A Neighborhood Search for Sequence-dependent Setup Time in Flow Shop Fabrics Making of Textile Industry

Abstract This paper proposes a neighborhood search to solve scheduling for fabrics making in a textile industry. The production process consists of three production stages from spinning, weaving, and dyeing. All stages have one processor. Setup time between two consecutive jobs with different color is considered. This paper also proposes attribute’s decomposition of a single job to classify available jobs to be processed and to consider setup time between two consecutive jobs. Neighborhood search (NS) algorithm is proposed in which the permutation of set of jobs with same attribute and the permutation among set of jobs is conducted. Solution obtained from neighborhood search, which might be trapped in local solution, then is compared with other known optimal methods

Production Scheduling with Capacity-Lot Size and Sequence Consideration

The General Lot-sizing and Scheduling Problem (GLSP) is a common problem found in continuous production planning. This problem involves many constraints and decisions including machine capacity, production lot-size, and production sequence. This study proposes a two-phase algorithm for solving large-scale GLSP models. In Phase 1, we generate patterns with a specific batch size and capacity and in Phase 2, based on the patterns selected in Phase 1, we optimize the production allocation. Additionally, the external supplies are included in the formulation to reflect the real situation in business with limited resources. In this work, the justification of the formulation is based on the ability of solving and calculation time. The proposed formulation was tested on eight scenarios. The results show that the proposed formulation is more tractable and is easier to solve than the GLSP.The General Lot-sizing and Scheduling Problem (GLSP) is a common problem found in continuous production planning. This problem involves many constraints and decisions including machine capacity, production lot-size, and production sequence. This study proposes a two-phase algorithm for solving large-scale GLSP models. In Phase 1, we generate patterns with a specific batch size and capacity and in Phase 2, based on the patterns selected in Phase 1, we optimize the production allocation. Additionally, the external supplies are included in the formulation to reflect the real situation in business with limited resources. In this work, the justification of the formulation is based on the ability of solving and calculation time. The proposed formulation was tested on eight scenarios. The results show that the proposed formulation is more tractable and is easier to solve than the GLSP

Modeling Industrial Lot Sizing Problems: A Review

In this paper we give an overview of recent developments in the field of modeling single-level dynamic lot sizing problems. The focus of this paper is on the modeling various industrial extensions and not on the solution approaches. The timeliness of such a review stems from the growing industry need to solve more realistic and comprehensive production planning problems. First, several different basic lot sizing problems are defined. Many extensions of these problems have been proposed and the research basically expands in two opposite directions. The first line of research focuses on modeling the operational aspects in more detail. The discussion is organized around five aspects: the set ups, the characteristics of the production process, the inventory, demand side and rolling horizon. The second direction is towards more tactical and strategic models in which the lot sizing problem is a core substructure, such as integrated production-distribution planning or supplier selection. Recent advances in both directions are discussed. Finally, we give some concluding remarks and point out interesting areas for future research

Industrial insights into lot sizing and schedulingmodeling

© 2015 Brazilian Operations Research Society. Lot sizing and scheduling by mixed integer programming has been a hot research topic inthe last 20 years. Researchers have been trying to develop stronger formulations, as well as to incorporatereal-world requirements from different applications. This paper illustrates some of these requirements anddemonstrates how small- and big-bucket models have been adapted and extended. Motivation comes fromdifferent industries, especially from process and fast-moving consumer goods industries

A priori reformulations for joint rolling-horizon scheduling of materials processing and lot-sizing problem

In many production processes, a key material is prepared and then transformed into different final products. The lot sizing decisions concern not only the production of final products, but also that of material preparation in order to take account of their sequence-dependent setup costs and times. The amount of research in recent years indicates the relevance of this problem in various industrial settings. In this paper, facility location reformulation and strengthening constraints are newly applied to a previous lot-sizing model in order to improve solution quality and computing time. Three alternative metaheuristics are used to fix the setup variables, resulting in much improved performance over previous research, especially regarding the use of the metaheuristics for larger instances

Numerical Analyses and Integration of Split Lot Sizing Using Lean Benchmark Model for Small Lot Manufacturing in High Mix Low Volume Production

As the global demand for automobiles has increased rapidly over the last fifty years, customers have become more particular about the characteristics of the autos they want. This change in demand, in part has pushed manufacturing to become more flexible and created a demand for alternative, more efficient processes like the High Mix Low Volume (HMLV) production of vehicles. During HMLV, manufacturers create production lot sizes and schedule to synchronize the production processes to meet customer demand on time. The demand for the automobile parts may not be uniform or parts may not be consumed by the customer immediately, Due to this variation in demand, companies avoid shortages by large production lots and storing excess inventory. However, excess inventory has to be managed differently during the production large lots. It increases the inventory holding cost; hence it is essential to know what, when and how much to produce. An excellent example of introducing controls for efficiencies is the Toyota Production System, which allows Toyota Motors to progress implement Just in Time (JIT) production, However, to achieve the JIT, needs for producing small lots have to be met. Hence, this thesis aims to assess a lot-sizing model that focuses on how to combine the production methods of high to low demand parts one machine to achieve JIT. The method was divided in two parts; first, it assesses the variable production of high to medium demand parts within a fixed amount of time described as Fixed Period Variable Amount (FPVA). The split lot technique used to minimize the inventory. Second, parts that have assess low demand were assessed within a Fixed Amount Variable Period (FAVP). It is proposed that a time-oriented method with the external changeover parameter can appropriately minimize the inventory of FAVP parts and avoid idling of the workforce. Also discussed the kaizen or continuous improvement approach for changeover with directed sequencing approaches to minimize longer changeover times, significant obstacle for the production of small lot production. The outcome of the propose model is then compared with two industry lot sizing and scheduling models, conventional lot sizing and lean benchmark lot sizing. The objective of conventional model is to minimize the cost without considering and HMLV environment and external changeover parameter. The objective of the lean benchmark model is to minimize inventory without creating idle time for the workforce. The thesis also investigates the integration and working of the Kanban scheduling system in the lean benchmark mode