Bilge Bilgen

Strategic tactical and operational production-distribution models: a review

The concept of supply chain management is gaining so much importance that the firms can compete in todays global economy. This paper provides a detailed literature survey of previous research on supply chain management literature at strategic, tactical, operational levels and reverse logistics, but we limited our research only to the models developed for production and distribution problems. We scrutinise the previous reviews in order to distinguish our research from the others. In the light of these previous reviews, we have developed our classification scheme. The models reviewed in this research have been classified in terms of the solution methodology used. These are: optimisation-based models, metaheuristic-based models, information technology (IT)-driven models and hybrid models. The objective is to develop a framework for the existing literature to reveal major trends in the literature and to explore research opportunities in this area.

Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem

An efficient integration of production and distribution plans into a unified framework is critical to achieving competitive advantage. This paper addresses the production and distribution planning problem in a supply chain system that involves the allocation of production volumes among the different production lines in the manufacturing plants, and the delivery of the products to the distribution centers. An integrated optimization model for production and distribution planning is proposed, with the aimed of optimally coordinating important and interrelated logistics decisions. However, a real supply chain operates in a highly dynamic and uncertain environment. Therefore, this model is transformed into fuzzy models taking into account the fuzziness in the capacity constraints, and the aspiration level of costs using different aggregation operators. The applicability and flexibility of the proposed models are illustrated through a case study in consumer goods industry.

Integrated production and distribution planning in the fast moving consumer goods industry: a block planning application

In the fast moving consumer goods industry there is an ongoing trend towards an increased product variety and shorter replenishment cycle times. Hence, manufacturers seek a better coordination of production and distribution activities. In this paper, a so-called block planning approach is presented which establishes cyclical production patterns based on the definition of setup families. For the delivery of final goods from the plants to distribution centres two transportation modes are considered, full truckload and less than truckload. The proposed mixed-integer linear optimization model minimizes total production and transportation costs. Numerical results demonstrate the practical applicability of the proposed block planning approach. In particular, a rigid and a flexible block planning approach are compared which differ by their degree of flexibility in the scheduling of the production lots.

Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling

In this paper we address the production scheduling and distribution planning problem in a yoghurt production line of the multi-product dairy plants. A mixed integer linear programming model is developed for the considered problem. The objective function aims to maximize the benefit by considering the shelf life dependent pricing component and costs such as processing, setup, storage, overtime, backlogging, and transportation costs. Key features of the model include sequence dependent setup times, minimum and maximum lot sizes, overtime, shelf life requirements, machine speeds, dedicated production lines, typically arising in the dairy industry. The model obtains the optimal production plan for each product type, on each production line, in each period together with the delivery plan.The hybrid modelling approach is adopted to explore the dynamic behavior of the real world system. In the hybrid approach, operation time is considered as a dynamic factor and it is adjusted by the results of the simulation and optimization model iteratively. Thus, more realistic solutions are obtained for the scheduling problem in yoghurt industry by using the iterative hybrid optimization-simulation procedure. The efficiency and applicability of the proposed model and approach are demonstrated in a case study for a leading dairy manufacturing company in Turkey.

Project selection through fuzzy analytic hierarchy process and a case study on Six Sigma implementation in an automotive industry

Six Sigma is viewed as a systematic, scientific, statistical and smarter approach for management of innovation and focuses on establishing world class business performance. The main identifiers and supreme features of Six Sigma amongst other improvement techniques are: its rich ground which covers many customer oriented and problem solving techniques and its scientific methodology which is based on statistics. One of the most important factors of achieving success is selection of the right Six Sigma projects. This article presents a case study in which both Six Sigma project is selected and Six Sigma methodology is adopted to reduce the energy cost by the optimisation of material transferring heat loss in an automotive supplier industry. To cope with ambiguity and vagueness in the Six Sigma project selection problem, the Fuzzy Analytic Hierarchy Process has been used. This article also describes how various tools and techniques are employed in the different phases within the Six Sigma methodology and how the improvement actions are implemented. In conclusion, the key benefits and experience gained from this project are emphasised.

Multi-bucket optimization for integrated planning and scheduling in the perishable dairy supply chain

This paper considers a dairy industry problem on integrated planning and scheduling of set yoghurt production. A mixed integer linear programming formulation is introduced to integrate tactical and operational decisions and a heuristic approach is proposed to decompose time buckets of the decisions. The decomposition heuristic improves computational efficiency by solving big bucket planning and small bucket scheduling problems. Further, mixed integer linear programming and constraint programming methodologies are combined with the algorithm to show their complementary strengths. Numerical studies using illustrative data with high demand granularity (i.e., a large number of small-sized customer orders) demonstrate that the proposed decomposition heuristic has consistent results minimizing the total cost (i.e., on average 8.75% gap with the best lower bound value found by MILP) and, the developed hybrid approach is capable of solving real sized instances within a reasonable amount of time (i.e., on average 92% faster than MILP in CPU time).

Mixed integer programming based heuristics for the Patient Admission Scheduling problem

Abstract The Patient Admission Scheduling (PAS) problem is a combinatorial optimization problem where elective patients are automatically assigned to beds for the duration of their stay considering not only the medical necessity but also the patient preferences. Due to its combinatorial nature, solving the previously published problem instances to optimality is a difficult task. In this paper, we present two Mixed Integer Programming (MIP) based heuristics namely Fix-and-Relax (F&R) and Fix-and-Optimize (F&O) where PAS problem instances are decomposed into sub-problems and then the sub-problems are optimized. Our approach uses patient decomposition considering patient length-of-Stay (LoS), room preference, admission date, specialism choice, and age, as well as time decomposition considering different optimization window sizes. Quick solutions generated by F&R heuristic are used as an input to the F&O heuristic and are improved in an iterative nature. Main goal of the study is to provide high quality solutions in shorter run times. Computational findings show that the proposed heuristics provide promising results towards the solution of the problem in faster CPU times than the previously reported values where less than 15 percent gap from the best known solutions is achieved for most of the test instances, and as low as 5 percent gap for some of the sample data.

Supply chain network modeling in a golf club industry via fuzzy linear programming approach

This paper addresses a supply chain network planning problem faced by a company that manages a golf club supply chain. The supply chain network is considered for the production of several products through an assembly of three modules supplied by different suppliers. A linear programming model is proposed to describe the problem, where multiple components can be assembled within a planning horizon of one to twelve months. The model involves the supply of various components from a set of suppliers and allocation of assembled products to customers. In real world problems practical situations are often not well-defined and thus can not be described precisely. Therefore this model is transformed into the fuzzy models with flexibility in the objective function, in the market demand, and in the available capacity of resources. The main objective is to construct a procurement-assembly plan enabling it to minimize its costs while satisfying the customers’ demand over a given planning horizon in such a way as to hedge against uncertainty. Finally, the proposed model is tested using a case of supply chain from the golf club industry. Sensitivity analyses are also conducted on various parameters to gain insight into the proposed model. Numerical case demonstrates that the proposed fuzzy model can provide a better and more flexible way of representing the problem for the considered industry.

Possibilistic Linear Programming in Blending and Transportation Planning Problem

This paper presents a possibilistic linear programming model for solving the blending and multi-mode, multi-period distribution planning problem with imprecise transportation, blending and storage costs. The solution procedure uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs, minimizing the risk of obtaining higher total costs. An illustration with a data set from a realistic situation is included to demonstrate the effectiveness of the proposed model.

Distribution Planning Problem: A Survey

In this paper we present a review for the distribution planning problem. We posi- tion the contribution in a framework that classifies the related research in terms of decision levels. These are strategic, tactical and operational levels. We also tabulate the reviewed re- search in terms of model types, model characteristics, solution procedures and the decision levels.