Multiobjective optimisation of production,distribution and capacity planning of global supply chains in the process industry

The performance of a supply chain should usually be measured by multiple criteria. We address production, distribution and capacity planning of global supply chains considering cost, responsiveness and customer service level simultaneously. A multiobjective mixed-integer linear programming (MILP) approach is developed with total cost, total flow time and total lost sales as key objectives. Also, two strategies to expand the formulation plants’ capacities are considered in the model. The ε-constraint method and lexicographic minimax method are used as solution approaches to tackle the multiobjective problem. Finally, a numerical example is investigated to demonstrate the applicability of the proposed model and solution approaches.     

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.     

Scenario aggregation in single-resource production planning models with uncertain demand

In this paper we present an application of the scenario aggregation approach proposed by Rockafellar and Wets to a simple standard multi-product multi-period production planning problem with uncertain demand and setup cost modelled by logical zero-one variables. The uncertainty in demand is expressed by a number of demand scenarios. As compared with more traditional approaches that require distributional assumptions and/or estimates of parameters from historical demand data, the scenario approach offers greater flexibility and makes it possible to take subjective information into account. The scenario aggregation principle and the corresponding progressive hedging algorithm offer a theoretically sound basis for generating consistent solutions for production planning models with uncertain demand. Since the production planning problem studied in this paper is of mixed-integer type the original scenario aggregation approach cannot be applied directly. However, since the integer variables in the production planning model are indirectly coupled to the continuous production decisions an alternative method in which only the production quantities are used to couple the different realizations can be used. This paper is a first attempt to perform this form of coupling. We illustrate the ideas on a small example and use this example to demonstrate how the solution can be evaluated in terms of flexibility measures.     

Failure Risk and Quality Cost Management in Single versus Multiple Sourcing Decision

The advantage of multiple sourcing to protect against supplier failures arising from undependable products due to latent defects is examined using a model with nonlinear external failure costs. Prior research has focused only on supplier failures arising from unreliable supply, such as late, insufficient, or no delivery. I derive a closed‐form characterization of the optimal production quota allocation for the LUX (Latent defect‐Undependable product‐eXternal failure) setting. The allocation determines the optimal supply base, with intuitive properties that hold under a mild requirement. The requirement includes the special case of equal procurement costs charged by suppliers but also allows unequal costs without any particular order. The key result of the article is a necessary and sufficient condition determining whether single or multiple sourcing is optimal. Another condition is obtained to determine the exact size of the optimal supply base, provided the mild requirement holds. With minor modifications, the results also hold when a buyer‐initiated procurement contract can be used to elicit private information on the suppliers’ unit variable production costs.     

Constrained production lot-size model with trade-credit policy: 'A comparison geometric programming approach via Lagrange'

In this paper our main objective is to investigate a deterministic inventory production lot-size model with a permissible delay in payment under a restriction. We analyse our deterministic inventory model under a restriction which will be assumed as the average inventory level. In fact we use in our analysis two approaches: the geometric programming approach; and the Lagrange method. Then a comparison between these two approaches is performed, which is our aim. Finally we deduce some previously published works of other researchers as special cases.     

How can we ascertain,understand and interpret the performance level of a production system? A visual method: 'The plan of preferences'

The general context of this study is the parameterizing of the production system. The aim is to regulate the physical flow according to objectives, usually expressed in terms of performance indicator values. Performance control requires mastery of the consequences of the choices made. It then becomes essential to be able to articulate the potential effects of the various decisions. The existing approaches to solving this problem all imply the use of means of simulation capable of assessing the value of the performance indicators for each configuration of the action parameters. Taking this assumption as a basis, and focusing on the user, a multiple criteria approach is developed towards the understanding and schematization of the behaviour of a given system. The particularity of this approach lies in combining both data analysis and user expertise. The instrumentation approach uses tools for data analysis and assistance in decision making to achieve an original visualization of the structure of performance levels in a plan, called the plan of preferences .     

一种策略层项目计划的双目标优化方法

针对产品开发项目管理的实际情况,对策略层计划优化方法进行研究。以工作包的工作量估算为基础,以资源投入水平和工期最小化为目标,考虑各种约束条件,提出一种策略层项目计划问题的混合整数规划问题模型。以非支配遗传算法NSGA-II为基础框架,设计了一种改进的双目标遗传算法。该算法针对问题的特点,提出了基于资源平滑的解码算法。参考NSGA-III的关键特征,对拥挤密度计算方法进行改进。通过企业实际项目案例,验证了算法的性能和所提出的策略层项目计划方法的有效性。     

Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives

Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linear programming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.     

Airline corporate planning— a conceptual framework

In the current economic recession, efficient planning is of paramount importance. This paper constructs a conceptual framework for the long term corporate planning of an airline: the framework suggested is an integrated approach rather than the divisional one that is widespread in the industry. The paper attempts to identify the long-term objectives of an airline, the various types of constraints under which it must operate, and the decision variables that are under its control.     

Production planning in practice

The changes and pressures facing the manufacturing and engineering industries today are increasing the importance of effective aggregate manpower and production planning. Several different theoretical optimisation models to tackle this problem have been described in detail in the literature but there have been few applications of them in practice.The reasons for this are many but include: the difficulty in expressing managements' conflicting and mixed objectives in an objective function; the necessity to oversimplify real life systems to enable these methods to be used; the simplistic approach to manpower planning used in these models; the difficulty in gaining managements' acceptance and finally the fact that what management actually wants is a tool to assist them in planning and decision making.What is being used by many managements is a case-study deterministic simulation model. Many companies are adopting this type of model for all types of planning and twelve out of twenty-seven companies visited in a research project described in this paper were using this type of model for aggregate manpower and production planning. It is proving to be an effective management tool and is being readily accepted principally because modern specialised financial modelling languages are enabling these models to be built, understood and used by non-specialist managers.     

A multiple criteria decision-making model for justifying the acceptance of rush orders

Rush orders are immediate customer demands that exceed the expectation of a currently effective MPS (master production schedule). Decision-makers are often hesitant in the decision of accepting such orders. This paper presents a multiple criteria decision-making model for justifying the acceptance of rush orders for an assembly-to-order production system. Four criteria or production objectives are simultaneously considered and a multiple objective programming technique, the e-constraints approach, is adopted to solve the decision-making problem. This model could give the cost estimation for producing a rush order under various combinations of production objectives. The computed cost value could serve as a valuable reference for justifying the economics of accepting the rush order, and help to determine its pricing strategy.     

Zero-base budgeting—Dealing with conflicting objectives

While Zero-Base Budgeting (ZBB) has received considerable attention in the public sector, virtually all the studies dealing with the system have neglected or given cursory attention to the problem of multiple conflicting objectives. This paper presents a goal programming approach as a systematic means to develop the ZBB process for multiple objectives. This approach allows the administrators to more realistically portray the decision environment as well as their judgment in their budgetary planning models, thus making the budgeting an effective and pragmatic way to implement the planning and decision making process. This study demonstrates a goal programming based ZBB system in the public sector based on real-world data.     

A Model for Setting Performance Objectives for Salmonella in the Broiler Supply Chain

A stochastic model for setting performance objectives for Salmonella in the broiler supply chain was developed. The goal of this study was to develop a model by which performance objectives for Salmonella prevalence at various points in the production chain can be determined, based on a preset final performance objective at the end of the processing line. The transmission of Salmonella through the broiler production chain was modeled. The prevalence at flock level was calculated from the measured prevalence at sample level. The transmission model is based on data on the occurrence of Salmonella collected in the Dutch broiler production chain during several years. The developed model can be used by policymakers and industry to determine economically and politically acceptable performance objectives for various points of the production chain and to draw conclusions about which interventions are most appropriate.     

A methodology for the design of manufacturing systems using group technology

The primary objectives in implementing computer integrated manufacturing systems (CIMS) are to improve quality, enhance flexibility, and increase productivity. One of the approaches used to achieve these objectives is to organize and group the production machines into manufacturing cells using group technology concepts. Cellular manufacturing allows small batch production to gain economic advantages similar to mass production while maintaining the flexibility of job shop production. This paper presents a methodology which uses design and manufacturing attributes to form manufacturing cells in a computer integrated manufacturing setting. The methodology is implemented in two phases. In phase I, parts are grouped into part families based on their design and manufacturing attributes. In phase II of the methodology, the machines are grouped into manufacturing cells based on relevant operational costs, and the various cells are assigned part families using an optimization technique. The optimization technique employs integer programming to minimize the total operational costs.     

Simulation-based performance assessment of master planning approaches in semiconductor manufacturing

Semiconductor manufacturing is confronted with a large number of products whose mix is changing over time, heterogeneous fabrication processes, re-entrant flows of material, and different sources of environmental and system uncertainty. In this context, the mid-term production planning approach, i.e., master planning, typically does not capture the entire complexity of the shop-floor. It deals with an aggregated representation of the production system. There is a need for evaluating the planning algorithm in use while taking the execution level into account. Therefore, we introduce in this paper a simulation-based framework that allows for modeling the behavior of the market demand and the production system. An appropriate performance assessment methodology is proposed. The performance of two heuristic approaches for master planning in semiconductor manufacturing, a genetic algorithm and a rule-based assignment procedure, is evaluated within a rolling horizon setting while considering demand and execution uncertainty. A reduced discrete-event simulation model is used to mimic a one-stage network of wafer fabrication facilities. The results of simulation experiments are discussed.     

一种决策者判断一致性的聚类方法

对于产量为模糊区间数的生产计划群决策问题,考虑不同产品生产的优先度和决策者权重对决策者判断一致性度量的影响,给出了相对加权一致度的一种计算方法。当群决策的结果不一致时,提出了依据相对加权一致度对决策者进行聚类的方法,并给出了每一类决策者决策结果的综合方法。最后通过算例说明了方法的应用过程。     

Lot sizing with storage losses under demand uncertainty

We address a variant of the single item lot sizing problem affected by proportional storage (or inventory) losses and uncertainty in the product demand. The problem has applications in, among others, the energy sector, where storage losses (or storage deteriorations) are often unavoidable and, due to the need for planning ahead, the demands can be largely uncertain. We first propose a two-stage robust optimization approach with second-stage storage variables, showing how the arising robust problem can be solved as an instance of the deterministic one. We then consider a two-stage approach where not only the storage but also the production variables are determined in the second stage. After showing that, in the general case, solutions to this problem can suffer from acausality (or anticipativity), we introduce a flexible affine rule approach which, albeit restricting the solution set, allows for causal production plans. A hybrid robust-stochastic approach where the objective function is optimized in expectation, as opposed to in the worst-case, while retaining robust optimization guarantees of feasibility in the worst-case, is also discussed. We conclude with an application to heat production, in the context of which we compare the different approaches via computational experiments on real-world data.     

多品种集约生产计划问题的模糊方法

提出具有模糊需求量和模糊能力约束以及资本水平约束的多品种类集约生产计划问题 的模糊优化模型及模糊解方法. 通过对模糊需求量和模糊等式的描述,提出了模糊需求环境下 生产- 库存平衡方程的两种等价的描述方法, 并给出了模糊等式的实用解释. 建立了具有模 糊需求量和模糊能力约束集约生产计划问题的优化模型FMAPP ,并给出了求解模型的参数规 划方法.     

Tactical production and distribution planning with dependency issues on the production process

Tactical production-distribution planning models have attracted a great deal of attention in the past decades. In these models, production and distribution decisions are considered simultaneously such that the combined plans are more advantageous than the plans resolved in a hierarchical planning process. We consider a two-stage production process, where in the first stage raw materials are transformed into continuous resources that feed the discrete production of end products in the second stage. Moreover, the setup times and costs of resources depend on the sequence in which they are processed in the first stage. The minimum scheduling unit is the product family which consists of products sharing common resources and manufacturing processes. Based on different mathematical modelling approaches to the production in the first stage, we develop a sequence-oriented formulation and a product-oriented formulation, and propose decomposition-based heuristics to solve this problem efficiently. By considering these dependencies arising in practical production processes, our model can be applied to various industrial cases, such as the beverage industry or the steel industry. Computation tests on instances from an industrial application are provided at the end of the paper.     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.