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The ability to detect disturbances quickly as they arise in a supply chain helps to manage them efficiently and effectively. The purpose of this paper is to demonstrate the feasibility of automatically and therefore quickly detecting a specific disturbance, which is constrained capacity at a supply chain echelon.

Different supply chain echelons of a simulated four echelon supply chain were individually capacity constrained to assess their impacts on the profiles of system variables, and to develop a signature that related the profiles to the echelon location of the capacity constraint. A review of disturbance detection techniques across various domains formed the basis for considering the signature-based technique.

The signature for detecting a capacity constrained echelon was found to be based on cluster profiles of shipping and net inventory variables for that echelon as well as other echelons in a supply chain, where the variables are represented as spectra.

Detection of disturbances in a supply chain including that of constrained capacity at an echelon has seen limited research where this study makes a contribution.

The purpose of this paper is to develop efficient heuristics for determining the route design and inventory management of inbound parts which are delivered for manufacturing, assembly, or distribution operations and for which there is limited storage space. The shipment frequencies and quantities are coordinated with the available storage space and the vehicle capacities.

Two heuristics that generate near optimal solutions are proposed. The first heuristic has an iterative routing phase that maximizes the savings realized by grouping suppliers together into routes without considering the storage constraint and then calculates the pickup frequencies in the second phase to accommodate the storage constraint. The second heuristic iteratively executes a routing and a pickup frequency phase that both account for the storage constraint. A lower bound is also developed as a benchmark for the heuristic solutions.

Near optimal solutions can be obtained in a reasonable amount of time by utilizing information about the amount of storage space in the route design process.

The traditional emphasis on high vehicle utilization in transportation management can lead to inefficient logistics operations by carrying excess inventory or by using longer, less efficient routes. Route formation and pickup quantities at the suppliers are simultaneously considered, as both are important from a logistics standpoint and are interrelated decisions.

The two proposed heuristics dynamically define seed sets such that the solutions to the capacitated concentrator location problem (CCLP) are accurately estimated. This increased accuracy helps in generating near‐optimal solutions in a practical amount of computing time.

A comprehensive review of the literature for the problem of lot‐size scheduling (serial and assembly) considering the uncapacitated problem and complicated capacitated assembly manufacturing structure. Analyses the different solution techniques and findings for each product set.

The purpose of this paper is to develop a new approach called advanced overlapping production planning (AOPP) model which considers multi‐site process selection, sequential constraints, and capacity constraints in a manufacturing supply chain environment (MSCE). AOPP model may determine the capacity plan and order margin allocation for each site and machines in an MSCE and provide the capacity information for a production planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources.

First, an AOPP model is presented to model the production scheduling problem in a supply chain with the objective of minimizing the fulfilling cycle time of each order and the overloads of each machine group. Second, a genetic algorithm (GA)‐based approach for solving the AOPP model is developed. Finally, a heuristic adjustment approach is proposed for planners to adjust the production plan whenever there is an exception of production occurring.

The production schedule obtained from the GA‐based AOPP approach retains order margins in each operation against other overlapping operations, and it satisfies the capacity constraints of each machine group in an MSCE and results in a better performance in process planning and production planning with finite capacity. In practice, the overloading problem can be solved by adding a work shift or working overtime. The GA‐based AOPP model provides useful information for production planners to make such decisions.

Production planners need a more flexible production plan with order margins to compensate for the uncertainties which frequently occur in the supply and demand sides. This research develops a model to help planners manage the order margin of production planning in an MSCE and showing that order margins become a crucial factor for achieving effective production objective in terms of short OTD (or order cycle) time.

The overlapping production planning approach is a useful finite capacity planning approach for handling the capacity and order margin management in certain manufacturing environment (e.g. flow shop), but less on overcoming multi‐site process selection, sequential constraints, and capacity constraints in an MSCE. In addition, the capacity plan and order margin allocation information for each site and facilities are very important for a planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources. This research addresses both issues.

The use of goal programming in minimising capacity waste in the Shock Absorber Department of a batch manufacturing automobile plant is illustrated. The concept of capacity waste is outlined and the objectives and constraints of capacity waste minimisation identified. Only linear objectives of the minimisation of idle time, set‐up time, work‐in‐process inventory and maximisation of profit are considered for the development of the goal programming model. The manufacture of different kinds of piston rods for various makes of shock absorbers are described and relevant data pertaining to processing time, setup time, work‐in‐process inventory, etc, are presented. A goal programming model, formulated for the problem, is analysed for different priority structures.

A critical issue in the design of routing protocols for wireless sensor networks is the efficient utilization of resources such as scarce bandwidth and limited energy supply. Many routing schemes proposed in the literature try to minimize the energy consumed in routing or maximize the lifetime of the sensor network without taking into consideration limited capacities of nodes and wireless links. This can lead to congestion, increased delay, packet losses and ultimately to retransmission of packets, which will waste considerable amount of energy. This paper presents a Minimum‐cost Capacity‐constrained Routing (MCCR) protocol which minimize the total energy consumed in routing while guaranteeing that the total load on each sensor node and on each wireless link does not exceed its capacity. The protocol is derived from polynomial‐time minimum‐cost flow algorithms. Therefore protocol is simple and scalable. The paper improves the routing protocol in (1) to incorporate integrality, node capacity and link capacity constraints. This improved protocol is called Maximum Lifetime Capacity‐constrained Routing (MLCR). The objective of MLCR protocol is to maximize the time until the first battery drains its energy subject to the node capacity and link capacity constraints. A strongly polynomial time algorithm is proposed for a special case of MLCR problem when the energy consumed in transmission by a sensor node is constant. Simulations are performed to analyzed the performance of the proposed protocols.

The learning outcomes of this study are to understand the theory of constraints and related concepts, to evaluate constraints impeding organizational growth and to develop a solution addressing the constraints in the best possible way.

Bottlenecks or constraints impede an organization from reaching its full potential, thus having implications for the internal and external functionalities of the organization. Internally, many resources remain idle or deteriorate, as constraints always hamper the overall capacity. Externally, the organization might lose the customer for not fulfilling their demands. The organization may be unable to procure the raw material at economical prices from the suppliers, as large-quantity orders may not be placed. This case was designed to make students understand the theory of constraints (TOC) and related concepts. The TOC framework was a management philosophy developed by Dr Eliyahu Goldratt and popularly mentioned in his book The Goal. This case study considered a dairy plant as its central focus. The protagonist was challenged by the existing constraints in his dairy plant, and his dilemmas were introduced. The dairy processes were discussed, followed by details of supplies made to the dairy plant regularly. The capacity constraints at different levels were mentioned. The case also discussed the quick fixes adopted by the dairy to overcome the constraints. Finally, this case ended with a dilemma presented before the protagonist and a dire need for a solution thereafter.

This case was appropriate for introducing TOC to undergraduate and postgraduate courses in operations management, logistics and supply chain management and general management.

Teaching notes are available for educators only.

CSS 9: Operations and Logistics.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The purpose of this paper is to suggest that the theory of constraints (TOC) can serve as a general theory in operations management. The paper first investigate linkages between TOC and the core concepts/components of operations management (OM) and show how OM concepts can be integrated with TOC using examples from the published TOC literature. A second important purpose is to show that TOC, as a theory, has properties essential for a good theory.

Using a commonly accepted categorization of operations decisions (process, quality, inventory and capacity), traditional views, and approaches to operations decisions to those inherent in the TOC are compared.

The paper concludes that the TOC provides approaches to operations decisions that avoid pitfalls of local optimization by reaching across functional boundaries in organizations. In addition, while the TOC appears to meet the criteria of a good theory, it has not been empirically tested for the most part.

The TOC can serve as a unifying theory or theme for operations management, providing new insights for researchers and an organizing principle for teachers.

The purpose of this paper is to present a comprehensive descriptive analysis of the sequential application of the Theory of Constraints (TOC) five‐step focusing process in improving the effectiveness of a service process that was limiting the performance of the entire service system.

The five‐step approach is schematically presented with each step being described and evaluated relative to its role in effective constraint management. A detailed example provides additional insights and nuances into its use in managing the constraint within a banking organization's subsystem, namely, the loan application and approval process. This hypothetical, realistic, and comprehensive illustration iterates through several cycles of the five‐step focusing process to demonstrate how managers are able to address different types of constraints.

The paper provides a detailed description on how each of the five‐steps can be used to improve the organization's performance relative to its stated goal by focusing management's attention on the system's (or an aligned subsystem's) leverage or control point. Major types of constraints include physical or capacity limitations and restrictive policies. Constraints may be located either internal or external to the process or system being managed.

Various managerial implications are discussed including: the relevance and utility of applying the TOC five‐step focusing process in services; advantages associated with utilizing this structured approach for continuous improvement in services; and some strategic issues associated with the placement of the ubiquitous system constraint.

Although the successful application of the five‐step focusing process has been well documented in improving manufacturing processes, this paper provides an illustrative tutorial which details its application in effectively managing a service process.