Decentralised decision-making in a multi-party supply chain

In this paper, we consider multi-party coordination in a supply chain (SC) that consists of a set of independent producers and a set of resource managers. A decentralised decision-making approach is proposed for a coal SC, with three independent parties – multiple mines, a rail operator and a terminal. The rail operator and the terminal act as common resource managers and connects the independent mines via a rail network. The objective of this SC is to efficiently use an independent rail operator to transport coal from different mines to meet the shipping demand at the terminal. The underlying coordination problem can be seen as a multi-resource constrained scheduling problem. A major part of this paper addresses the key challenges in a decentralised approach based on column generation (CG), which are to compute the value of a column, better upper bounds and to update the multipliers using decentralised methods. We have also discussed the mathematical models for different decision units, the CG algorithm and different strengthening methods. A comprehensive computational experiment based on randomly generated instances highlights the effect of decentralisation and the value of information-sharing. The proposed solution approaches can be extended to a multi-party case with any number of common resources.     

A CBFSA approach to resolve the distributed manufacturing process planning problem in a supply chain environment

In this competitive world cost and lead time reduction are of prime concern for manufacturing firms. To achieve this objective manufacturing entities are adopting several management philosophies such as Total Quality Management (TQM), just-in-time (JIT), and theory of constraints (TOC). The present paper addresses the advanced computer-aided process planning (ACAPP) problem in a distributed manufacturing supply chain environment and aims at cost and lead times reduction under several technological constraints. To deal with the complexity of the problem the constraint based fast simulated annealing (CBFSA) algorithm has been explored in this article. Extensive computations have been performed over the well-known benchmarks of advanced planning problems and the results obtained prove the superiority of the proposed algorithm over the prior approaches.     

Distributed project scheduling with information sharing in supply chains: part I—an agent-based negotiation model

Distributed project scheduling problem (DPSP) in supply chains is concerned with configuration and scheduling of multiple projects in a network of independent and autonomous enterprises. Individual enterprises must collaborate with each other during two main stages: the configuration of a project—selection of contractors for performing project operations and the project scheduling—determining when the operations start. However, the collaboration is especially difficult because none of these enterprises holds the global information about the entire supply chain and all constituent enterprises. Instead, they have to capitalize whatever information is shared between them in order to solve their own local problems in a distributed and autonomous fashion. It is essential for the solution process to strike an overall balance between effectiveness and efficiency. The research, reported in this two-part paper, is aimed at proposing a negotiation-based algorithm for solving DPSP. Its emphasis is how to improve the convergence and quality of the solution by taking advantage of inter-enterprise information sharing especially the sharing of schedule flexibility information (SFI). The first part of this paper describes a new agent-based approach to DPSPs in supply chains while the second part will present detailed discussion on the theoretical and experimental analysis.     

Open-pit coal mine production sequencing incorporating grade blending and stockpiling options: An application from an Indian mine

Production scheduling is a crucial aspect of the mining industry. An optimal and efficient production schedule can increase the profits manifold and reduce the amount of waste to be handled. Production scheduling for coal mines is necessary to maintain consistency in the quality and quantity parameters of coal supplied to power plants. Irregularity in the quality parameters of the coal can lead to heavy losses in coal-fired power plants. Moreover, the stockpiling of coal poses environmental and fire problems owing to low incubation periods. This article proposes a production scheduling formulation for open-pit coal mines including stockpiling and blending opportunities, which play a major role in maintaining the quality and quantity of supplied coal. The proposed formulation was applied to a large open-pit coal mine in India. This contribution provides an efficient production scheduling formulation for coal mines after utilizing the stockpile coal within the incubation periods with the maximization of discounted cash flows. At the same time, consistency is maintained in the quality and quantity of coal to power plants through blending and stockpiling options to ensure smooth functioning.     

The equipment maintenance scheduling problem in a coal production system

This paper addresses an equipment maintenance scheduling problem in a coal production system which includes three consecutive stages: the coal mining stage, the coal washing stage and the coal loading stage. Each stage is composed of different equipment that needs maintenance each day. There exists intermediate storage with finite capacities and the finished products are transported by train. Moreover, some equipment has a different preference for (aversion to) the start time of maintenance (STOM). The objective is to minimise the weighted sum of aversion about STOM, changeover times and train waiting time. We first formulate this problem into a mixed integer linear programming (MILP) model, then a hybrid genetic algorithm (HGA) is proposed to solve it. The proposed method has been tested on a practical coal enterprise in China and some randomly generated instances. Computational results indicate that our algorithm can produce near-optimal solutions efficiently.     

Optimal scheduling of logistical support for an emergency roadway repair work schedule

The completion of every disaster rescue task performed by repair work teams relies on the in-time supply of materials to the rescue workers. Up to now, logistical support planning for emergency repair work in Taiwan has been done manually, which is neither effective nor efficient. To remedy the problem, this study presents a logistical support scheduling model for the given emergency repair work schedule. The objective is to minimize the short-term operating cost subject to time constraints and other related operating constraints. This model is formulated as an integer multiple-commodity network flow problem which is characterized as NP-hard. A heuristic algorithm, based on the problem decomposition and variable fixing techniques, is also proposed to efficiently solve this problem. Computational tests are performed using data from Taiwan's 1999 Chi-Chi earthquake. The results show that the model and the solution algorithm would be useful for the logistical support scheduling.     

R&D project scheduling when activities may fail

An R&D project typically consists of several stages. Due to technological risks, the project may have to be terminated before completion, each stage having a specific likelihood of success. In the project planning and scheduling literature, this technological uncertainty has typically been ignored and project plans are developed only for scenarios in which the project succeeds. In this paper we examine how to schedule projects in order to maximize their expected net present value when the project activities have a probability of failure and when an activity's failure leads to overall project termination. We formulate the problem, show that it is NP-hard, develop a branch-and-bound algorithm that allows us to obtain optimal solutions and provide extensive computational results. In the process, we establish a complexity result for an open problem in single-machine scheduling, namely for the discounted weighted-completion-time objective with general precedence constraints.     

The coordinated production and transportation scheduling problem with a time-sensitive product: a branch-and-cut algorithm

In many supply chain scenarios in which short lifespan products are considered, production and transportation decisions must be made in a coordinated manner with no inventory stage. Hence, a solution to this problem conveys information about production starting times of each product lot at facility and delivery times of the lots to various customer-sites located in different geographic regions. In this paper, we study a variant of the problem that single product with limited shelf life is produced at single facility. Once produced, production lot is directly distributed to the customers with non-ignorable transportation time by single vehicle having limited capacity before the lifespan. Objective is to determine the minimum time required to produce and deliver all customer demands. To this end, we develop a branch-and-cut (B&C) algorithm using several valid inequalities adopted from the existing literature to improve lower bounds and applying a local search based on simulated annealing approach to improve upper bounds. On test problems available in the literature, we evaluate the performance of the B&C algorithm. Results show the promising performance of the B&C algorithm.     

Advanced production scheduling for batch plants in process industries

An Advanced Planning System (APS) offers support at all planning levels along the supply chain while observing limited resources. We consider an APS for process industries (e.g. chemical and pharmaceutical industries) consisting of the modules network design (for long–term decisions), supply network planning (for medium–term decisions), and detailed production scheduling (for short–term decisions). For each module, we outline the decision problem, discuss the specifi cs of process industries, and review state–of–the–art solution approaches. For the module detailed production scheduling, a new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far. The new approach decomposes detailed production scheduling for batch production into batching and batch scheduling. The batching problem converts the primary requirements for products into individual batches, where the work load is to be minimized. We formulate the batching problem as a nonlinear mixed–integer program and transform it into a linear mixed–binary program of moderate size, which can be solved by standard software. The batch scheduling problem allocates the batches to scarce resources such as processing units, workers, and intermediate storage facilities, where some regular objective function like the makespan is to be minimized. The batch scheduling problem is modelled as a resource–constrained project scheduling problem, which can be solved by an efficient truncated branch–and–bound algorithm developed recently. The performance of the new solution procedures for batching and batch scheduling is demonstrated by solving several instances of a case study from process industries.     

An approach to integrate production-transportation planning and scheduling in an aluminium supply chain network

This article is based on a real-life problem of a global aluminium supply chain network driven by an aluminium smelter. At each echelon of the aluminium supply chain network, several members are involved which are scattered around the world. Producing aluminium begins with bauxite mining. Next, aluminium oxide is made from bauxite and finally aluminium is produced from aluminium oxide. A novel type of mixed-integer decision-making model, including a time-continuous representation of the planning period, is presented. The model enables coordination of production quantities and times of all supply chain members in order to minimise production and transportation costs of the whole supply chain minus bonus payments for early deliveries which are stipulated between the supply chain network and its customers. Material flows can take place with or without temporary storage of intermediate products at supplying and/or receiving sites. Furthermore, relax-and-fix heuristics are presented. A number of randomly generated scenarios are presented to demonstrate that the heuristics can find nearly optimal solutions along with drastically reduced computation times. The relax-and-fix heuristic enables iterative planning between centralised and decentralised decision makers.     

Modeling the planning and scheduling across the outsourcing supply chain: a Chaos-based fast Tabu–SA approach

Planning and Scheduling are the interrelated manufacturing functions and should be solved simultaneously to achieve the real motives of integration in manufacturing. In this paper, we have addressed the advanced integrated planning and scheduling problem in a rapidly changing environment, where the selection of outsourcing machine/operation, meeting the customers (single or multiple) due date, minimizing the makespan are the main objectives while satisfying several technological constraints. We developed a mixed integer programming model for integrated planning and scheduling across the outsourcing supply chain and showed how such models can be used to make strategic decisions. It is a computationally complex and mathematically intractable problem to solve. In this paper, a Chaos-based fast Tabu-simulated annealing (CFTSA) incorporating the features of SA, Tabu and Chaos theory is proposed and applied to solve a large number of problems with increased complexity. In CFTSA algorithm, five types of perturbation schemes are developed and Cauchy probability function is used to escape from local minima and achieve the optimal/near optimal solution in a lesser number of iterations. An intensive comparative study shows the robustness of proposed algorithm. Percentage Heuristic gap is used to show the effectiveness and two ANOVA analyses are carried out to show the consistency and accuracy of the proposed approach.     

A dynamic programming approach to integrated assembly planning and supplier assignment with lead time constraints

As manufacturers face fierce competition in the global market, responsiveness has become an important competitiveness factor in addition to quality and cost. One essential responsiveness strategy is to reduce product development and lead times by integrating assembly planning with supplier assignment. This paper addresses the problem of integrated assembly and supply chain design under lead-time constraints by formulating and solving an optimisation problem with minimal total supply chain costs. This new time-constrained joint optimisation problem belongs to an NP-hard resource-constrained scheduling problem. To model this problem effectively, we develop a novel Hyper AND/OR graph and apply it for integrating assembly and supply chain decisions. We also develop a dynamic programming model and associated algorithm in order to solve the integrated optimisation problem with pseudo-polynomial time complexity in practice. Numerical case studies validate that the methods developed can solve the integrated decision-making problem optimally and efficiently. This paper overcomes the limitations of previous studies on concurrent assembly decomposition and supplier selection, which optimises cost without time constraints. The models and results of this research can be applied to a variety of areas including assembly design, maintenance module planning and supply chain restructuring.     

A stochastic programming approach to determine robust delivery profiles in area forwarding inbound logistics networks

One technique to coordinate the suppliers’ and the producers’ production plans in a supply chain is the use of delivery profiles, which provide fixed delivery frequencies for all suppliers. The selection of a delivery profile assignment has major effects on the cost efficiency and the robustness of a supply chain and thus should be performed carefully. In this work, we consider planning approaches to select delivery profiles for the case of area forwarding-based inbound logistics networks, which are commonly used in several industries to consolidate supplies in an early stage of transport. We present a two-stage stochastic mixed integer linear programming model to determine robust delivery profile assignments under uncertain and infrequent demands and complex tariff systems. The model is embedded into a solution framework consisting of scenario generation and reduction techniques, a decomposition approach, a genetic algorithm, and a standard MILP solver. On the basis of an industrial case study, we show that our approach is computationally feasible and that the planning solutions obtained by our model outperform both a deterministic approach and the planning methodology prevailing in industrial practice.     

Single machine scheduling with preventive maintenances

We consider the single machine total flow time problem in which the jobs are non-resumable and the machine is subject to preventive maintenance activities of known starting times and durations. We propose a branch-and-bound algorithm that employs powerful optimality properties and bounding procedures. Our extensive computational studies show that our algorithm can solve large-sized problem instances with up to 80 jobs in reasonable times. We also study a two-alternative maintenance planning problem with minor and major maintenances. We give a polynomial-time algorithm to find the optimal maintenance times when the job sequence is fixed.     

Managing warehousing in an agile supply chain environment: an F-AIS algorithm based approach

In recent years, application of the agile concept in the manufacturing sector has been researched extensively to reduce the varying effect of customer demands. However, most of the research work is focused on the shop floor of different manufacturing processes, while issues concerning the control of warehouse scheduling in a supply chain have been neglected so far. Realising this in the present research an attempt has been made to address the scheduling aspect of a warehouse in an agile supply chain environment. To resolve the warehouse problem in this paper, the authors have proposed a new Fuzzy incorporated Artificial Immune System Algorithm (F-AIS). This algorithm encapsulates the salient features of a fuzzy logic controller and immune system. The proposed algorithm has been compared with genetic algorithm (GA), simulated annealing (SA) and artificial immune system (AIS) algorithm to reveal the efficacy of the proposed F-AIS algorithm.     

Setting safety stocks in multi-stage inventory systems under rolling horizon mathematical programming models

This paper considers the problem of determining safety stocks in multi-item multi-stage inventory systems that face demand uncertainties. Safety stocks are necessary to make the supply chain, which is driven by forecasts of customer orders, responsive to (demand) uncertainties and to achieve predefined target service levels. Although there exists a large body of literature on determining safety stock levels, this literature does not provide an effective methodology that can address complex multi-constrained supply chains. In this paper, the problem of determining safety stocks is addressed by a simulation based approach, where the simulation studies are based on solving the supply chain planning problem (formulated as a mathematical programming model) in a rolling horizon setting. To demonstrate the utility of the proposed approach, an application of the approach at Organon, a worldwide operating biopharmaceutical company, will be discussed.     

Integrated process planning and scheduling using an imperialist competitive algorithm

Effective performance of modern manufacturing systems requires integrating process planning and scheduling more tightly, which is consistently challenged by the intrinsic interrelation and intractability of these two problems. Traditionally, these two problems are treated sequentially or separately. Integration of process planning and scheduling (IPPS) provides a valuable approach to improve system performance. However, IPPS is more complex than job shop scheduling or process planning. IPPS is strongly NP-hard in that, compared to an NP-hard job shop scheduling problem with a determined process plan, the process plan for each job in IPPS is also to be optimised. So, an imperialist competitive algorithm (ICA) is proposed to address the IPPS problem with an objective of makespan minimisation. An extended operation-based representation scheme is presented to include information on various flexibilities of process planning with respect to determined job shop scheduling. The main steps of the proposed ICA, including empires construction, assimilation, imperialistic competition, revolution and elimination, are elaborated using an illustrative example. Performance of the proposed ICA was evaluated on four sets of experiments taken from the literature. Computational results of the ICA were compared with that of some existing algorithms developed for IPPS, which validates the efficiency and effectiveness of the ICA in solving the IPPS problem.     

A hybrid VNS-HS algorithm for a supply chain scheduling problem with deteriorating jobs

This paper investigates a coordinated scheduling problem in a two stage supply chain where parallel-batching machine, deteriorating jobs and transportation coordination are considered simultaneously. During the production stage, jobs are processed by suppliers and there exists one parallel-batching machine in each supplier. The actual processing time of a job depends on its starting time and normal processing time. The normal processing time of a batch is equal to the largest normal processing time among all jobs in its batch. During the transportation stage, the jobs are then delivered to the manufacturer. Since suppliers are distributed in different locations, the transportation time between each supplier and the manufacturer is different. Based on some structural properties of the studied problem, an optimal algorithm for minimising makespan on a single supplier is presented. This supply chain scheduling problem is proved to be NP-hard, and a hybrid VNS-HS algorithm combining variable neighbourhood search (VNS) with harmony search (HS) is proposed to find a good solution in reasonable time. Finally, some computational experiments are conducted and the results demonstrate the effectiveness and efficiency of the proposed VNS-HS.     

MILP-based campaign scheduling in a specialty chemicals plant: a case study

Supply chain management in chemical process industry focuses on production planning and scheduling to reduce production cost and inventories and simultaneously increase the utilization of production capacities and the service level. These objectives and the specific characteristics of chemical production processes result in complex planning problems. To handle this complexity, advanced planning systems (APS) are implemented and often enhanced by tailor-made optimization algorithms. In this article, we focus on a real-world problem of production planning arising from a specialty chemicals plant. Formulations for finished products comprise several production and refinement processes which result in all types of material flows. Most processes cannot be operated on only one multi-purpose facility, but on a choice of different facilities. Due to sequence dependencies, several batches of identical processes are grouped together to form production campaigns. We describe a method for multicriteria optimization of short- and mid-term production campaign scheduling which is based on a time-continuous MILP formulation. In a preparatory step, deterministic algorithms calculate the structures of the formulations and solve the bills of material for each primary demand. The facility selection for each production campaign is done in a first MILP step. Optimized campaign scheduling is performed in a second step, which again is based on MILP. We show how this method can be successfully adapted to compute optimized schedules even for problem examples of real-world size, and we furthermore outline implementation issues including integration with an APS.     

A multi-commodity supply chain design problem

We consider a multi-commodity supply chain design problem in which we need to determine where to locate facilities and how to allocate customers to facilities so as to minimize total costs. The cost associated with each facility exhibits economies of scale. We show that this problem can be formulated as a nonlinear integer program and propose a Lagrangian-relaxation solution algorithm. By exploiting the structure of the problem, we find a low-order polynomial algorithm for the nonlinear integer program that must be solved in solving the Lagrangian relaxation subproblems. We also compare our approach with an existing algorithm.