Disassembly leveling and lot sizing for multiple product types: a basic model and its extension

At the end-of-life (EOL) of a product, there are several options available for its processing including reuse, remanufacturing, recycling and disposing. In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as the reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly sequencing be developed. In this paper, we present a genetic algorithm for disassembly sequencing of EOL products. A case example is considered to demonstrate the functionality of the algorithm.

A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality

This paper considers the problem of deciding disassembly order quantity in a disassembly system. Returned end-of-use/life (EOL) products are disassembled to retrieve reusable parts sold to secondary markets. For the problem, we suggest novel push and pull disassembly policy models by extending reverse economic order quantity models. The objective of the models is to minimise the costs of the disassembly setup, the disassembly operations and inventory holding, the scrapping of non-reusable parts, and backorder. We develop optimal equations and algorithms for the models and perform a case study of Korean automobile disassembly systems. We then conduct a series of sensitivity analyses based on real-world and reliable data and discuss possible applications of the push and pull policies. The results show that the pull policy gives better in terms of the total cost while the push policy is less insensitive to the disassembly setup cost.

SUMMARY Traditionally, upon solution, independent demand inventory models result in the determination of a closed form for the economic lot size. Generally, this is obtained from the result that holding costs and setup costs are constant and equal at the optimum. However, the experience of the Japanese indicates that this need not be the case. Specifically, setup cost may be reduced by investing in reduced setup times resulting in smaller lot sizes and increased flexibility. Various authors have investigated the impact of such investment on classical lot sizing formulas which has resulted in the derivation of modified relationships. A common assumption of this research has been that demand and lead time are deterministic. This paper extends this previous work by considering the more realistic case of investing in decreasing setup costs where lead time is stochastic. Closed form relationships for optimal lot size, optimal setup cost, optimal total cost, etc. are derived. Numerical results are presented for...

This paper studies the problem of user association, scheduling and channel allocation in dense cellular networks with energy harvesting base stations (EBSs). In this problem, the EBSs are powered solely by renewable energy and each user has a request for downloading data of certain size with a deadline constraint. The objective is to maximize the number of associated and scheduled users while allocating the available channels to the users and respecting the energy and deadline constraints. First, the computational complexity of this problem is characterized by studying its NP-hardness in different cases. Next, efficient algorithms are proposed in each case. The case of a single channel and a single EBS is solved using two polynomial-time optimal algorithms---one for arbitrary deadlines and a less-complex one for common deadlines. The case of a single channel and multiple EBSs is solved by proposing an efficient constant-factor approximation algorithm. The case of multiple channels is efficiently solved using a heuristic algorithm. Finally, our theoretical analysis is supplemented by simulation results to illustrate the performance of the proposed algorithms.

Lot sizes and setup frequency with learning in setups and process quality

Pricing, relaxing and fixing under lot sizing and scheduling

This paper deals with lot sizing decisions in a flexible job shop manufacturing system considering dependencies between lot sizing decisions. A novel mathematical model is developed to optimise lot sizing and scheduling simultaneously using a product-oriented approach as opposed to the job-oriented approach which has been prevalently considered in the literature. Moreover, the proposed model considers further underlying assumptions such as assembly operations, sequence-dependent setup times, initial inventory and safety stock levels, as well as lots with unequal and variable sizes. The objective is to minimise the total production, setup, and tardiness penalty costs of the system. To solve the formulated problem, a self-adaptive Cuckoo Optimisation Algorithm (COA) embedded with three new immigration mechanisms is developed. Numerical experiments are conducted to demonstrate the validity of the model and investigate the efficiency and effectiveness of the employed optimisation algorithm.

This paper derives a mathematical integrated production inventory model of single vendor and buyer to investigate the effects of capital investment in setup cost of the vendor and order processing cost of the buyer on the decision variables of the model. Main purpose of this study is to minimize the integrated total cost of the vendor and buyer by optimizing the delivery lot size, lead-time, setup and order processing cost and number of deliveries per order simultaneously while lead-time demand follows normal distribution and unknown distribution, i.e., distribution free case. This model is analyzed by calculus method and justified with appropriate numerical illustrations. Numerical results show that significant savings can be achieved at optimal investments in reducing lead time, ordering cost and setup cost. Finally, sensitivity analysis of the key parameters is carried out and some managerial implications are also included.

In manufacturing industries, production planning and scheduling strategy usually flow in a hierarchical direction. In this direction, the production planning problem is solved first; then the scheduling problem is solved to meet the production targets. This often generates an infeasible production plan because of not considering the details of scheduling. Therefore, it is necessary to develop models that can integrate production planning and scheduling. For manufacturing companies that have identical units of single products and are often grouped in production batches (lots), lot streaming can be used as a scheduling technique. However, there has been no model to integrate production planning (lot sizing) and scheduling, using lot streaming technique which can accelerate production. The main objective of this research is to develop mixedinteger mathematical models for integration of lot sizing and flowshop lot streaming problems such as variable sublots, consistent and equal sublots, scheduling with learning effects and the possibility of preventive maintenance tasks. The objective of these mathematical models is minimization of total costs and also, five goals of problem can simultaneously be solved, namely: determining the sequence among sublots, optimal number of sublots for each lot, size of each lot, inventory levels and size of individual sublots. The second objective of this research is to propose a solution procedure for problems when data are fuzzy. Finally, the third research objective is to validate the proposed model through a case study. Three software are used to extract the results of mathematical models and validate the solution procedure. The name of these software are: LINGO, MINITAB and MATLAB. In this research, the author proposes the first mixed-integer mathematical models for integration of lot sizing and lot streaming problems. By these proposed models, not only sequencing and timing decisions of multiple products are calculated but also lot size of each product, work-in-process and inventory levels of finished products are calculated when lots can be split into smaller sublots. To get the results of mathematical models, in three examples, 70 randomlygenerated problems are solved by LINGO solver. Moreover, a two-way ANOVA test as a statistical method is applied to validate the mathematical model, using four examples consisting sixteen problems, by MINITAB software. The NDM Company is used as a case study. The mathematical models are used to solve NDM company’s problems by LINGO solver. The results showed 32 percent (77.66 hours) reduction in makespan compared to non-integrated mathematical model. Validation of the proposed solution procedure is achieved by comparison of results with max-min method results,using Shahab Shishe company data. The results of using the proposed mathematical models in this research are first to reduce cost by using these proposed models. Secondly is greater marginal benefits were obtained by intermingled sublot cases than non-intermingled sublot cases. It is concluded that better makespans were obtained by intermingled sublot cases than non-intermingled sublot case.

A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem

Towards autonomous adaptive disassembly of permanent-magnet synchronous motors with industrial robots

The planning of purchasing and production lot-size for multi-item multi-period multi-workstation capacitated production (MLCLSP-M) is discussed. Since the model is a NP-hard problem, this research proposes a heuristic method (A-LS), which decomposes the model into two sub-problems and iteratively solves them. The two sub-problems consisting of an assignment sub-problem with given lot size and a lot sizing sub-problem with given assignment. Solution method was developed, tested and compared to illustrate the solution quality and computational time. Mathematical models for both sub-problems were solved with AMPL/CPLEX 8.0.0 solver. The computational results indicate that the proposed heuristic (A-LS) gives satisfactory solution with faster solving time comparing with the original mathematical model.

Economic lot sizes and vehicle scheduling

In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and sequence-dependent setup time in realistic situations is presented. Since few works have been implemented in this field, the necessity of finding better solutions is a motivation to extend heuristic or meta-heuristic algorithms. This type of production system is found in industries such as food processing, chemical, textile, metallurgical, printed circuit board, and automobile manufacturing. A mixed integer linear programming (MILP) model is proposed to minimize the makespan. Since this problem is known as NP-Hard class, a meta-heuristic algorithm, named Genetic Algorithm (GA), and three heuristic algorithms (Johnson, SPTCH and Palmer) are proposed. Numerical experiments of different sizes are implemented to evaluate the performance of presented mathematical programming model and the designed GA in compare to heuristic algorithms and a benchmark algorithm. Computational results indicate that the designed GA can produce near optimal solutions in a short computational time for different size problems.