Elif Akçali

Quantitative models for inventory and production planning in closed-loop supply chains

The increased popularity of remanufacturing requires firms to rethink their supply chain systems and explore new avenues for the coordination of forward and reverse supply chains to transform existing supply chain systems into closed-loop supply chain (CLSC) systems. Several issues encountered in remanufacturing create unique operational challenges that must be taken into account in specifying guidelines for inventory and production planning (I&PP) for CLSC systems. These challenges provide a rich context for novel optimisation problems. Consequently, the literature on I&PP for CLSC systems has been growing continually. In this paper, we review the existing quantitative literature on I&PP for CLSC systems. We broadly categorise the present work into deterministic and stochastic problems according to the modelling of demand and return processes. Upon identifying the important relevant modelling parameters that influence the complexity of the underlying models, we provide a comprehensive classification of the current work according to these parameters. In addition, we highlight the solution methodologies used in the literature. Our goals are twofold: to provide a comprehensive exposition of the state-of-art in quantitative models for I&PP for CLSC systems, and to identify new avenues of research in I&PP that will aid in the practical implementation of remanufacturing.

An Integrated Inventory-Routing System for Multi-item Joint Replenishment with Limited Vehicle Capacity

In this paper, we develop a mathematical programming approach for coordinating inventory and transportation decisions in an inbound commodity collection system. In particular, we consider a system that consists of a set of geographically dispersed suppliers that manufacture one or more non-identical items, and a central warehouse that stocks these items. The warehouse faces a constant and deterministic demand for the items from outside retailers. The items are collected by a fleet of vehicles that are dispatched from the central warehouse. The vehicles are capacitated, and must also satisfy a frequency constraint. Adopting a policy in which each vehicle always collects the same set of items, we formulate the inventory-routing problem of minimizing the long-run average inventory and transportation costs as a set partitioning problem. We employ a column generation approach to determine a lower bound on the total costs, and develop a branch-and-price algorithm that finds the optimal assignment of items to vehicles. We also propose greedy constructive heuristics, and develop a very large-scale neighborhood (VLSN) search algorithm to find near-optimal solutions for the problem. Computational tests are performed on a set of randomly generated problem instances.

Production smoothing in just-in-time manufacturing systems: a review of the models and solution approaches

Production smoothing is one of the most important tactical planning activities for the efficient operation of mixed-product just-in-time (JIT) manufacturing systems. As a result, increased research attention has focused on this topic. However, a closer examination of the analytical literature reveals that the majority of the existing work concentrates on synchronized assembly line systems, which is, in part, due to the fact that JIT philosophy originated in an assembly line environment. This limits the applicability of the results of the analytical research in practical settings. Although a relatively recent line of work considers alternative manufacturing environments, an incomplete understanding of the practical and modelling challenges associated with production smoothing hinders the wider adoption of JIT philosophy in a variety of manufacturing environments. Therefore, this paper first discusses the practical and modelling challenges that arise in production smoothing in the context of JIT manufacturing. Then, an extensive review of the existing literature that focuses on analytical models and solution algorithms developed in the field is given. Finally, the gaps in the current body of knowledge are highlighted and several areas where further research is needed in production smoothing are identified.

Alternative loading and dispatching policies for furnace operations in semiconductor manufacturing: a comparison by simulation

In semiconductor manufacturing, furnaces are used for diffusion and deposition operations. A furnace is a batch processing machine, which can simultaneously process a number of lots together as a batch. Whenever a furnace becomes available, scheduling the next batch involves decisions on both which operation to process next (dispatching policy) and how many lots to put into the batch (loading policy). A simulation model of a wafer fabrication facility is used to examine the effects of different loading and dispatching policies for diffusion operations. Results indicate that the loading policy has a significant effect on the average diffusion flow time as well as the overall cycle time of the products, whereas dispatching policy has a less significant effect. We show that the production volume of a product should be considered in setting the minimum number of lots needed to start a batch. We suggest that the diffusion flow time for a low volume product can be reduced by releasing the product in batches or by setting the minimum batch size such that the work-in-process of the product can be moved faster.

A sequential solution methodology for capacity allocation and lot scheduling problems for photolithography

In this paper, we study the shift scheduling problem for photolithography workcenters, which involves specifying the start and finish times for individual lots over the duration of the shift. For the schedule to be accurate, the operational policies (such as set-up control policies) and auxiliary resources of the workcenter (such as reticles) must be considered, which further complicates the problem. We present a sequential procedure that separates the problem into capacity allocation and lot sequencing sub-problems. Although solving these problems sequentially is not a new idea, the lack of empirical evidence of the benefits and insight into implementation issues motivate this research. We use a simulation model of a wafer fabrication facility to examine the effects of using this procedure for scheduling of the photolithography operations. We show that the procedure is a viable method to schedule photolithography workcenter. The choice of the time horizon over which the capacity allocation problem is solved affects cycle time metrics and the number of set-ups significantly. However, varying reticle and set-up constraints has little impact. Finally, we show that the procedure is sensitive to the structure of the operation-stepper matrix and suggest a remedy to overcome the problem.

Applications of supply chain management and e-commerce research

Supply Chain Operations.- Coordination of Inventory and Shipment Consolidation Decisions: A Review of Premises, Models, and Justification.- A Near-Optimal Order-Based Inventory Allocation Rule in an Assemble-To-Order System and its Applications to Resource Allocation Problems.- Improving Supply Chain Performance through Buyer Collaboration.- The Impact of New Supply Chain Management Practices on the Decision Tools Required by the Trucking Industry.- Managing the Supply-Side Risks in Supply Chains: Taxonomies, Processes, and Examples of Decision-Making Modeling.- Demand Propagation in ERP Integrated Assembly Supply Chains: Theoretical Models and Empirical Results.- Electronic Commerce and Markets.- Bridging the Trust Gap in Electronic Markets: A Strategic Framework for Empirical Study.- Strategies and Challenges of Internet Grocery Retailing Logistics.- Enabling Supply-Chain Coordination: Leveraging Legacy Sources for Rich Decision Support.- Collaboration Technologies for Supporting E-Supply Chain Management.- From Research to Practice.- The State of Practice in Supply-Chain Management: A Research Perspective.- Myths and Reality of Supply Chain Management: Implications for Industry-University Relationships.- Supply Chain Management: Interlinking Multiple Research Streams.- PROFIT: Decision Technology for Supply Chain Management at IBM Microelectronics Division.- Case Studies: Supply Chain Optimization Models in a Chemical Company.

Optimizing production smoothing decisions via batch selection for mixed-model just-in-time manufacturing systems with arbitrary setup and processing times

This paper is concerned with the production smoothing problem that arises in the context of just-in-time manufacturing systems. The production smoothing problem can be solved by employing a two-phase solution methodology, where optimal batch sizes for the products and a sequence for these batches are specified in the first and second phases, respectively. In this paper, we focus on the problem of selecting optimal batch sizes for the products. We propose a dynamic programming (DP) algorithm for the exact solution of the problem. Our computational experiments demonstrate that the DP approach requires significant computational effort, rendering its use in a real environment impractical. We develop three meta-heuristics for the near-optimal solution of the problem, namely strategic oscillation, scatter search and path relinking. The efficiency and efficacy of the methods are tested via a computational study. The computational results show that the meta-heuristic methods considered in this paper provide near-optimal solutions for the problem within several minutes. In particular, the path relinking method can be used for the planning of mixed-model manufacturing systems in real time with its negligible computational requirement and high solution quality.

Pricing and recovery planning for demanufacturing operations with multiple used products and multiple reusable components

In this study, we consider a pricing and recovery planning problem encountered in demanufacturing where multiple types of reusable components can be recovered from multiple used product quality groups. The problem is to maximize the net profit of the demanufacturer by characterizing the optimal acquisition prices of the used products and selling prices along with recovery quantities of the reusable components. To address this problem, we develop a mathematical model and analyze the structural properties of an optimal solution to this model. We propose a solution algorithm that exploits these structural properties. Results from a comprehensive computational study shows that the proposed algorithm is very efficient and can be used in practice to address the large-sized instances of the problem. We also present a polynomial time algorithm for a special case of the problem of interest where there is a single used product quality group.

Analyzing the effects of inventory cost setting rules in a disassembly and recovery environment

In this study we consider a disassembly and recovery facility receiving end-of-life products and facing demand for a specific part that is disassembled from the product and then recovered. The disassembly and recovery operations can be either performed before hand, or upon customer arrival. In the latter case, a discount on the selling price is applied to compensate the customer for waiting for the completion of the disassembly and recovery operations. One of the difficulties faced in planning for such a system is the determination of the opportunity cost associated with carrying recovered parts inventory. The difficulty arises in seeking the value added to the part given the costs incurred for maintaining the product return, disassembly and recovery costs and revenue earned from the hulk, that is the remaining product after the disassembly of the part. The main objective of the study is to investigate the effect of different rules to determine this opportunity cost on the performance of the system. Six rules are considered in the study. The performance of the rules is assessed by a computational study under an approximate inventory control policy.

A Hybrid Meta-Heuristic for the Batching Problem in Just-In-Time Flow Shops

This paper is concerned with a batching problem encountered in the context of production smoothing in just-in-time manufacturing systems. The manufacturing system of interest is a multi-level system with a flow-shop at the final level. We develop a hybrid meta-heuristic method to solve the batching problem, which is known to be NP-hard. We hybridize strategic oscillation (SO) and path re-linking (PR) methods and compare the hybrid methods performance to two benchmark methods: a bounded dynamic programming method developed for the problem earlier and an implementation of robust tabu search (RTS) meta-heuristic. Through a computational study, we show that the proposed hybrid method is effective in solving the problem within several minutes of computer time and yielding near-optimal results.