Production Planning Decisions Research Articles

Matheuristics vs. metaheuristics for joint lot-sizing and dynamic pricing problem with nonlinear demands

This paper considers a joint dynamic pricing and production planning decisions problem for a profit-maximizing firm that produces and sells multiple products. The objective is to develop a coordinated decision approach for multi-product pricing and lot sizing decisions for a manufacturer considering a limited production capacity. The demand for each product is assumed to be iso-elastic and integrates the complementarity and the substitution effects between the products. First, the problem is formulated as a non-convex mixed integer nonlinear programming model (MINLP) incorporating capacity constraints, setup costs, and nonlinear demand functions. Then, since the model is nonlinear and non-convex, a set of approximate approaches based on the Genetic algorithm, Late Acceptance Hill Climbing and Simulated Annealing methods are designed to solve this problem. Based on this study, the performances of two variants of approximate methods: matheuristics and metaheuristics are discussed and analyzed. The extensive experimental study, performed on real-world inspired instances, shows that matheuristic methods with setup-variables encoding scheme outperform the rest of the methods. The research outcomes show that coordinating a decision-making process by optimizing both prices and production plans simultaneously can result in significant profit for a company. However, one must consider the joint effect of the parameters of the demand function as well as the impact of the production capacity. This comprehensive understanding enables the company to avoid excessive investments in less lucrative products with lower sales potential, thereby ensuring resource allocation aligns with profitability and market demand.

The relationship between the role of management accountants, advanced manufacturing technologies, cost system sophistication and performance: a path model

Purpose This paper aims to test a contingency-based path model that concurrently links the role of management accountants (MA) and advanced manufacturing technologies (AMTs) to cost system sophistication (CSS), as well as linking the latter to improvements in organisational performance through improving cost management and product planning decisions. Design/methodology/approach This paper used the questionnaire survey strategy to collect data from 373 medium and large manufacturing business units based in the UK, then subjected the data to structural equation modelling analysis to test a contingency-based path model. Findings The results show that the role of MAs and AMTs positively influence CSS. Moreover, it was found that the latter is positively associated with improvements in cost management decisions which, in turn, lead to improvements in organisational performance. However, no support was found for the association between the level of CSS and improvements in product planning decisions, although the latter was found to be positively associated with organisational performance. These results confirm the theory and empirical findings regarding the role that MAs and AMTs play in designing the cost accounting system, and support the argument that adopting sophisticated cost systems does not lead directly to improvements in organisational performance, unless the benefits of such systems, in terms of improved decision-making and cost applications, are used. Originality/value This research contributes to the literature by testing a contingency-based path model that incorporates hitherto underexamined contextual factors, namely, the role of MAs and AMTs; examining the effect of CSS on a critical output, organisational performance and the mechanisms of this effect; and considering the complexity of the business environments through the concurrent testing of the relationships involved in the research model.

Production planning decisions in the broiler chicken supply chain with growth uncertainty

Broiler chickens are specially raised for meat production and their supply chain is composed of breeders, hatcheries, feed mills, farms, slaughterhouses, wholesalers, and retailers. The coordination between supply chain players has great potential to streamline production and increase competitiveness in the market. A critical element in the decision-making process of this agri-system is the weight gain of broiler chickens. Variability in chicken growth patterns can affect production plans in practice. The most efficient weight estimation models require sensors in real-time, which are not available in many companies. Thus, some small farms require a simpler approach to adjust production planning based on the uncertainty of chickens’ weight while they transition to new technologies. This research proposes an optimization-based methodology for the integration of farms and slaughterhouses to plan production under chicken growth uncertainty. The methodology includes two models: (i) a two-stage stochastic model that supports lot-sizing and inventory management decisions while considering scenarios of chicken growth uncertainty; and (ii) a mixed-integer linear programming model that supports lot allocation. We present the results of implementing the methodology in a poultry company in Santa Marta (Colombia), where we improved costs by 8.6% while meeting tactical, biological, and biosecurity constraints.

OPTIMIZATION OF PRODUCTION COSTS WITH SIMPLEX METHOD

One of the problems in the company is my resource limitation, the time of production and the tools used in the production process. Companies in making decisions in production planning in the presence of these limitations, should seek to maximize the profit generated. However, in the production process a production planning reference is required to maximize the results obtained and minimize the production costs used. To solve the problem, needed a problem solving tool that is linear program using simplex method. Simplex method is one of the methods of linear program in solving the problem of more than two variables that can be applied into everyday life and can be used in production process planning. With one of the ultimate goal is the achievement of the optimum value with the constraints of limited resources. The results of the simplex method have decreased costs incurred less than the usual costs incurred every month and the results obtained using the simplex method can be used as a reference in making decisions to get optimal results on production costs in the company.

Production planning decision of a dairy under supply disruption and demand uncertainty

PurposeThe purpose of this paper is to develop a model for the production planning decision of a dairy plant in a multi-product setting under supply disruption risk and demand uncertainty while determining the optimal product-mix and material planning requirement.Design/methodology/approachA mixed-integer nonlinear programming model is proposed to determine the optimal product-mix that maximizes the expected profit of a dairy. The data are collected through visits to the dairy site, conducting brainstorming sessions with the plant manager and marketing head at the corporate office. Disruption data are collected from the India Meteorological Department, Odisha.FindingsFrom the analysis, it is recommended that the dairy should not produce curd during the planning period. Moreover, turnover from toned, double toned and baby food is maximum than that of the curd and these products are produced in the planning period. The expected profit increases from its present value when an optimal product-mix is followed. Sensitivity analysis is performed to analyze the effect of demand uncertainty, supply disruption and production quota. The expected profit decreases as the supply failure probability increases.Research limitations/implicationsThe model is implemented in a dairy plant under Orissa State Cooperative Milk Producers Federation, Odisha, India. The proposed methodology has not been validated, theoretically. The concerned dairy is based on the Indian context, but the authors believe that the study is highly relevant to other dairies as well.Practical implicationsThis study provides a methodology for dairy plant managers to plan production effectively under supply disruption risk with demand uncertainty. It also suggests material requirement planning at different factories of the dairy plant.Originality/valueThis paper develops a mathematical model for the production planning decision of a dairy plant that determines the optimal product-mix, which maximizes the expected profit of a dairy under disruption risk and demand uncertainty (in the Indian context).

A review of condition-based maintenance: Its prognostic and operational aspects

Condition-based maintenance (CBM) detects early signs of failure and dictates when maintenance should be performed based on the actual condition of a system. In this paper, we first review some of the recent research on CBM under various physical structures and signal data. Then, we summarize several kinds of prognostic models that use monitoring information to estimate the reliability of complex systems or products. Monitoring information also facilitates operational decisions in production planning, spare parts management, reliability improvement, and prognostics and health management. Finally, we suggest some research opportunities for the reliability and operations management communities to fill the research gap between these two fields.

Joint production planning, pricing and retailer selection with emission control based on Stackelberg game and nested genetic algorithm

In practice, it is of paramount importance that firms make joint decisions in production planning, pricing and retailer selection while considering emission regulation. This is because the joint decisions can ensure firms to obtain higher profits while contributing to sustainable environments. However, due to the problem complexity, no models facilitating such decision making are available. This study aims to develop a model to help firms make optimal joint decisions. To model the situations where a manufacturer is the leader and the retailers are followers, we adopt the Stackelberg game theory and develop a 0–1 mixed nonlinear bilevel program to maximize the profits of both the manufacturer and his retailers. We further develop a nested genetic algorithm to solve the game model. Numerical examples demonstrate (i) the applicability of the game model and the algorithm and (ii) the robustness of the algorithm. Managerial insights are obtained, suggesting that (i) manufacturers need to identify the capacity ranges (called capacity traps) where capacity increases result in reduced profits when making decisions to optimize profits; (ii) retailers should make suitable, e.g., pricing decisions so that the manufacturers can include them in the supply chains; (iii) both manufacturers and retailers may not need to consider the carbon emission buying (or selling) price when making decisions.

Simulation-Based Performance Assessment of Production Planning Models With Safety Stock and Forecast Evolution in Semiconductor Wafer Fabrication

We study the performance of production planning models for wafer fabs in a rolling horizon setting under stochastic demand modeled using the Martingale Model of Forecast Evolution (MMFE). The models differ in how they incorporate lead times and safety stocks. The number of frozen periods is considered as an experimental factor to study the nervousness of the resulting plans. Our simulation experiments show that chance-constrained production planning models that integrate safety stock and production planning decisions outperform others. The formulations with workload-dependent lead times also outperform those with fixed lead times that are an integer multiple of the period length.

A Tabu List-Based Algorithm for Capacitated Multilevel Lot-Sizing with Alternate Bills of Materials and Co-Production Environments

The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing turns into an increasingly complex set of decisions that requires efficient solution approaches, in response to the time-consuming exact methods (LP, MIP). This paper aims to propose a Tabu list-based algorithm (TLBA) as an alternative to the Generic Materials and Operations Planning (GMOP) model. The algorithm considers a multi-level, multi-item planning structure. It is initialized using a lot-for-lot (LxL) method and candidate solutions are evaluated through an iterative Material Requirements Planning (MRP) procedure. Three different sizes of test instances are defined and better results are obtained in the large and medium-size problems, with minimum average gaps close to 10.5%.

Simulation-optimization model for production planning in the blood supply chain.

Production planning in the blood supply chain is a challenging task. Many complex factors such as uncertain supply and demand, blood group proportions, shelf life constraints and different collection and production methods have to be taken into account, and thus advanced methodologies are required for decision making. This paper presents an integrated simulation-optimization model to support both strategic and operational decisions in production planning. Discrete-event simulation is used to represent the flows through the supply chain, incorporating collection, production, storing and distribution. On the other hand, an integer linear optimization model running over a rolling planning horizon is used to support daily decisions, such as the required number of donors, collection methods and production planning. This approach is evaluated using real data from a blood center in Colombia. The results show that, using the proposed model, key indicators such as shortages, outdated units, donors required and cost are improved.

Multi-objective aggregate production planning decisions using two-phase fuzzy goal programming method

In practical aggregate production planning (APP) decisions, the decision maker (DM) must simultaneously handle multiple conflicting goals that govern the use of the constrained resources. This study aims to present a two-phase fuzzy goal programming method to solve multi-objective APP problems with multiple products and multi-time periods. The designed fuzzy multi-objective linear programming model attempts to simultaneously minimize total costs, total carrying and backordering volume, and total rates of changes in labor levels with reference to inventory carrying levels, machine capacity, work-force levels, warehouse space and available budget. An industrial case is used to demonstrate the feasibility of applying the proposed method to real-life APP decisions. The contribution of this study lies in presenting a two-phase fuzzy goal programming methodology to solve multi-objective APP decision problems and provides a systematic decision-making framework that facilitates a DM to interactively adjust the search direction until the preferred efficient compromise solution is obtained.

A multiperiod model for production planning and design in a multiproduct batch environment

A general multiperiod model to optimize simultaneously production planning and design decisions applied to multiproduct batch plants is proposed. This model includes deterministic seasonal variations of costs, prices, demands and supplies. The overall problem is formulated as a mixed-integer linear programming model by applying appropriate linearizations of non-linear terms. The performance criterion is to maximize the net present value of the profit, which comprises sales, investment, inventories, waste disposal and resources costs, and a penalty term accounting for late deliveries. A noteworthy feature of this approach is the selection of unit dimensions from the available discrete sizes, following the usual procurement policy in this area. The model simultaneously calculates the plant structure (parallel units in every stage, and allocation of intermediate storage tanks), and unit sizes, as well as the production planning decisions in each period (stocks of both product and raw materials, production plans, policies of sales and procurement, etc.).

Evaluating Product Plans Using Real Options

Product planning helps a company to strategically plan its current and future product platforms and offer product variants in the marketplace. Product platforming is widely touted as a successful strategy for mass customization. However, due diligence should be exercised before implementing any product platform strategy. The product planning exercise should account for future uncertainties. Traditional financial tools such as the net present value (NPV) are static since they do not compensate for any exogenous and endogenous uncertainties during the course of the project. The crux of the problem lies in the evaluation model that is used for evaluating the product planning projects. While many view uncertainties in a product planning project as problematic, it can also be viewed as a source of new opportunities. We argue that uncertainties should be an integral part of the evaluation model. If the future possibilities (or strategic options) are not considered in the evaluation model, a corporation may face a “myopic syndrome”. In this article, we consider two important product planning decisions—platform decisions and product variant decisions. The platform decision involves strategic selection of a concept product platform from various possible alternative concept product platforms. The product variant decision involves deciding how long a company should continue to offer its current product variant in the marketplace and whether the existing product variant should be discontinued, scaled down, or scaled up with additional product features. To address the two aforementioned decisions, we developed a real options–based methodology that considers technical, project implementation, and market-related uncertainties. The proposed methodology uses a binomial and quadranomial lattice approach to build a decision tree. Product planning decisions at various decision tree nodes are evaluated using a risk-neutral option valuation methodology. We demonstrate the working of the proposed methodology using an illustrative example.

On the importance of sequencing decisions in production planning and scheduling

We discuss the traditional hierarchical approach to production planning and scheduling, emphasizing the fact that scheduling constraints are often either ignored or considered in a very crude way. In particular, we underline that the way scheduling is carried out is crucial for the capacity constraints on the lot sizes. Usual methods to handle capacity in theory or in practice are reviewed. Finally, we present an approach that tries to overcome these drawbacks by capturing the shop–floor capacity through scheduling considerations.

Appropriateness and Impact of Platform-Based Product Development

In their quest to manage the complexity of offering greater product variety, firms in many industries are considering platform-based product development. Product platforms, which are component and subsystem assets shared across a product-family, enable a firm to better leverage investments in product design and development. While the platform approach offers a number of benefits, it also imposes certain additional costs that have not received adequate research attention. In this paper, we use an industrial example both to illustrate some of the costs and benefits of platform-based product development and to motivate the development of a mathematical model. The model is formulated to better understand the appropriateness of product platforms and their impact on product-planning decisions. Our results indicate that platforms are not appropriate for extreme levels of market diversity or high levels of nonplatform scale economies. Also, a firm's product positioning and introduction sequence decisions made during the product-planning phase are significantly impacted by the presence of platforms. Specifically, a platform increases the separation among products and offers a multitude of product introduction strategies. We translate our model findings into a managerial framework.

PRODUCTION PLANNING DECISIONS IN FLEXIBLE MANUFACTURING SYSTEMS WITH RANDOM MATERIAL FLOWS

Abstract In this paper, we consider the FMS planning problem of determining optimal machine workload assignments in order to rninimize mean part flow time. We decompose this problem into the subproblems of first forming machine groups and next assigning operations to these groups. Three types of grouping configurations—no grouping, partial grouping and total grouping—are considered. In both no grouping and partial grouping, each machine is tooled differently. While each operation is assigned to only one machine in no grouping, partial grouping permits multiple operation assignments. On the other hand, total grouping partitions the machines into groups of identically-tooled machines; each machine within a group is capable of performing the same set of operations. Within this grouping framework, we consider three machine loading objectives—minimizing the total deviation from the optimal group utilization levels, minimizing part travel and maximizing routing flexibility, for generating a variety of system configurations. A queueing network model of an FMS is used to determine the optimal configurations and machine workload assignments for the no grouping and total grouping cases. It is shown that under total grouping, the configuration of M machines into G groups that minimizes flow time is one in which the sizes of the machine groups are maximally unbalanced and the workload per machine in the larger groups is higher. This extends previous results on the optimality of unbalancing both machine group sizes and machine workload to the mean flow time criterion. A simulation experiment is next conducted to evaluate the alternative machine configurations to understand how their relative performance depends upon the underlying system characteristics, such as system utilization level and variation among operation processing times. We also investigate the robustness of these configurations against disruptions, such as machine unreliability and variation in processing batch sizes. While different configurations minimize mean flow time under different parameter values, partial grouping with state-dependent part routing performs well across a wide range of these values. Experimental results also show that the impact of disruptions can be reduced by several means, such as aggregating operations of a part to be performed at the same machine, in addition to providing routing flexibility.

Semi‐quantitative risk assessment method for product management

AbstractThe workshop presented a semi‐quantitative risk assessment technique that can be used to judge the total risk on all undesired consequences/events for a product manufacturing operation. The technique requires an experienced team to judge risk utilizing a numerical rating system that, although arbitrarily chosen, is self consistent and provides a relative assessment of product manufacturing risk. Once calculated, the risk is compared to the product's net income for making risk management and product planning decisions. The workshop included an exercise where all participants could try the technique.

Robustness of linear models in dynamic multivariate predictions

Linear aggregation models employing unit and equal weights have been shown to be superior to human decisions in a surprising range of decision situations. In addition, decisions based on these models have often been found to be superior to those based on linear regression models (LRMs). This general issue was explored for repetitive decisions in production planning. The problem considered differs in several aspects from the types of problems investigated previously: (1) the problem is dynamic rather than static; (2) a set (or vector) of interactive decisions dependent on previous decisions is required to be made, where a decision in stage t, the dependent variable, becomes an independent variable in stage t + 1; and (3) the criterion function is cost with a quadratic loss function (rather than the correlation measure of R 2). Moreover, since repetitive decisions were involved, the parameters of the models were estimated using past human decisions. These were used to predict specific values of the decision variables (rather than rank order), which in turn were employed recursively to predict values of the decision variables at subsequent stages. While decisions from equal weighting rules were found to be superior to human decisions and not greatly inferior to decisions from linear regression models, decisions from unit weighting rules performed poorly. The rationale for such performance is discussed, indicating that previous theoretical and empirical research on linear weighting models is not generally applicable to dynamic, multivariate interactive decisions problems with lagged variables.

Decision Systems for Inventory Management and Production Planning

An in-depth discussion of the major decisions in production planning, scheduling, and inventory management faced by organizations, both private and public. Strategic and operational issues are covered, as well as the latest systems used to make decisions, including Just-in-Time Manufacturing, KANBAN, Distribution Requirements Planning, and PUSH Control. A series of cases focusing on one organization complement the text's discussion, and several problem sets are also included. An extensive list of references allows the advanced student to pursue topics of interest in more detail.