Pisal Yenradee

PSO-based algorithm for home care worker scheduling in the UK

This paper presents the novel application of a collaborative population-based meta-heuristic technique called Particle Swarm Optimization (PSO) to the scheduling of home care workers. The technique is applied to a genuine situation arising in the UK, where the provision of community care service is a responsibility of the local authorities. Within this provision, optimization routes for each care worker are determined in order to minimize the distance traveled providing that the capacity and service time window constraints are not violated. The objectives of this paper are twofold; first to exploit a systematic approach to improve the existing schedule of home care workers, second to develop the methodology to enable the continuous PSO algorithm to be efficiently applied to this type of problem and all classes of similar problems. For this problem, a particle is defined as a multi-dimensional point in space which represents the corresponding care activities and assignment priority. The Heuristic Assignment scheme is specially designed to transform the continuous PSO algorithm to the discrete job schedule. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), i.e. insertion and swap, are embedded in the PSO algorithm in order to further improve the solution quality. The proposed methodology is implemented, tested, and compared with existing solutions on a variety of real problem instances.

Aggregate production planning with workforce transferring plan for multiple product types

This paper presents the aggregate production planning for multiple product types where the worker resource can be transferred among the production lines. A mathematical model was formulated in spreadsheet format. Then the spreadsheet-solver technique was used as a tool to solve the model. A real situation of a manufacturing company was selected as a case study. The actual data was used to test and validate the proposed model. The optimal aggregate production plan provides the information on managing the available production capacity together with the useful workforce transferring plan. The obtained solutions were compared to those of another approach where the workers cannot be transferred among the production lines. The total cost is significantly reduced when the workers are allowed to transfer among the production lines.

Development of finite capacity material requirement planning system for assembly operations

A conventional material requirement planning (MRP) logic considers infinite capacity of machines. A planner must manually solve capacity problems (if any) after the material requirement plan has been generated. This paper tries to alleviate the capacity problems by developing a finite capacity material requirement planning (FCMRP) system for assembly operations. The FCMRP system is capable of automatically allocating some jobs from one machine to another and adjusting timing of the jobs considering a finite available time of all machines. The FCMRP system has been tested using real data from a steering-wheel factory. Effects of options – namely, scheduling, rearranging and adjusting options in the FCMRP system on the performance measures – are statistically analysed. The FCMRP system can reduce required overtime if the jobs are allowed to be completed earlier than the due dates. The required overtime can be further reduced if the jobs are allowed to be completed later than the due dates.

Modified drum–buffer–rope scheduling mechanism for a non-identical parallel machine flow shop with processing-time variation

The drum–buffer–rope (DBR) is a scheduling mechanism under the Theory of Constraints (TOC) philosophy. In DBR, ‘drum’ is a production schedule on the capacity-constrained resources (CCRs), which controls the speed of production for the whole system; ‘rope’ is a mechanism to release the required material to the CCRs; and ‘buffer’ is used to protect the CCRs from starvation due to statistical fluctuations. For a non-identical parallel machine flow-shop environment, estimating an efficient rope and time buffer for DBR implementation is not an easy task because of the complexity of non-identical parallel machine loading. This paper proposes a new scheduling method, which is called the modified DBR (MOD-DBR). It applies a backward finite capacity scheduling technique, including machine loadings and detail scheduling, instead of the rope mechanism in DBR. The scheduling performances of MOD-DBR are evaluated under variable processing time situations. The experimental results indicate that the MOD-DBR without a time buffer outperformed the DBR with a considerable level of buffer on the average flow time, while they have the same performance on tardiness, constraint resource utilization, and throughput.

A finite capacity material requirements planning system

In a discrete parts manufacturing environment, material requirement planning (MRP) is carried out without considering the manufacturing resource capacity. As a result, during implementation, adjustments in planned orders may become necessary. This paper presents a finite capacity material requirements planning algorithm (FCMRP) to obtain capacity-based production plans. These plans need no costly adjustments to satisfy the capacity constraints. Performance of the FCMRP system, when studied through a set of test examples, has been found to be superior to the existing MRP system.

Application of optimized production technology in a capacity constrained flow shop: a case study in a battery factory

Abstract An application of the OPT principles and some simple scheduling rules without the sophisticated OPT software in a four-stage capacity constrained flow shop is demonstrated by using a medium-sized battery factory as a case study. The throughput and inventory performances of OPT policy are evaluated and compared with those of push and pull policies by using a simulation study. The performances of OPT are attractive. Although, the OPT policy has relatively low inventory level, it can still maintain relatively high throughput rate.

Implementation sequence of engineering and management techniques for enhancing the effectiveness of production and inventory control system

This paper discusses a methodology to determine an appropriate sequence for implementing engineering and management (E&M) techniques for enhancing the effectiveness of the production and inventory control (P&IC) system. Firstly, direct relationships among E&M techniques are analysed using the fuzzy interpretive structural modelling (FISM) approach. A procedure based on a max-min fuzzy composition of the direct relationships is also applied to determine indirect relationships among E&M techniques. Then, an appropriate implementation sequence of these E&M techniques is determined based on the driver power and dependence scores that are derived from the overall direct and indirect relationships among these techniques.

Performance of TOC based finite capacity material requirement planning system for a multi-stage assembly factory

This paper aims to develop a finite capacity material requirement planning (FCMRP) system based on TOC philosophy (TOC-MRP) for multi-stage assembly factory that has some bottleneck stations. The proposed TOC-MRP system tries to load and schedule operations on bottleneck stations in a manner that they are free of idle time and overtime. The schedules on non-bottleneck stations will be arranged until they are not conflicting with those on the bottleneck stations. The non-bottleneck stations are allowed to have idle time and overtime if necessary. To analyse whether TOC-MRP is effective, it is compared with a FCMRP method that does not adopt TOC philosophy. The experimental results reveal that the TOC-MRP outperforms the FCMRP without TOC philosophy.

Optimal supply chain network design with process network and BOM under uncertainties

New model for manufacturing supply chain design is proposed.The model combines process network and bill of materials with some fuzzy data.Fewer variables and easier to interpret the result than the FLP based models.A real case study is used to demonstrate the applicability of the proposed model. Design of supply chain network significantly affects supply chain performance for long period. Since each industry has a unique set of characteristics which evidently drive the design supply chain network, a number of various models have been formulated to meet the needs of such business contexts. Even though many models have been proposed for manufacturing industry context, most of them are based on the facility location model. It tends to lead the supply chain network design model to be complicated. Therefore, the purpose of this research is to propose an alternative approach to formulate manufacturing network design problem. Features, such as multi-echelon, multi-commodity, products structure, and manufacturing process, are taken into consideration as characteristics of the studied environment. Moreover, uncertainty factors are also integrated to the model by employing possibilistic theory. Eventually in addition to the methodology, a case study in a consumer product firm is used to demonstrate applicability of the proposed method. Two models, deterministic and fuzzy models, have been explored in the study and both of them have demonstrated the validity of the proposed formulation method. Moreover, it is shown that the fuzzy model has outperformed its deterministic counterpart in term of cost effectiveness.

A Comment on the Formulation of an Aggregate Production Planning Problem

Conventionally, a revenue function, a cost function and a profit function are selected to be the objective function for aggregate production planning (APP) problems. The theory of constraints (TOC) alternative consideration argues that instead of measuring by cost, factory should evaluate their performance by throughput. Even though, there are a lot of research works on formulations of APP problems, there has been no investigation, which formulation is the most appropriate for APP problems. In this research, the investigation of the formulation of existing APP problems is done. In order to clarify the difference of each objective function, a simple case study has been used to compare the performances of the APP problem with revenue, cost, and profit objective functions when resource constraints (limited processing time) are not included and included in the model. For the profit objective function, two formulations are also compared: profit objective function by TOC and profit objective function by linear programming. From the results, it can be shown that setting the objective function of an APP problem is very important because it may lead to a wrong decision in production planning