Machine Setup Cost Research Articles

Improvement to an existing multi-level capacitated lot sizing problem considering setup carryover, backlogging, and emission control

This paper presents a multi-level, multi-item, multi-period capacitated lot-sizing problem. The lot-sizing problem studies can obtain production quantities, setup decisions and inventory levels in each period fulfilling the demand requirements with limited capacity resources, considering the Bill of Material (BOM) structure while simultaneously minimizing the production, inventory, and machine setup costs. The paper proposes an exact solution to Chowdhury et al. (2018)'s[1] developed model, which considers the backlogging cost, setup carryover & greenhouse gas emission control to its model complexity. The problem contemplates the Dantzig-Wolfe (D.W.) decomposition to decompose the multi-level capacitated problem into a single-item uncapacitated lot-sizing sub-problem. To avoid the infeasibilities of the weighted problem (WP), an artificial variable is introduced, and the Big-M method is employed in the D.W. decomposition to produce an always feasible master problem. In addition, Wagner & Whitin's[2] forward recursion algorithm is also incorporated in the solution approach for both end and component items to provide the minimum cost production plan. Introducing artificial variables in the D.W. decomposition method is a novel approach to solving the MLCLSP model. A better performance was achieved regarding reduced computational time (reduced by 50%) and optimality gap (reduced by 97.3%) in comparison to Chowdhury et al. (2018)'s[1] developed model.© 2023 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.

A multi-objective vibration damping optimization algorithm for solving a cellular manufacturing system with manpower and tool allocation

In this paper,a novel bi-objectivemathematical model is proposed to designa four-dimensional (i.e.,part, machine, operator, and tool) cellular manufacturing system (CMS) in a dynamic environment. The main objectives of this model are to 1) minimize total costs including tools processing cost, costs of transporting cells between various cells, machine setup cost, and operators’ educational costs, and 2) maximizing skill level of operators. The developedmodel is strictly NP-hard and exact algorithms cannot find globally optimal solutions in reasonably computational time. So, a multi-objective vibration damping optimization algorithm (MOVDO) with a new solution structure that satisfies all the constraints and generates feasible solutions is proposed to find near-optimal solutions in reasonablycomputational time. Since there is no benchmark available inthe literature, three other meta-heuristic algorithms (i.e., non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO) and multi-objective invasive weeds optimization (MOIWO)) with the similar solution structure are developed to validate theperformance of the proposedMOVDOalgorithm for solving various instances of the developed model. A Taguchi method is employed to calibrate the main parameters ofthese fouralgorithms. The result of comparing theirperformances based on statistical tests and different measuring metrics reveals that theproposed MOVDO algorithm outperforms remarkably better than other meta-heuristics used in this paper.

Single-machine lot scheduling problem for deteriorating items with negative exponential deterioration rate

Determining production-inventory level is one of the most important and challenging problems in a manufacturing system.Economic Batch Quantity(EBQ) is the simplest and the most employed inventory model in this field. Here, a single-source production lot-sizing model for deteriorating products with negative exponential deterioration rate is developed. In this manufacturing system, we assume that all items are manufactured using a single source. In this paper the optimal values for cycle length and lot size are derived in a way that the total cost (consists of machine setup cost, manufacturing cost, carrying and disposal costs of the deteriorating items) is minimized. Consequently, the developed model for the proposed problem is formulated. According to the derived mathematical model, we could not provide a closed form optimal solution, but we show that there is a unique optimal solution for the cycle length. At first, the upper and lower bounds of the optimal solution are determined, then using a Bi-section method (root-finding method) the problem is solved. To demonstrate the applicability of this method, we solve a simple example for a manufacturing system with three products. Finally, results of ten experiment that obtain from this method compare to answers from Newton-Raphson method, then it has been shown that this method has great effectiveness and less calculation efforts for the single-machine problem with deteriorating items.

ABC Analysis of an Aerospace Business Case on ALM Technologies in Aerospace and Defence Supply Chain

This article provides an analysis of an aerospace business case on additive layer manufacturing (ALM) process. Using activity-based costing (ABC), we can breakdown the cost drivers and understand the cost levers associated with ALM process. This can help us in finding the innovative ways to enhance the ALM process operation’s efficiency. We have considered an aircraft bracket manufacturing using the direct metal laser sintering process as a business case for our ABC analysis on additive layer manufacturing technologies in aerospace and defence supply chain. The additive layer manufactured components associated cost can be divided into design cost, production cost, post-processing cost and qualification cost. Each step in the additive manufacturing process performs at one organisation or across a supply chain based on the various internal and external suppliers. Also, there are overhead costs associated with each step of this additive manufacturing process. This article explains the cost involved in the component design and its relevance to supply chain. It explains the cost involved in the component production such as materials cost, machine set-up cost, machine runtime cost and final build plate removal cost. It also explains the cost involved in the post-processing such as heat-treating cost, machining cost and surface finish cost. The component process cost is required to qualify based on its end use. The component qualification cost is required to add significantly to the component overall cost including the labour cost and computer tomography scan cost. The overhead cost is depending on the individual operation and it assigns these costs appropriately. It computes the ABC for an aerospace bracket and concludes with analysis to make a convinceable business case for the use of innovative technology and to enable to identify the cost of drivers and levers, so it can drive business with more efficiently.

Mathematical model for dynamic cell formation in fast fashion apparel manufacturing stage

This paper presents a mathematical programming model for dynamic cell formation to minimize changeover-related costs (i.e., machine relocation costs and machine setup cost) and inter-cell material handling cost to cope with the volatile production environments in apparel manufacturing industry. The model is formulated through findings of a comprehensive literature review. Developed model is validated based on data collected from three different factories in apparel industry, manufacturing fast fashion products. A program code is developed using Lingo 16.0 software package to generate optimal cells for developed model and to determine the possible cost-saving percentage when the existing layouts used in three factories are replaced by generated optimal cells. The optimal cells generated by developed mathematical model result in significant cost saving when compared with existing product layouts used in production/assembly department of selected factories in apparel industry. The developed model can be considered as effective in minimizing the considered cost terms in dynamic production environment of fast fashion apparel manufacturing industry. Findings of this paper can be used for further researches on minimizing the changeover-related costs in fast fashion apparel production stage.

Modeling of RFID-Enabled Real-Time Manufacturing Execution System in Mixed-Model Assembly Lines

To quickly respond to the diverse product demands, mixed-model assembly lines are well adopted in discrete manufacturing industries. Besides the complexity in material distribution, mixed-model assembly involves a variety of components, different process plans and fast production changes, which greatly increase the difficulty for agile production management. Aiming at breaking through the bottlenecks in existing production management, a novel RFID-enabled manufacturing execution system (MES), which is featured with real-time and wireless information interaction capability, is proposed to identify various manufacturing objects including WIPs, tools, and operators, etc., and to trace their movements throughout the production processes. However, being subject to the constraints in terms of safety stock, machine assignment, setup, and scheduling requirements, the optimization of RFID-enabled MES model for production planning and scheduling issues is a NP-hard problem. A new heuristical generalized Lagrangian decomposition approach has been proposed for model optimization, which decomposes the model into three subproblems: computation of optimal configuration of RFID senor networks, optimization of production planning subjected to machine setup cost and safety stock constraints, and optimization of scheduling for minimized overtime. RFID signal processing methods that could solve unreliable, redundant, and missing tag events are also described in detail. The model validity is discussed through algorithm analysis and verified through numerical simulation. The proposed design scheme has important reference value for the applications of RFID in multiple manufacturing fields, and also lays a vital research foundation to leverage digital and networked manufacturing system towards intelligence.

A tabu search algorithm for the cell formation problem with part machine sequencing

Abstract.This paper presents extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously and part machine utilisation is considered. More specifically, an integration of inter-cell movements of parts and machine set-up costs within the objective function, and also a combination of machine set-up costs associated with parts revisiting a cell when the part machine operation sequence is taken into account are examined and an enhanced model is formulated. Based upon this model’s requirements, an initial three stage approach is proposed and a tabu search iterative procedure is designed to produce a solution. The initial approach consists of the allocation of machines to cells, the allocation of parts to machines in cells and the evaluation of the objective function’s value. Special care has been taken when allocating parts to machine cells as part machine operation sequence is preserved making the system more complex but more realistic. The proposed tabu search algorithm integrates short term memory and an overall iterative searching strategy where two move types, single and exchange, are considered. Computational experiments verified both the algorithm’s robustness where promising solutions in reasonably short computational effort are produced and also the algorithm’s effectiveness for large scale data sets.

Green Production Scheduling/Planning with Parallel-Machine Layout for Multiple Orders and Diversified Due Dates

This paper proposes an Integer Nonlinear Programming (INLP) mathematical model to investigate the practical situations of production scheduling and planning with a parallel-machine layout focusing on multiple orders, diversified delivery deadlines, and multiple types of products. Production cost, machine setup cost, product holding cost, shortage cost, and antipollution cost are also considered in this model to achieve the goal of total cost optimization. This study creates a highly repeated characteristic because the proposed mathematical model and its solving procedure, (Accumulation and Backtrack staged Scheduling Approach, ABSSA) method, are constructed by the packaged software. Therefore, this study has its value for practical applications, and it is suitable for diversified production scheduling as well as the real-time scheduling of production planning. For production scheduling and planning of parallel machines, this study has provided a valuable decision support tool.

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS2O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.

Issues concerning optimization of non-conventional parameters in manufacturing

There are several parameters regarding the manufacture of an item such as machine setup cost, carrying cost, demand and production rate etc. Traditionally, manufacturing batch size or production cycle time is optimized after formulation of the total relevant cost. A discussion is made in the present paper concerning variation of non-conventional parameters such as production rate, on the basis of available literature. Implications related to variation of demand of an item are also mentioned. Focus is on discussion and formulation of the problem. Complete solution methods are not provided. However various available non-linear programming methods may be applied such as univariate method, penalty function method etc. in addition to classical optimization technique.

Fuzzy extensions to Integer Programming models of cell-formation problems in machine scheduling

Cell formation has received much attention from academicians and practitioners because of its strategic importance to modern manufacturing practices. Existing research on cell formation problems using integer programming (IP) has achieved the target of solving problems that simultaneously optimise: (a) cell formation, (b) machine-cell allocation, and (c) part-machine allocation. This paper will present extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously, and also a significant number of constraints together with an enhanced objective function are considered. The main study examines the integration of inter-cell movements of parts and machine set-up costs within the objective function, and also the combination of machine set-up costs associated with parts revisiting a cell when part machine operation sequence is taken into account. The latter feature incorporates a key set of constraints which identify the number of times a part travels back to a cell for a later machine operation. Due to two main drawbacks of IP modelling for cell formation, i.e. (a) only one objective function can be involved and (b) the decision maker is required to specify precisely goals and constraints, fuzzy elements like fuzzy constraints and fuzzy goals will be considered in the proposed model. Overall the paper will not only include an extended and enhanced integer programming model for assessing the performance of cell formation, but also perform a rigorous study of fuzzy integer programming and demonstrate the feasibility of achieving better and faster clustering results using fuzzy theory.

Multiple-objective optimization of machine cell layout using genetic algorithms

A prototype genetic algorithm-based system has been developed to group machines into manufacturing cells, subject to objective functions such as gross part (inter- and intra-cell) movement, cell load variation and machine set-up costs. The system is an improvement over two contemporary techniques because it can handle complex machine groupings with multiple objective functions. In order to assess chromosomes from different populations, a new crossover technique is proposed. The prototype system was evaluated using four machine groupings, namely Models 1, 2, 3 and 4 and three Approaches involving three different objective (fitness) functions. Models 1 and 2 have the twin objective functions of minimizing gross part movement and cell load variation. Models 3 and 4 are Models 1 and 2, respectively, with the minimizing machine set-up cost as the third objective function. In Approach 1, the chromosomes were drawn from two distinct populations, while Approaches 2 and 3 were defined by the authors to handle 3 or more objective functions. A chromosomal performance index, C obj , is defined to evaluate the fitness values of Approaches 2 and 3. It was found that Approach 3 was consistently able to produce sub-optimal solutions in all four models after weights are applied to different objective functions, whereas Approach 2 can perform as well as Approach 3 only if there is a sufficient number of iterations. On the other hand, Approach 2 requires a much shorter computational run time than Approach 3.

Production lot sizing in failure prone two-stage serial systems

We consider lot sizing policies in multi-stage serial production systems with machines prone to failure. The production rates are finite; failure and restoration of unreliable machines follow some stochastic processes with known probability distributions. The total production cost includes order or procurement cost, machine setup cost, restoration cost, and inventory carrying costs for raw material, Work-In-Process (WIP), and finished product. The expected production cost per unit time is minimized by stochastic models. The contributions of this paper are twofold. We analyze the general model for the two-stage lot sizing problem; and analytic formulae are derived for the special production structure. It has been shown that lot sizing policies in the unreliable systems can be smaller or larger than the ones in the classical model. Numerical experiments also indicate that lot sizing decisions in these systems can be counterintuitive, due to the interaction of machine breakdown and repair, and inventory carrying effects. Computational findings also give insight for the operational planning and control of unreliable production systems.

Double band policy for a classof stochastic industrialmanufacturing system in heavy traffic

We consider the problem of controlling a stochastic industrial manufacturing system producing a single product. The demand for this system consists of two components: one is deterministic with constant rale dand the other is stochastic with random demand batches .The manager can decide at any time to turn on or turn oil the machine. He can also decide at any time to purchase any amount of product from outside vendors. The cost of system operation consists of linear holding cost, machine setup cost, fixed order cost and linear varied purchase cost. The objective is to minimize the total expected discounted cost. Under heavy traffic condition, this problem is approximated by an impulse control problem of Brownian motion, which is solved explicitly. Its solution is then converted into a policy for the original system, which turned out to be asymptotically optimal. The resulting policy is characterized by three parameters and is dubbed double band policy. When the inventorv reaches S 0 the machine is turned off and it is turned on again when the inventory decreases to level Q 0 If the inventory goes to zero, the manager will purchase qufrom outside vendor

Integration of operation routing and economic production quantity decisions in batch manufacturing systems

Models for the determination of economic production quantity (EPQ) are always presented in batch manufacturing as if machine setup cost and unit processing cost are easily definable. In a job processing situation where alternate machines exist for each operation and the operations within the job can be sequenced in more than one way, selection of the operation sequence and the machine to use for each operation can significantly affect the EPQ and the total manufacturing cost. The function of process sequencing and routing specification falls in the domain of process planning while the determination of the EPQ is the function of inventory control. These two functions have been generally undertaken in isolation of one another in most manufacturing systems despite the significant influence of the process planning decision on the efficiency of the lot sizing policy. In this paper, a methodology to integrate the operation sequencing and machine routing decisions of the process planner and the work of the inventory control analyst in order to minimize total cost of manufacturing in environment with routing flexibility is presented. The model is sufficiently flexible to deal with many production planning situations in which alternate processes exist. Solution algorithms are developed and computational results based on some sample problems are presented and discussed.

Production scheduling on parallel machines subject to staircase demands

We are given a set of m unrelated machines working in parallel and a set of n independent products which have to be produced on these machines using p additional resources (e.g. transportation facilities, additional equipment, operators, …); α i units of resource 1 are available at any time. A machine cannot work on more than one product at a time but a product can simultaneously be produced on different machines. Each pair (machine, product) requires at most one unit of resource 1 at a time. Production plans are imposed in form of periodic demands for each product. For each triple (machine, product, resource) we are given a productivity per time unit. The objective is to minimize the total penalty following from deviations above and below production plans. The sum of machine setup costs is considered as a secondary performance measure. The problem is solved by an extension of the two phase method [1]; Phase 1 consists in solving an LP problem and Phase 2 is the construction of the schedule which reduces to a sequence of compatible flow problems.