A TSSA algorithm based approach to enhance the performance of warehouse system

In this plethora of increased competitiveness and globalization the effective management of the warehouse system is a challenging task. Realizing that proper scheduling of the warehouses is necessary to outperform the competitors on cost, lead time, and customer service basis (Koster, 1998); the proposed research focuses on optimization of warehouse scheduling problems. This research aims to minimize the total tardiness so that the overall time involved in managing the inventory inside the warehouse could be effectively reduced. This research also deals with the vehicle routing issues in the warehousing scenario and considers various constraints, and decision variables, directly influencing the undertaken objective so as to make the model more realistic to the real world environment. The authors have also proposed a hybrid tabu sample-sort simulated annealing (TSSA) algorithm to reduce the tardiness as well as to enhance the performance of the warehousing system. The proposed TSSA algorithm inherits the merits of the tabu search and sample-sort annealing algorithm. The comparative analysis of the results of the TSSA algorithm with other algorithms such as simulated annealing (SA), tabu search (TS), and hybrid tabu search algorithms indicates its superiority over others, both in terms of computational time as well as total tardiness reduction

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Imprecise WareHouse Space in Aggregate Production Planning Using Fuzzy Goal Programming

Considering the fluctuating market demands with variable storage capacity and available production capacity, this study examines a number of workable techniques for modeling multiproduct aggregate production planning problems with fuzzy numbers. The suggested method makes use of factors including; inventory levels, labor levels, overtime, backordering levels, workforce capacity, machine capacity, and fuzzy warehouse capacity in an effort to reduce operating costs, reduce production waste, and increase capacity utilization rate. With the aid of this formulation and interpretation, a fuzzy multiproduct aggregate production planning model is developed. Finally, the study's conclusions were arrived at using information provided by Rich Pharmaceuticals Ltd. using Lingo version 18 software (RPL).and it uses parametric programming, best balancing, and interactive techniques to give solutions that can be adjusted to fit a variety of decision-making circumstances

Building Decision Support Systems in Excel for Production and Distribution Planning: A Case Study

We develop a decision support system in Microsoft Excel that integrates production and distribution for a manufacturer of natural fiber-based products in North America. The production and distribution of the company’s products were optimized using a linear programming model, implemented in Excel. The spreadsheet dynamically adjusts the formulation to reflect the user’s current requirements, solves the optimization model in the background, and generates detailed managerial reports. In addition, it allows users to conduct what-if analyses by varying the number of plants and warehouses. It demonstrates the ability of a Linear Programming Model run on an Excel platform to provide the firm with an optimized production plan resulting in significant, cost savings since implementation

PEMODELAN FIXED TIME PERIOD UNTUK SISTEM PENGENDALIAN PERSEDIAAN PADA GUDANG REGIONAL

Inventory control modelling is utilized to optimize warehousing cost and the availability of product and raw material while keeping customer satisfaction level. A formulation of fuzzy time series based on raw material planning for  fixed-time period safety stock model was proposed to enhance forecasting accuracy in limited  data availability.  Safety stock levels were computed in monthly fashion to optimizing the most appropriate capacity in each regional warehouse. Forecasting accuracy was based on MAPE indicator. By deploying forecasted data,  the   calculation of product quantity and capacity level were set to correspond  with the target of product box quantity to produce. The result showed that the forecasting of six products in four regions showed the range of  MAPE values with  minimum at 0.29% and maximum at 1.94% level. The implementation of formula on a real field application had increased the  data flow transaction and mobility amount among stakeholders. Keywords: formulation, fuzzy tim series forecasting, inventory control, modelling, MAP

A novel approach for sustainable supply Chain management with analyzing the effective governance under fuzzy uncertainty

Nowadays, knowledge has become one of the most important tools of power, distributing public services that accept the audience as citizens and not consumers and provide the principle of services without financial worries. Moreover, the urban products have a specific production and distribution channel that should be assessed. In this research, a mathematical framework is proposed for designing the supply chain network of urban products. The main contribution of this research is to incorporate the effect of public service into urban products' supply chain planning. In this regard, a mixed-integer mathematical model is proposed. In this mathematical model, an attempt is made to minimize the costs of the product distribution system by considering the effects of production, maintenance, and distribution. Moreover, fuzzy uncertainty has been applied to adapt the mathematical model to real conditions. The numerical results show that if manufacturers and distributors want to strengthen their institutions and maintain their leadership roles as in the past, they can optimize their distribution network structure to achieve the best possible performance. Moreover, technological advances and innovations in production and distribution systems can create a huge leap in profitability

Analytical and economic methodology for storage of large heavyweight equipment in industrial processes

Numerous studies concerning warehouse-design methodologies have been performed focused on the storage of products on pallets or of intermediate size and/or moderate weight loads. These studies, however, do not provide with optimal results for industries that work with equipment or objects of uncommon sizes and shapes and with large weights, which are difficult to move and involve high costs and complex operational actions, affecting to the production processes and interfering with the logistic processes or the supply-chain of a company. This study proposes an analytical methodology using economic and technical qualitative criteria that can be applied specifically to large and heavy equipment warehouses. Both quantitative aspects, such as availability and cost of space, and also qualitative considerations, such as flexibility requirements, impact on manufacturing process and risks associated, are evaluated. To determine an optimum implementation solution, several decision-making methods, such as Electra I & II and Analytic Hierarchy Process are employed with due consideration of multiple criteria. The results obtained are modulated and reinforced using a SWOT (strengths-weaknesses, opportunities- threats) and a Risk analysis to verify this single ultimate solution. The said process led to the establishment of a decision-making methodology suitable for any organization possessing large-scale storage systems