisfying a time-varying request and operation constraints. At the first stage, before the realizations of the random variables are known, a decision on the microgrid operations has to be made. At the second stage, after random variables outcomes become known, correction actions must be taken, which have a cost. The proposed approach aims at minimizing the expected cost of correction actions. Mathematically, the stochastic optimization problem is stated as a mixed-integer linear programming problem, which is solved in an efficient way by using commercial solvers. The stochastic problem is incorporated in a model predictive control scheme to further compensate the uncertainty through the feedback mechanism. A case study of a microgrid is employed to assess the performance of the on-line optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid: experimental results show the feasibility and the effectiveness of the proposed approach.     

On control of transport in Brownian ratchet mechanisms

Engineered transport of material at the nano/micro scale is essential for the manufacturing platforms of the future. Unlike conventional transport systems, at the nano/micro scale, transport has to be achieved in the presence of fundamental sources of uncertainty such as thermal noise. Remarkably, it is possible to extract useful work by rectifying noise using an asymmetric potential; a principle used by Brownian ratchets. In this article a systematic methodology for designing open-loop Brownian ratchet mechanisms that optimize velocity and efficiency is developed. In the case where the particle position is available as a measured variable, closed loop methodologies are studied. Here, it is shown that methods that strive to optimize velocity of transport may compromise efficiency. A dynamic programming based approach is presented which yields up to three times improvement in efficiency over optimized open loop designs and 35% better efficiency over reported closed loop strategies that focus on optimizing velocities.     

The valve location problem: Minimizing environmental damage of a spill in long oil pipelines

A shutoff valve is a control device that blocks oil flow in a pipeline in order to reduce the oil escape. This paper addresses the valve location problem where, given a pipeline network and a number of valves for installation, the task is to find a valve location that minimizes the maximum environmental damage of an oil spill. We present the first complete framework for fast computing of an optimal valve location in a general oil pipeline network. To achieve this, we formalize the problem and explain how to quantify environmental damages. Next, we present two fast algorithms optimally solving the valve location problem on linear pipeline segments. Further, we show how to extend the algorithms to solve the problem on a general pipeline network. We conclude with a computational study showing that solutions provided by our framework can reduce the adverse effects of a spill by up to 37% compared to the currently used solutions.     

Modeling supplier selection and the use of option contracts for global supply chain design

As supply chains become more and more dependent on the efficient movement of materials among facilities that are geographically dispersed there is more opportunity for disruption. One of the common disruptions is the loss of production capability at supplier sites. We formulate a two-stage stochastic program and a solution procedure to optimize supplier selection to hedge against these disruptions. This model allows for the effective quantitative exploration of the trade-off between cost and risks to support improved decision-making in global supply chain design. A realistic case study is explored.     

Optimal packing of transformer coil via dynamic programming

One of the transformer major design elements is its core design. The good performance of the transformer requires its coil core to be laminated and composed of different - widths stepped layers of specific steel packed together. For optimum design, the cross sectional shape of the core must be as circular as possible. So, an optimal packing can be attained by maximizing the steel core coverage. The number of different widths of steel plates n is selected to compose the packed core for a coil of diameter d. Then, an optimum sequence of decisions, for the n widths as well as the number of thin plates of each, is required. Dynamic programming (dp) is concerned with this optimum sequential decisions. An adequate forward recursive equation is formulated , because there isn't a standard dp model for all sequential decision making problems. To implement the recursive computations, a FORTRAN program is developed. Assuming that d is given the values 150, 200, 250, 300, 400 and 500 mm. while giving n the values from 2 to 15, the program is run at different combinations of n and d. It is found that maximum core coverage is sensitive to changing of n but it is slightly affected by changing of d.     

Development of a decision support system for supplier evaluation and order allocation

This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.     

The optimal location of replicas in a network using a READ-ONE-WRITE-ALL policy

We consider the problem of locating replicas in a network to minimize communications costs. Under the assumption that the read-one-write-all policy is used to ensure data consistency, an optimization problem is formulated in which the cost function estimates the total communications costs. The paper concentrates on the study of the optimal communications cost as a function of the ratio between the frequency of the read and write operations. The problem is reformulated as a zero-one linear programming problem, and its connection to the p-median problem is explained. The general problem is proved to be NP-complete. For path graphs a dynamic programming algorithm for the problem is presented. Received: May 1993 / Accepted: June 2001     

Exploring predictors and consequences of embitterment in the workplace

Abstract

Research on the feeling of embitterment at work is still in its infancy. The present study investigated the predictors and consequences of the feeling of embitterment at work. It was hypothesised that organisational injustice as well as over-controlling supervision would predict embitterment at work and that embitterment would be associated with work-related rumination. Three hundred and thirty-seven employees completed an online survey. Regression analysis revealed that procedural injustice and over-controlling supervision were significant predictors of embitterment and that embitterment contributed significantly to the prediction of increased affective rumination and reduction in detachment. Mediation analysis indicated that embitterment at work was a significant mechanism through which organisational injustice and over-controlling supervision exerted their effect on affective rumination, which is indicative of insufficient recovery from work. Findings suggest that breaches in organisational justice can generate feelings of embitterment at work, which in turn can interfere with employees’ ability to adequately recover from work.

Practitioner Summary: The purpose of this study was to investigate predictors and consequences of embitterment in the workplace using an online questionnaire. Findings suggest that perceived unfairness, because of structural and organisational aspects, predicts feelings of embitterment and that feeling embittered at work can prevent employees from adequately recovering from work.     

Minimization of squared deviation of completion times about a common due date

We discuss a non-preemptive single-machine job sequencing problem where the objective is to minimize the sum of squared deviation of completion times of jobs from a common due date. There are three versions of the problem—tightly restricted, restricted and unrestricted. Separate dynamic programming formulations have already been suggested for each of these versions, but no unified approach is available. We have proposed a pseudo-polynomial DP solution and a polynomial heuristic for general instance. Computational results show that tightly restricted instances of up to 600 jobs can be solved in less than 6 s. General instances of up to 80 jobs take less than 2 s.Statement of scope and purposeIn this paper, we have considered an NP-complete single-machine scheduling problem arising in JIT environment, a field of great importance in manufacturing industry. The objective of the problem is to schedule a set of given jobs to minimize the sum of squared deviation of their completion times from a common due date. This paper presents a number of precedence rules, a polynomial heuristic and more importantly a unified pseudo-polynomial dynamic programming formulation. Empirical results show that the dynamic programming formulation performs better than the existing approaches.     

On a system of first-order quasi-variational inequalities connected with the optimal switching problem

In this paper we prove the existence of the maximum bounded Lipschitz continuous solution to a system of first-order quasi-variational inequalities. The method is to discretize by an Euler scheme the characteristic lines of the differential operator appearing in the system and to solve by iteration the corresponding approximate problem. The solution is interpreted as the value function of a deterministic optimal switching problem.     

Finding the Consensus Shape for a Protein Family

We define and prove properties of the consensus shape for a protein family, a protein-like structure that provides a compact summary of the significant structural information for a protein family. If all members of the protein family exhibit a geometric relationship between corresponding -carbons, then that relationship is preserved in the consensus shape. In particular, distances and angles that are consistent across family members are preserved. For the consensus shape, the spacing between successive -carbons is variable, with small distances in regions where the members of the protein family exhibit significant variation and large distances (up to the standard spacing of about 3.8 Å) in regions where the family members agree. Despite this non-protein-like characteristic, the consensus shape preserves and highlights important structural information. We describe an iterative algorithm for computing the consensus shape and prove that the algorithm converges. As a by-product we produce a multiple structure alignment for the family members. We present the results of experiments in which we build consensus shapes for several known protein families.