Economic lot-sizing problem with remanufacturing option: complexity and algorithms

In a single item dynamic lot-sizing problem, we are given a time horizon and demand for a single item in every time period. The problem seeks a solution that determines how much to produce and carry at each time period, so that we will incur the least amount of production and inventory cost. When the remanufacturing option is included, the input comprises of number of returned products at each time period that can be potentially remanufactured to satisfy the demands, where remanufacturing and inventory costs are applicable. For this problem, we first show that it cannot have a fully polynomial time approximation scheme. We then provide a polynomial time algorithm, when we make certain realistic assumptions on the cost structure.

Risk analysis for lot-sizing and maintenance optimization problem under energy constraint with subcontractor solution

PurposeThe main contribution of this manuscript is to suggest new approaches in order to deal with dynamic lot-sizing and maintenance problem under aspect energetic and risk analysis. The authors introduce a new maintenance strategy based on the centroid approach to determine a common preventive maintenance plan for all machines to minimize the total maintenance cost. Thereafter, the authors suggest a risk analysis study further to unforeseen disruption of availability machines with the aim of helping the production stakeholders to achieve the obtained forecasting lot-size plan.Design/methodology/approachThe authors tackle the dynamic lot-sizing problem using an efficient hybrid approach based on random exploration and branch and bound method to generate possible solutions. Indeed, the feasible solutions of random exploration method are used as input for branch and bound to determine the near-optimal solution of lot-size plan. In addition, our contribution to the maintenance part is to determine the optimal common maintenance plan for M machines based on a new algorithm called preventive maintenance (PM) periods means.FindingsFirst, the authors have funded the optimal lot-size plan that should satisfy the random demand under service level requirement and energy constraint while minimizing the costs of production and inventory. Indeed, establishing a best lot-size plan is to determine the appropriate number of available machines and manufactured units per period. Second, for risk analysis study, the solution of subcontracting is proposed by specifying a maximum cost of subcontractor in the context of a calling of tenders.Originality/valueFor maintenance problem, the originality consists in regrouping the maintenance plans of M machines into only one plan. This approach lets us to minimize the total maintenance cost and reduces the frequent breaks of production. As a second part, this paper contributed to the development of a new risk analysis study further to unforeseen disruption of availability machines. This risk analysis developed a decision-making system, for production stakeholders, in order to achieve the forecasting lot-size plan and keeps its profitability, by specifying the unit cost threshold of subcontractor in the context of a calling of tender.

ANALISA PERENCANAAN PERSEDIAAN BAHAN BAKU PRODUK GEAR DENGAN MENGGUNAKAN METODE DYNAMIC LOT SIZING DI PT TJOKRO BERSAUDARA

Perencanaan jumlah persediaan yang akan dimiliki perusahaan merupakan salah satu ma-salah yang sering dihadapi oleh perusahaan. Terutama adalah ketika perencanaan persedi-aan bahan baku merupakan faktor penting yang dapat menunjang proses produksi perus-ahaan maupun membantu memenuhi permintaan konsumen. Pada PT Tjokro Bersaudara besi as merupakan bahan baku utama dengan presentase sebesar 55%. Sedangkan besi plat penggunaan nya hanya sebesar 45% dari semua produk yang dihasilkan. Pada besi as menggunakan S45c diameter 105mm dan pada besi plat dibedakan berdasarkan ketebalannya, mulai dari ketebalan 1mm; 2mm; dan 3mm. banyaknya jenis besi dan uku-ran yang digunakan oleh perusahaan perlu pengelolaan bahan baku sebaik mungkin. Teknik Dynamic lot sizing merupakan teknik yang digunakan untuk menentukan jumlah item yang harus dipesan dengan meminimalkan biaya yang dikeluarkan. Biaya yang berkaitan dengan lot sizing adalah biaya awal dan biaya simpan. Biaya awal merupakan biaya yang dikeluarkan untuk memesan bahan baku ke supplier. Sedangkan biaya simpan merupakan biaya yang dikeluarkan untuk penyimpanan bahan baku. Dari hasil penelitian didapat biaya bahan baku metode perusahaan sebesar Rp.35.619.030,- sedangkan biaya bahan baku dengan metode Dynamic Lot Sizing sebesar Rp.33.159.734,-. Metode Dynamic Lot Sizing menghasilkan penghematan biaya sebesar Rp. 2.459.296.- atau 6,9%. Jumlah produk pemesanan untuk bulan November 2019 sampai dengan Oktober 2020 yaitu untuk bahan baku As S45c sebesar 330 unit, untuk bahan baku plat 1mm sebesar 275 lembar, untuk bahan baku plat 2mm sebesar 301 lembar dan untuk bahan baku plat 3mm sebesar 189 lembar, dengan rincihan pemakaian untuk pembuatan 1 produk Gear dalam Bill Of Material adalah As S45c sebesar 0,7 meter, Plat 1mm sebesar 0,3 meter, Plat 2mm sebesar 0,45 meter, Plat 3mm sebesar 0,5 meter, dengan total biaya bahan baku sebesar Rp. 33.763.052.

Solving multi-objective master production schedule problem using memetic algorithm

<span>A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (SA). This paper proposes a memetic algorithm to solve multi-objective master production schedule (MOMPS). The proposed memetic algorithm combines the evolutionary operations of MA (such as mutation and Crossover) with local search operators (swap operator and inverse movement operator) to improve the solutions of MA and increase the diversity of the population). This algorithm has proved its efficiency in solving MOMPS problems compared with the genetic algorithm and simulated annealing. The results clearly showed the ability of the algorithm to evaluate properly how much, when and where extra capacities (overtime) are permitted so that the inventory can be lowered without influencing the level of service. </span>