An integrated production planning model for molds and end items

This study develops a mathematical modelling framework for simultaneously generating production plans for molds and the end items that are made with them. The inputs considered are the item demand (assumed constant over an infinite planning horizon), holding costs and shortage costs, together with the molds’ statistical lifetime distribution (in terms of number of uses) and costs pertaining to amortization, preventive replacements and corrective replacements.     

关于开展电力中长期远景规划研究的几点建议

开展电力中长期远景规划研究是关系到国民经济可持续发展的一件大事。文章通过对我国能源资源现状分析,指出我国能源资源短缺必将影响国民经济可持续发展。针对我国能源资源现状和发展前景,提出了加快我国能源建设的措施建议。     

An iterative approach to item-level tactical production and inventory planning

In this paper, we propose an iterative approach to jointly solve the problems of tactical safety stock placement and tactical production planning. These problems have traditionally been solved in isolation, even though both problems operate in the same decision making space and the outputs of one naturally serve as the inputs to the other. For simple supply chain network structures, two stages and one or many products, we provide sufficient conditions to guarantee the iteration algorithm’s termination. Through examples, we show how the algorithm works and prove its applicability on a realistic industrial-scale problem.     

Determining safety stocks for production planning in uncertain manufacturing

When computing production plans and available-to-promise statements, the randomness of flow times and yield rates are the two major sources of uncertainty. Considering both of these uncertainties, this study employs a simple formula for computing the standard deviation for cumulative good product output of a particular product type at a particular time. Using this standard deviation, the necessary safety stock for that product type at that time can be obtained. This study was motivated by semiconductor wafer manufacturing, which requires many process steps performed by highly unreliable equipment. Such an environment causes the means and the variances in flow times to be generally larger than those for other industries. It also makes yield rates uncertain. Thus, when making order quotations and calculating production plans, safety stocks must be used to guard against the uncertainties of the manufacturing processes to ensure on-time-delivery ratios and to provide better customer service.     

A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon

This paper addresses the problem of redesigning a supply chain network with multiple echelons and commodities. The problem belongs to a comprehensive class of network redesign problems previously introduced in the literature. Redesign decisions comprise the relocation of existing facilities to new sites under an available budget over a finite time horizon, the supply of commodities by upstream facilities, the inventory levels at storage facilities, and the flow of commodities through the network. The problem is modeled as a large-scale mixed-integer linear program. Feasible solutions are obtained by using a tabu search procedure that explores the space of the facility location variables. The latter prescribe the time periods in which changes in the network configuration occur. They are triggered by the setup of new facilities, which operate with capacity transferred from the existing facilities, and by closing the latter upon their entire relocation. As the problem is highly constrained, infeasible solutions with excess budget are allowed during the course of the search process. However, such solutions are penalized for their infeasibility. Computational experiments on realistically sized randomly generated instances indicate that this strategic oscillation scheme used in conjunction with tabu search performs very well.     

An experimentally confirmed resource planning model of services under production function uncertainties

The way in which service firms transform inputs into outputs is typically uncertain or unknown. Consequently decision makers, at best, can only make estimates of the underlying production function. The purpose of this study is to offer policy recommendations to service providers and to examine experimentally the sensitivity of estimates of production functions. The primary result of this study is a foundation for modeling manpower planning decisions for co-produced services when production functions are mis-estimated and/or mis-specified.     

A hierarchical planning system for energy intensive production environments

This paper describes a hierarchical production planning approach with decision support features for energy intensive industries with particular reference to a tile manufacturing factory. In the tiling industry, the facilities which contribute most to the consumption of energy (and, hence, to the production costs) are usually the kilns where the curing operation is carried out. Frequently, the kilns are also the bottleneck in terms of capacity utilization. Thus, in order to save on energy costs, a planning approach which aims at minimizing the number of active kilns throughout the year is needed besides optimizing the process design in the curing department. To achieve the latter goal, it is necessary to take into account demand fluctuations as well as detailed capacity restrictions while deciding on the lot sizes of the products and the kilns on which the products are loaded. Rather than adopting a monolithic mathematical model for developing a desirable production plan, a hierarchical approach which decomposes the problem into two sub-problems is preferred. In the first level, products and capacity are aggregated over the planning horizon to achieve an overall consideration of demand fluctuations over time. Then, the solution provided by the aggregate solution for the current planning period is disaggregated into a detailed lot sizing and loading solution. The disaggregated problem is difficult to solve and hence, a heuristic is proposed here. This planning approach is sustained by a Decision Support System which enables the elimination of possible inconsistencies in the production plan by providing an effective interaction with the decision maker.     

A hybrid simulation optimization method for production planning of dedicated remanufacturing

This paper presents a hybrid cell evaluated genetic algorithm (CEGA) for optimization of the dedicated remanufacturing system with simulation. The paper first summarizes the special characteristics and problems of the dedicated remanufacturing. The paper then proposes a simulation model with a prioritized stochastic batch arrival mechanism, considering factors that affect the total profit. Based on the simulation model, the CEGA algorithm is developed to optimize the production planning and control policies for dedicated remanufacturing. A case study is provided based on the remanufacturing facility located at Austin, USA     

Reengineering the production planning process in the food industry

This paper addresses managerial process reengineering and in particular the reengineering of the production planning process. The reengineered process highlights planning options to avoid the process imbalance and loss of production potential that can follow innovation in a facility that is fully committed to JIT production.The study was motivated by production problems following product innovation within the food industry but the reengineering conclusions and procedures are applicable to all similarly structured industries. An example based on data from a snack food manufacturing company illustrates the reengineered procedure for a plant that is typical of the food processing industry.     

Integrating noncyclical preventive maintenance scheduling and production planning for a single machine

This paper deals with the problem of integrating noncyclical preventive maintenance and tactical production planning for a single machine. We are given a set of products that must be produced in lots during a specified finite planning horizon. The maintenance policy suggests possible preventive replacements at the beginning of each production planning period, and minimal repair at machine failure. The proposed model determines simultaneously the optimal production plan and the instants of preventive maintenance actions. The objective is to minimize the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs and production costs, while satisfying the demand for all products over the entire horizon. The problem is solved by comparing the results of several multi-product capacitated lot-sizing problems. The value of the integration and that of using noncyclical preventive maintenance when the demand varies from one period to another are illustrated through a numerical example and validated by a design of experiment. The later has shown that the integration of maintenance and production planning can reduce the total maintenance and production cost and the removal of periodicity constraint is directly affected by the demand fluctuation and can also reduce the total maintenance and production cost.     

A master production scheduling procedure for stochastic demand and rolling planning horizons

The problem of interest is a one product, uncapacitated master production schedule (MPS) in which decisions are made under rolling planning horizons. Demand is stochastic and time varying, and effectiveness is measured by inventory holding, production setup, and backorder costs.Typically, in both the research literature and the business practice the stochastic nature of the problem is modeled in an ad hoc fashion. The stochastic MPS problem is usually solved by adding safety stock to production quantities obtained from a deterministic lot-sizing algorithm. Here, the stochastic nature of the problem is explicitly considered, as an optimal algorithm for solving the static probabilistic dynamic lot-sizing problem is adapted to rolling planning horizons. The resulting algorithm is found to dominate traditional approaches over a wide variety of experimental factors, reducing total costs by an average of 16% over traditional methods.     

Applying possibilistic linear programming to aggregate production planning

This work presents a novel interactive possibilistic linear programming (PLP) approach for solving the multi-product aggregate production planning (APP) problem with imprecise forecast demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. The proposed approach uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs, and minimizing the risk of obtaining higher total costs. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed PLP approach yields an efficient APP compromise solution and overall degree of decision maker (DM) satisfaction with determined goal values. Particularly, several significant management implications and characteristics of the proposed PLP approach that distinguish it from the other APP decision models are presented.     

Goal programming approach to marketing/ production planning

This article focuses on the need for greater coordination and integration between production and marketing. The potential conflicts between the production and marketing areas are highlighted, and a goal-programming algorithm is developed for dealing with the complex trade-off decisions involved in marketing/production planning. The algorithm is applied to a case example to illustrate its use in managerial decision making.     

Integrated production planning with sequence-dependent family setup times

This paper proposes an integrated optimization model of aggregate production planning (APP), family disaggregation planning, and family scheduling problems in hierarchical production planning (HPP) systems considering sequence-dependent family setup times. The model obtains the optimal production plan for each product type and product family in each period, together with the globally optimal production sequence of product families in all planning periods. The proposed model is tested with randomly generated experimental data consistent with what is prevalent in the manufacturing industry and its results are compared with those of the traditional HPP models. Our results show that the integrated model realizes greater cost savings.     

An optimal production run length in deteriorating production processes

This paper presents an EMQ model which determines an optimal production run length in a deteriorating production process. It is assumed that the process is subject to a random deterioration from an in-control state to an out-of-control state with an arbitrary distribution and, thus, produces some proportion of defective items. An optimal production run length and a minimum average cost are derived in three deteriorating processes: constant, linearly increasing, and exponentially increasing. Several different results from those of previous research are obtained and discussed. A numerical experiment is carried out to see the behavior of the proposed model depending on the cost and process parameters. Some interesting behaviors are observed.     

Modeling and simulation of order-driven planning policies in build-to-order automobile production

In adopting build-to-order order fulfillment systems, automotive companies strive to better synchronize their production output with market demand. This essentially gives rise to a new paradigm in production planning. Since all business is linked to customer orders, the operational performance is substantially determined by order-driven planning. Therefore, a clear understanding of the associated planning tasks, order promising and master production scheduling, as well as their dynamic interaction is essential. Based on the analysis of the decision situation of order-driven planning in build-to-order settings, we provide a framework comprising separate interlinked quantitative models for order promising and master production scheduling. The focus of the contribution is on the modeling and evaluation of both models in a dynamic setting. The approach is evaluated by means of a simulative analysis using empirical data from the automotive industry. Conclusions regarding the potentials of such systems with respect to customer service, the leveling of resource utilization, and holding are presented.     

A hierarchical process industry production–distribution planning model

Global and multinational companies are subject to government regulations in addition to other international uncertainties due to operation in diverse geographic locations. Such government regulations often affect the cost of raw materials adversely which in turn creates adverse impact on product cost and forces the decision makers to re-evaluate current production–distribution plan. This paper presents an integrated supply chain model for simultaneous strategic and operational planning of a strategic business unit (SBU) in a global supply chain affected by government regulations. The model incorporates impact of changes in cost of inputs on expected product cost and solves for an optimal strategic and operational plan for the entire supply chain. In addition, the model includes exchange rates, border crossing costs and solves a multi-period model with due consideration of uncertainties in demand and transportation time.     

A dynamic model for the optimization of decoupling point and production planning in a supply chain

In this paper, we propose a dynamic model to simultaneously determine the optimal position of the decoupling point and production-inventory plan in a supply chain such that the total cost of the deviation from the target production rate and the target inventory level is minimized. Using the optimal control theory, we derive the closed form of the optimal solution when the production smoothing policy and the zero-inventory policy are applied. The result indicates that under the production smoothing policy, the overestimation of demand rate during the pre-decoupling stage guarantees the existence of the optimal decoupling point; meanwhile the optimal decoupling point exists under zero-inventory policy when the demand rate is underestimated. Also we perform mathematical analysis on the behavior of the optimal production rate and the inventory level and the effect of problem parameters such as the length of the product life cycle and the forecast error on the performance.     

Variable neighborhood search heuristic for nonconvex portfolio optimization

Abstract

In this article we consider a portfolio optimization problem under multiple real-world constraints, such as: cardinality constraints, tracking error, active share, and turnover. We propose a heuristic based on variable neighborhood search (VNS) that effectively addresses additional constraints that introduce non-convexities. In the VNS-based heuristic, several neighborhood structures are introduced and fast local search is implemented. We develop a VNS portfolio rebalancing framework (VNS-PRF) with two rebalance strategies. Data sets provided by a financial investment firm are used to evaluate the validity and reliability of the proposed VNS-PRF. Computational experiments and different portfolio performance measures indicate that our approach is able to obtain solutions with competitive quality and can be applied on large-scale data sets.